Unit-1
1.
Introduction to Management Support Systems
1.1 Managers and decision making process
· Decision: A decision is a choice between two or
more alternatives. If you only have one alternative, you do not have a
decision. A typical thesaurus might use words like accommodation, agreement,
arrangement, choice, compromise, declaration, determination, outcome,
preference, resolution, result, and verdict to try and give the concept of
“decision” some dimension.
· Decision-making is one of the defining
characteristics of leadership. It’s core to the job description. Making
decisions is what managers and leaders are paid to do. Yet, there isn’t a day
that goes by that you don’t read something in the news or the business press
that makes you wonder, “What were they thinking?” or “Who actually made that
decision?”
· Manager and Decision: Being in a managerial role
usually requires prompt decision making. Decision making process is the process
by which managers respond to opportunities, threats, analyze all the available options
and make a sound decision which is commensurate with the goals of the
organization. The decision taken helps decide the further course of action.
Decision making is one of the core responsibilities of a manager’s job. There
are other responsibilities as well which usually involve problem solving and
work distribution. A manager must make informed decisions based on his
expertise, technical knowledge and experience. Managers use a wide range of
decision strategies, often changing these strategies from one situation to the
next. The strategies lead to a wide variety of choices of varying quality,
depending on the decision being made. Managers are equipped with a number of
useful techniques for diagnosing problems, clarifying values and goals, structuring
and modeling decisions, and gathering useful information. The three kinds of
managerial roles include:
·
Interpersonal -- which include figure heads and
leaders
·
Informational -- who receive and disseminate
critical information
·
Decisional -- who initiate activities, handle
disturbances, allocate resources and negotiate conflicts.
Decisions made
by top managers commit the total organization towards a particular course of
action. Decisions made by lower level managers implement the strategic
decisions of top managers in the operating areas of the organization. Top
managers make Category II decisions. Operating managers make Category I
decisions, while the middle managers supervises the making of Category I
decisions and support the making of Category II decisions. The success of the
decision taken is a function of the decision quality and decision
implementation.
·
Decision making Process: eight steps that add
structure and simplicity to the Decision making process.
o Recognize
and identify the problem.: Decisions are responses to situations or problems
that need addressing. Therefore is important to have a clear definition of what
needs addressing before attempting to go further in the decision making
process.
o Consider
the nature of the problem that you are trying to resolve: What is the type
issue, problem, or situation you need to address? Is it problematic in terms of
creating an awkward situation between individuals, is it needed to change
direction of a business? Why does the problem need a decision? What are the
results you are hoping to achieve by this decision?
o Analyze
or research the problem: It is important to gather all the information involved
in the problem or question, so that informed choices can be made.
o Develop
a list of possible solutions: List the possible decisions that could be made,
and what their consequences would be.
o Select
the best alternative: Look at the list drawn up in point 4 and choose the best
solution for the situation.
o Execute
the best choice: Sometimes the hardest part of making a decision is taking
action. The best decisions are ones that deliver strong decision action.
o Follow
Up and communication: A good decision needs to be followed throughout its
process and constant communication made with those involved.
o Feedback:
It is extremely important to gather feedback on a decision. This determines the
overall success of and reaction to the decision.
1.2 The nature of manager’s works
The Canadian academic, Henry
Mintzberg(1973), PhD thesis at the MIT Sloan School of Management Analyzing the actual work habits and time
management of chief executive officers (CEOs), research involved observing
and analyzing the activities of the CEOs of five private and semi-public
organizations identified six characteristics of the job:
o
Managers process large, open-ended workloads
under tight time pressure - a manager's job is never done.
o
Managerial activities are relatively short in
duration, varied and fragmented and often self-initiated.
o
CEOs prefer action and action driven activities
and dislike mail and paperwork.
o
They prefer verbal communication through meetings
and phone conversations.
o
They maintain relationships primarily with their
subordinates and external parties and least with their superiors.
o
Their involvement in the execution of the work
is limited although they initiate many of the decisions.
Mintzberg then identified ten separate roles in managerial
work, each role defined as an organized collection of behaviors belonging to an
identifiable function or position. He separated these roles into three
subcategories: interpersonal contact (1, 2, 3), information processing (4, 5,
6) and decision making (7-10).
S.N
|
function or position
|
Role
|
1
|
FIGUREHEAD
|
ceremonial and
symbolic duties as head of the organisation
|
2
|
LEADER
|
adopts a proper work
atmosphere and motivates and develops subordinates;
|
3
|
LIASION
|
develops and
maintains a network of external contacts to gather information
|
4
|
MONITOR
|
gathers internal and
external information relevant to the organisation
|
5
|
DISSEMINATOR
|
transmits factual
and value based information to subordinates
|
6
|
SPOKESPERSON
|
communicates to the
outside world on performance and policies
|
7
|
ENTREPRENEUR
|
designs and
initiates change in the organisation
|
8
|
DISTURBANCE HANDLER
|
deals with
unexpected events and operational breakdowns
|
9
|
RESOURCE ALLOCATOR
|
controls and authorizes
the use of organizational resources
|
10
|
NEGOTIATOR
|
participates in
negotiation activities with other organizations and individuals
|
He identified four clusters of independent variables:
external, function related, individual and situational. He concluded that eight
role combinations were 'natural' configurations of the job:
o
contact manager -- figurehead and liaison
o
political manager -- spokesperson and negotiator
o
entrepreneur -- entrepreneur and negotiator
o
insider -- resource allocator
o
real-time manager -- disturbance handler
o
team manager -- leader
o
expert manager -- monitor and spokesperson
o
new manager -- liaison and monitor
1.3 Need for computerized decision support
A Decision
Support System (DSS) is a computerized system that assists in corporate
decision making, with a decision being a choice between alternatives based on
the estimated values of those alternatives. Generally, decision support systems
are interactive, flexible, and adaptable information systems, developed to
support the solution of non-structured management problems for improved
decision making. These systems are designed to make use of data in order to
help identify certain problems a business might be experiencing, and to help
make the decisions necessary to address and work those problems out.Some of the
benefits that Decision Support Systems bring to the Decision Making process are:
·
Speed up the process of decision making
·
Increases organizational control
·
Encourages exploration and discovery on the part
of the decision maker
·
Speeds up problem solving in an organization
·
Facilitates interpersonal communication
·
Promotes learning or training
·
Generates new evidence in support of a decision
·
Creates a competitive advantage over competition
·
Reveals new approaches to thinking about the
problem space
·
Helps automate managerial processes
·
Decision Support Systems can be used by all
sorts of businesses as assistive tools. They help in a variety of circumstances
including problem management, data analysis and forecasting situations.
1.4 Decision support technologies:
·
Types of
Decision Support Systems:
· Model-driven DSS puts emphasis on
manipulation of a statistical, financial, or simulation model. This type of DSS
uses data and parameters provided by users to assist decision makers in
analyzing a situation; (they are not necessarily data intensive.) Parameters
are provided by users for the analysis of a situation.
· Communication-driven DSS supports more
collaboration on a shared task. Examples include integrated tools like
Microsoft's NetMeeting or Groove or SharePoint.
· Data-driven DSS emphasizes manipulation of a
chronological series of corporate internal data or occasionally, external data.
· Document-driven DSS manages and manipulates
unstructured information in from a variety of electronic formats.
· Knowledge-driven DSS provides specialized
problem solving expertise stored as facts, rules, procedures, or in similar
structures.
· Decision support systems often use Business
Intelligence and Data Mining technology to provide aggregations of timely data
as well as additional valuable insight. Data mining and predictive analytics
often provide helpful information for decision support.
·
Decision Support Technologies
·
Management
Support Systems (MSS): Subset of management information system (MIS), it
extends the information retrieval capabilities of the end-users with 'query and
analysis functions' for searching a database, generating 'what if' scenarios,
and other such purposes.
·
Decision
Support Systems (DSS): Decision Support Systems often include features that
allow the user to project how a business result would be affected if an
underlying assumption were to change. For example, an investment decision might
look less attractive if the company’s cost of capital is driven up by an
increase in the federal funds rate. The Decision Support System could calculate
the cost and the return on investment in several scenarios.
·
Group
Support Systems (GSS), including Group DSS (GDSS): Group Decision Support
Systems (GDSS) are a class of electronic meeting systems, a collaboration
technology designed to support meetings and group work .GDSS are distinct from
computer supported cooperative work (CSCW) technologies as GDSS are more
focused on task support, whereas CSCW tools provide general communication
support .
·
Executive
Information Systems (EIS):Executive Information Systems (sometimes known as
Scorecards or Dashboards, depending on their graphical presentation) are
designed to deliver specific key information to top managers at a glance, with
little or no interaction with the system. A decision support system is an
analytical application that permits the user to call up information from the
data warehouse and manipulate it to derive actionable information.
·
Expert
Systems (ES): An expert system is a computer system that emulates the decision-making
ability of a human expert. Expert systems are designed to solve complex
problems by reasoning about knowledge, like an expert, and not by following the
procedure of a developer as is the case in conventional programming.
·
Artificial
Neural Networks (ANN): An artificial neural network (ANN), usually called
neural network (NN), is a mathematical model or computational model that is
inspired by the structure and/or functional aspects of biological neural
networks. A neural network consists of an interconnected group of artificial
neurons, and it processes information using a connectionist approach to
computation.
·
Hybrid
Support Systems
·
Cutting
Edge Intelligent Systems
(Genetic Algorithms, Fuzzy Logic, Intelligent Agents, ...)
(Genetic Algorithms, Fuzzy Logic, Intelligent Agents, ...)
·
1.1 Concept of MIS
MIS refers broadly to a computer-based system that provides managers with
the tools for organizing, evaluating and efficiently running their departments
with right Information to the right person at the right place at the right time
in the right form at the right cost.
The three sub-componentsManagement, Information and System together bring
out the focus clearly & effectively.
A management information system (MIS) provides information
that is needed to manage organizations efficiently and effectively. Management
information systems involve three primary resources: people, technology, and
information or decision making. Management information systems are distinct
from other information systems in that they are used to analyze operational activities
in the organization. Academically, the term is commonly used to refer to the
group of information management methods tied to the automation or support of
human decision making, e.g. decision support systems, expert systems, and
executive information systems.
Unit 2
2.1
System Concept:
2.1.1
Definition:
The word system means plan, method, order, and arrangement.
A “ system”, says the dictionary, is a regularly interacting or inter –
dependent group of items forming a united whole. A system is thus a set of
interacting elements, interacting with each other to achieve a predetermined
objective or goal. For example, in a computer system, the computer receives
inputs and processes than produces the output.
A system is a group of interrelated components working
together towards a common goal by accepting inputs and producing outputs in an
organized transformation process.
IEEE defines
software as, “the collection of computer programs, procedures, rules and
associated documentation and data”. This definition clearly states that, the
software is not just a collection of programs, but includes all associated
documentation and data. This implies that, software development process should
focus on all the things constitute the software.
2.1.2
Characteristics:
There
are some common characteristics of any system.
·
Every
system has a certain objectives and goals.
·
Main
system has a several subsystems or models.
·
The
structure of the system is representation of the interaction and
interrelationships between different components or subsystems that from a
system.
·
The
lifecycle of the system is expression of the phases in the alive usage life of
the system.
·
System
operates in the terms of goals and predetermined scope.
·
Systems in real life do not operate in
isolation.
2.1.3
Elements of System:
It has five basic interacting
components.
·
Input:
o
Capturing
/ accepting and assembling components that enter the system to be processed Example: raw data, raw material
etc.
·
Processing:
o
Process
that series of changes to be done on information, to convert input into output.
Example: data processing,
manufacturing process etc.
·
Output:
o
Which produced by the transformation process to their ultimate
destination. Example: reports,
finished products etc.
·
Control :
o
The
control elements guide the system. It is the decision making sub system that
controls the pattern of activities governing input , processing and output.
·
Feedback: Control in a dynamic system is achieved by
feedback. Feedback measures output against a standard in some form of
cybernetic procedure that includes communication and control.
2.1.1
Types of System:
·
PHYSICAL: Systems are tangible entities that may be
static or dynamic Example of static - Office desk, Chair Example of Dynamic -
Programmed Computer.
·
ABSTRACT: Systems are conceptual or non-physical
entities. They may be as straight forward as formulas of relationships among
set of variables or models is the abstract conceptualization of physical
system.
·
OPEN &
CLOSED SYSTEM:
A closed system is one which is self-contained. It has no interaction with its
environment. No known system can continue to operate for a long period of time
without interacting with its environment. An open system continuously,
interacts with its environment. This type of system can adapt to changing
internal and environmental conditions. A business organization is an excellent
example of an open system.
·
Deterministic
and Probabilistic System: The behavior of a deterministic system
is completely known. There is no uncertainty involved in defining the outputs
of the system knowing the inputs. This implies that the interaction between
various subsystems is known with certainty. Computer program is a good example
of deterministic system, here, knowing the inputs, the outputs of the program
can be completely defined. In the probabilistic system, the behavior cannot be
predicted with certainty; only probabilistic estimates can be given. In this case,
the interactions between various subsystems cannot be defined with certainty.
·
MAN MADE
INFORMATION SYSTEM: COMPUTER BASED INFORMATION SYSTEM:
This
category of the information system mainly depends on the computer for handling
business applications. System analysts develop several different types of
information systems to meet a variety of business needs. There is a class of
systems known collectively as Computer Based Information System. Computer based
information systems are of too many types. They are classified as:
·
Transaction
processing System (TPS):
o
A
transaction processing system can be defined as a computer based system that
capture, classifies, stores, maintains, updates and retrieves transaction data
for record keeping. Transaction Processing system are aimed at improving the
routine business activities on which all organisations depend.
·
Management
Information Systems (MIS):
o
MIS
can be described as information system that can provide all levels of
management with information essential to the running of smooth business.
This information must be as relevant
timely, accurate, complete, concise and economically feasible must be as
relevant, timely, accurate, complete and concise as is economically feasible.
·
Decision
support system (DSS):
o
Decision support systems assist managers who
must make decisions that are not highly structured, often called unstructured
or semi-structured decisions. A decision is considered unstructured if there
are not clear procedures for making the decision and if not all the factors to
be considered in the decision can be readily identified in advance.
·
Office
Automation Systems (OAS):
o
Office
automation systems are among the newest and most rapidly expanding computer
based information systems. They are being developed with the hopes and
expectations that they will increase the efficiency and productivity of office
workers-typists, secretaries, administrative assistant, staff professionals,
managers and the like. Many organisations have taken the first step toward
automating their offices.
·
Open
System and Closed System
A closed system
is one which is self contained. It has
no interaction with its environment. No
known system can continue to operate for a long period of time without
interacting with its environment. An open system continuously, interacts with
its environment. This type of system can adapt to changing internal and
environmental conditions. A business
organization is an excellent example of an open system.
Many
mathematical models are confined to
closed systems. A special type of closed
system is called the black box. In such a system inputs and outputs are well defined but the
process itself is not specified. DSS attempt to deal with systems that are
fairly open. Such systems are complex, and during their analysis it is
necessary to check the impacts on and from the environment.
Major
differences between closed and open systems
Open
System
|
Closed
System
|
Open system
interacts or communicates with
the environment constantly
|
Whereas
a closed system does
not
react with the environment
|
An
open system has an infinite scope till the organization
services
|
Whereas
a closed system has
limited
shape
|
In
an open system relevant variables keep on interacting.
|
Whereas
the variables in a closed
system
are self contained
|
An
open system is generally
flexible
and abstract
|
Whereas
a closed system is rigid
and
mathematical
|
2.2
Models, Degree of model, Benefits of
model
A major characteristic of DSS is the inclusion of a modeling
capability. The basic idea is to execute the DSS analysis on a model of reality
rather than on reality itself.
A model is a physical,
mathematical, or otherwise logical representation of a system, entity,
phenomenon, or process. Briefly, it is a simplified representation or
abstraction of reality. It is usually simplified because reality
is too complex to copy exactly and because much of
the complexity is
actually irrelevant to
the specific problem. The characteristics of
simplification and representation are
difficult to achieve simultaneously in practice (they
contradict each other). The
representation of systems
or problems through models can
be done at
various degrees of abstraction;
therefore models are classified,
according to their
degree of abstraction, into three
groups.
Iconic (Scale)
Models. An iconic model , the least
abstract model , is
a physical replica of
a system or
graphical display that
looks like the
system being modeled, usually
based on a different scale from the original. Iconic models may appear to scale
in three
dimensions, such as
that of an
airplane, car, bridge,
or production line. Graphical user interface and object-oriented programming are
other examples of the use of icons.
Analog Models. An analog model does not look like the
real system but behaves like it. It is more abstract than an iconic model and
is considered a symbolic representation of reality. There are usually
two–dimensional charts or diagrams: that is they could be physical models, but
the shape of the model differs from that of the actual system.
Mathematical
(Quantitative) Models. The
complexity of relationships
in many organizational systems
cannot be represented
with icons or
analogically, or such representation may be
cumbersome and time-consuming. Therefore a more
abstract model is used
with the aid
of mathematics. Most
DSS analysis is
executed numerically with the aid of mathematical or other quantitative
models.
With recent advances
in computer graphics,
there is an
increased tendency to use
iconic and analog models to complement mathematical modeling in DSS.
•
Iconic: Small
physical replication of system
•
Analog: Behavioral
representation of system, May not look like system
•
Quantitative
(mathematical): Demonstrates relationships between systems
2.3
The Decision Making Process
Simon’s original three
phases:
·
Intelligence
·
Design
·
Choice
He added fourth phase
later:
·
Implementation
Phase
|
Major
Activities
|
Intelligence
|
Scan the environment ,Analyze
organizational goals, Collect data, Identify problem, Categorize problem,
Programmed and non-programmed, Decomposed into smaller parts, Assess
ownership and responsibility for problem resolution
|
Design
|
Develop alternative courses of action,
Analyze potential solutions, Create model, Test for feasibility , Validate
results, Select a principle of choice, Establish objectives, Incorporate into
models, Risk assessment and acceptance, Criteria and constraints
|
Choice
|
•
Develop
alternative courses of action
•
Analyze
potential solutions
•
Create
model
•
Test for
feasibility
•
Validate
results
•
Select a
principle of choice
–
Establish
objectives
–
Incorporate
into models
–
Risk
assessment and acceptance
–
Criteria
and constraints
•
Principle
of choice
–
Describes
acceptability of a solution approach
•
Normative
Models
–
Optimization
•
Effect of
each alternative
–
Rationalization
•
More of
good things, less of bad things
•
Courses of
action are known quantity
•
Options
ranked from best to worse
–
Sub-optimization
•
Decisions
made in separate parts of organization without consideration of whole
|
Implementation
|
•
Putting
solution to work
•
Vague
boundaries which include:
–
Dealing
with resistance to change
–
User
training
–
Upper
management support
|
Unit 3
2
Overview of Decision Support System (DSS)
2.1
Introduction:
Decision
Support Systems (DSS) are a class of computerized information system which designed
to support managerial decision-making in unstructured problems and support
decision-making activities. DSS are interactive computer-based systems and
subsystems intended to help decision makers use communications technologies,
data, documents, knowledge and/or models to complete decision process tasks. Typical
information that a decision support application might gather and present would
be,
·
Accessing all
information assets, including legacy and relational data sources;
·
Comparative data figures;
·
Projected figures based on new data or
assumptions;
·
Consequences of
different decision alternatives, given past experience in a specific context.
A DSS is a methodology that supports decision-making. It is:
Flexible; Adaptive; Interactive; GUI-based; Iterative; and Employs modeling.
2.2
Characteristics of DSS
2.1
Components of DSS
•
Three fundamental components of DSS:
–
the database management system (DBMS),
–
the model management system (MBMS), and
–
the dialog generation and management system
(DGMS).
•
the Data Management Component stores information
(which can be further subdivided into that derived from an organization's
traditional data repositories, from external sources such as the Internet, or
from the personal insights and experiences of individual users);
•
the Model Management Component handles
representations of events, facts, or situations (using various kinds of models,
two examples being optimization models and goal-seeking models); and
•
User Interface Management Component is of course
the component that allows a user to interact with the system
2.1
What do Decision Support
Systems Offer?
·
Quick
computations at a lower cost
·
Group
collaboration and communication
·
Increased
productivity
·
Instant
access to information stored in multiple databases and data warehouses
·
Ability to
analyze multiple alternatives and apply risk management
·
Enterprise
resource management
·
Tools to
obtain and maintain competitive advantage
The combination of the Internet, which
enables speed and access, and the maturation of artificial intelligence
techniques have led to sophisticated aids to support decision making under
these risky and uncertain conditions. These aids have the potential to improve
decision making by suggesting solutions that are better than those made by the
human alone.They are increasingly available in diverse fields from medical
diagnosis to traffic control to engineering applications.
2.2 Applications:
There are theoretical possibilities
of building such systems in any knowledge domain.
l Clinical
decision support system for medical diagnosis.
l a
bank loan officer verifying the credit of a loan applicant
l an
engineering firm that has bids on several projects and wants to know if they
can be competitive with their costs.
l DSS
is extensively used in business and management. Executive dashboards and other
business performance software allow faster decision making, identification of
negative trends, and better allocation of business resources.
l A
growing area of DSS application, concepts, principles, and techniques is in
agricultural production, marketing for sustainable development.
l A
specific example concerns the Canadian National Railway system, which tests its
equipment on a regular basis using a decision support system.
l A
DSS can be designed to help make decisions on the stock market, or deciding
which area or segment to market a product toward.
2.3
Types of Decision Problems
·
Structured:
situations where the procedures to follow when a decision is needed can be
specified in advance
o
Repetitive
o
Standard
solution methods exist
o
Complete
automation may be feasible
o
Mostly use
by operation level management
o
·
Unstructured:
decision situations where it is not possible to specify in advance most of the
decision procedures to follow
o
One-time
o
No standard
solutions
o
Rely on judgment
o
Automation
is usually infeasible
·
Semi-structured:
decision procedures that can be pre-specified, but not enough to lead to a
definite recommended decision
o
Some
elements and/or phases of decision making process have repetitive elements
o
DSSs most
useful for repetitive aspects of semi-structured problems
TYPE OF DECISION:
|
TYPE OF CONTROL
|
||
Operational Control
|
Managerial Control
|
Strategic Planning
|
|
Structured
(Programmed)
|
Accounts
receivable, accounts payable, order entry
|
Budget
analysis, short-term forecasting, personnel reports, make or buy
|
Financial
management - Investments, warehouse locations, distribution centers
|
Semistructured
|
Production
scheduling, inventory control
|
Credit
evaluation, budget preparation, plant layout, project scheduling, rewards
systems, inventory categorization
|
Building
new plant, Mergers and acquisitions, new product planning, compensation,
quality assurance, HR policy planning, inventory planning
|
Unstructured
(Unprogrammed)
|
Selecting
a cover for a magazine, Buying software, approving loans, help desk
|
Negotiations,
recruiting an executive, hardware purchasing, lobbying
|
R&D
planning, new technology development, social responsibility planning
|
2.1
Stages of decision making
·
Define The Situation: Of the stages in decision making, this is
probably the most significant step. It is vital to have a good understanding
and be very clear about a) the situation and b) what you want to achieve.
·
Generate alternatives :The number of
alternatives you choose will depend on such factors as experience,
knowledge, skills, number of people involved in generating alternatives and
what's considered important.
·
Information gathering As alternatives are suggested, it may require
further clarification of the situation or the decision to be made
·
Selection
Selection is the choosing of one of the alternatives
·
Action Great decisions are only great when they
are carried into action and the action achieves the desired result.
- Evaluate options that will solve the problem, pros, cons and risks of each alternative
- Select the best option- may be necessary to loop back and gather more info
- Develop a plan of action - and implement it!
4. Database for Decision Support System
Components of DSS
The
Database :
·
Internal data come mainly from the organization’s transaction processing
system
·
External data include industry data, market research data, census data,
regional employment data, government regulations, tax rate schedules, and
national economic data
·
Private data can include guidelines used by specific decision makers and
assessments of specific data and/or situations
Data
Organization
Data for DSS can be
entered directly into models
extracted directly from larger
databases e.g. Data Warehouse
Can include multimedia objects
Data
extraction: The process of capturing data from several sources, synthesizing
them, summarizing them, determining which of them are relevant,and organizing
them, resulting in their effective integration. The process of
capturing data from several sources
synthesizing, summarizing
determining which of them are
relevant
and organizing them
resulting in their effective
integration
A
database is created, accessed and updated by a DBMS
Software for establishing, updating,
and querying e.g. managing a database
¢ record navigation
¢ data relationships
¢ report generation
Query
Facility
• The (database) mechanism that
–
accepts requests for data
–
accesses
–
manipulates
–
and queries data
• Includes a query language
–
e.g. SQL
Data
Directory
• A catalog of all the data in a
database or all the models in a model base
• Contains
–
data definitions
–
data source
–
data meaning
• Supports addition and deletion of new
entries
Key DB
& DBMS Issues
• Data quality
–
“Garbage in/garbage out" (GIGO)
–
Managers feel they do not get the data they need – 54% satisfied
–
Poor quality data leads to poor quality information
•
waste
•
lost opportunities
•
unhappy customers
• Data integration
–
For DSS to work, data must be integrated from disparate sources
–
“Creating a single version of the truth”
• Scalability
–
Volume of data increases dramatically
•
e.g. from 2001 – 2003, size of largest
TPS DB increase two-fold (11 – 20 terabytes)
–
Needs new storage and search technologies
• Data security
–
data must be protected from unauthorized access through security measures
–
tools to monitor database activities
–
audit trail
Database technologies can be applied into two types of
scenarios:
- Transaction Processing(OLTP)
- Analytic Processing, using statistical method(OLAP) or
machine/computational learning method(Data Mining)
OLTP, which is based on E.F. Codd's relation model, is the
traditional (maybe most popular) application type of DBMS and most people are
very familiar with it. This post tries to summarize related technologies in
analytic processing, which is widely adopted in decision support systems.
Operational data (in OLTP system) is extracted,
transformed(also cleaned) and loaded(by ETL subsystem) into the data warehouse
for further analyzing. OLAP and DM systems read these data, analyze them,
produce useful reports and present them to end(business) users.
OnLine Analytical Processing(OLAP)
OLAP is the processing of large scale multidimensional data
using statistical based methods.
Multidimensional Model
Multidimensional model view data as as cubes that generalize
spreadsheets to any number of dimensions. It categorizes data either as
numerical values(a.k.a. measures) associated with some facts or textual
values(a.k.a. dimensions) that characterize the facts.
OLAP Server
Architecture
ROLAP (Relational
OLAP)
Fact data is stored in relation model based storage system
and some special induces technologies may be adopted. In this architecture,
measures are derived from the records in the fact table and dimensions are derived
from the dimension tables.from star schema example
This architecture can be further divided into two types:
Star Schema
Snowflake Schema
- MOLAP (Multidimensional OLAP)
Fact data is stored in an optimized multi-dimensional array
storage, i.e., the server supports the multidimensional model directly.
- HOLAP (Hybrid OLAP)
It is a combination of ROLAP and MOLAP. HOLAP allows storing
part of the data in a MOLAP store and another part of the data in a ROLAP
store.
Unit
-5
4. Modeling
and DSS Model management
Decision
Support Systems (DSS) are a class of computerized information system that
support decision-making activities. DSS are interactive computer-based systems
and subsystems intended to help decision makers use communications
technologies, data, documents, knowledge and/or models to complete decision
process tasks. Typical information that a decision support application might
gather and present would be,
·
Accessing all
information assets, including legacy and relational data sources;
·
Comparative data figures;
·
Projected figures based on new data or
assumptions;
·
Consequences of
different decision alternatives, given past experience in a specific context.
4.1 MODEL DRIVEN DSSs
Model-Driven DSS: This type of DSS emphasizes access to and manipulation of
a model, e.g., statistical, financial, optimization and/or simulation. Simple
statistical and analytical tools provide the most elementary level of
functionality.
In general, model-driven DSS use complex financial, simulation,
optimization and/or rule (expert) models to provide decision support.
Model-driven DSS use data and parameters provided by decision makers to aid
decision makers in analyzing a situation, but they are not usually data
intensive, that is very large data bases are usually not need for model-driven
DSS.
Simple examples: a spread-sheet with formulas, a statistical forecasting model.
But also complex, initially statistical modelling oriented, software packages,
like SAS or STATISTICA, as well as modules of multiple contemporary data
manipulation systems of .... Further step: Business Analytics
4.2 DATA DRIVEN
DSSs
This type of DSS emphasizes access to and manipulation of a time-series of
internal company data and sometimes external data
·
Basic level: queries and reports
·
Intermediate level: Data Warehousing (Data Warehouse),
increasing the efficiency of data sharing
·
Highest level: use of advanced analytical techniques:
On-line Analytical Processing (OLAP), and cross-sectional multilevel analysis
(data cubes)
Most data-driven DSSs are targeted at
managers, staff and also product/service suppliers. Data-driven DSS is a type of DSS
that emphasizes access to and manipulation of a time-series of internal company
data and sometimes external data. Simple file systems accessed by query and
retrieval tools provide the most elementary level of functionality. Data
warehouse systems that allow the manipulation of data by computerized tools
tailored to a specific task and setting or by more general tools and operators
provide additional functionality. Data-driven DSS with On-line Analytical
Processing (OLAP) provides the highest level of functionality and decision
support that is linked to analysis of large collections of historical data.
Executive Information Systems (EIS) and Geographic Information Systems (GIS)
are special purpose Data-Driven DSS.
·
A Data Warehouse is a database designed to
support decision making in organizations. It is batch updated and structured
for rapid online queries and managerial summaries. Data warehouses contain
large amounts of data. A data warehouse is a subject-oriented, integrated,
time-variant, nonvolatile collection of data in support of management's
decision making process.
·
On-line Analytical Processing (OLAP) software
is used for manipulating data from a variety of sources that has been stored in
a static data warehouse. The software can create various views and
representations of the data. For a software product to be considered an OLAP
application it must contain three key features: 1. multidimensional views of
data; 2. complex calculations; and 3. time oriented processing capabilities.
·
Executive Information Systems (EIS) are
computerized systems intended to provide current and appropriate information to
support executive decision making for managers using a networked workstation.
The emphasis is on graphical displays and an easy to use interface that present
information from the corporate database. They are tools to provide canned
reports or briefing books to top-level executives. EIS offer strong reporting
and drill-down capabilities.
·
A Geographic Information System (GIS) or
Spatial DSS is a support system that represents data using maps. It helps
people access, display and analyze data that have geographic content and
meaning.
4.3 KNOWLEDGE
DRIVEN DSSs
Knowledge-Driven DSS: can suggest or recommend actions to managers.
These DSS are person-computer systems with specialized problem-solving
expertise. The "expertise" consists of knowledge about a particular
domain, understanding of problems within that domain, and "skill" at
solving some of these problems. The resulting systems are often called Expert
Systems.
Tools used for building Knowledge-Driven DSS (knowledge acquisition,
machine learning, uncertainty management) are sometimes called intelligent
techniques, while the systems (tools) are called Intelligent Decision Support Systems.
Such systems are close to the classical meaning of a DSS (interactive, limited
use of data and models, suggest solution or recommendation).
Data Mining tools can be used to create Knowledge-driven DSS that have
major data and knowledge components.
4.4 COMMUNICATIONS
DRIVEN DSS
systems that use network and communications technologies to facilitate
collaboration and communication. The communications technologies are central to
supporting decision-making. Technologies include: LANs, WANs, Internet, ISDN,
Virtual Private Networks.
4.5 Document-driven
DSS - no need to
introduce Google or Web of Science/Web of Knowledge. Such systems integrate a
variety of storage and processing technologies to provide complete document
retrieval and analysis. The Web provides access to large document databases
including databases of hypertext documents, images, sounds and video. A search
engine is a primary tool associated with a Document-Driven DSS.
Compression
between data driven and model driven DSS:
Model-driven DSS
|
Data-driven DSS
|
User
interacts primarily with a (mathematical) model and its results
|
User
interacts primarily with the data
|
Helps to
solve well-defined and structured problem (what-if-analysis)
|
Helps to
solve mainly unstructured problems
|
Contains in
general various and complex models
|
Contains in
general simple models
|
Large amounts
of data are not necessary
|
Large amounts
of data are crucial
|
Helps to
understand the impact of decisions on organizations
|
Helps to
prepare decisions by showing developments in the past and by identifying
relations or patterns
|
4.1 What if analysis:
In the early
days of decision support systems, one of the major DSS "selling
points" of vendors and academics was the ability to do "What
If?" analysis. In the 1970s, model-driven DSS for sales and production
planning allowed a manager to change a decision variable like the number of
units to produce and then immediately get a new result for an outcome variable
like profit. As DSS have gotten more sophisticated and become more diverse, the
use of "What If?" as a concept has broadened.
In a nutshell,
what-if analysis can be described as a data-intensive simulation whose goal is
to inspect the behavior of a complex system (i.e., the enterprise business or a
part of it) under some given hypotheses (called scenarios). More pragmatically,
what-if analysis measures how changes in a set of independent variables impact
on a set of dependent variables with reference to a given simulation model ;
such model is a simplified representation of the business, tuned according to
the historical enterprise data. A simple example of what-if query in the
marketing domain is: How would my profits change if I run a 3 × 2 promotion for
one week on some products on sale?
What-if
analysis should not be confused with sensitivity analysis, aimed at evaluating
how sensitive is the behavior of the system to a small change of one or more
parameters. Besides, there is an important difference between what-if analysis
and simple forecasting, widely used especially in the banking and insurance
fields. In fact, while forecasting is normally carried out by extrapolating
trends out of the historical series stored in information systems, what-if
analysis requires to simulate complex phenomena whose effects cannot be simply determined as a
projection of past data, which in turn requires to build a simulation model
capable of reproducing – with satisfactory approximation – the real behavior of
the business. For the same reason, the design of what-if applications is also
more complex than that of conventional DWs, which only relies on a static model
of business.
What if
analysis - Observing how changes to selected variables affect other
varibales. Example: What happens to sales if we cut advertising
by 10%?
4.2 Sensitivity
analysis (SA) is
the study of how the uncertainty in the output of a model (numerical or
otherwise) can be apportioned to different sources of uncertainty in the model
input. A related practice is uncertainty analysis which focuses rather on
quantifying uncertainty in model output. Ideally, uncertainty and sensitivity
analysis should be run in tandem. Sensitivity analysis - Observing how
repeated changes to a single variable affect other variables. Example: If advertising is cut by $100 repeatedly, to
see how sales change
In
more general terms uncertainty and sensitivity analysis investigate the
robustness of a study when the study includes some form of statistical
modeling. Sensitivity analysis can be
useful to computer modelers for a range of purposes, including:
·
Support
decision making or the development of recommendations for decision makers (e.g.
testing the robustness of a result);
·
Enhancing communication from modelers to
decision makers (e.g. by making recommendations more credible, understandable,
compelling or persuasive);
·
Increased understanding or quantification of
the system (e.g. understanding relationships between input and output
variables); and
·
Model development (e.g. searching for errors
in the model).
Let us give an example: in any
budgeting process there are always variables that are uncertain. Future tax
rates, interest rates, inflation rates, headcount, operating expenses and other
variables may not be known with great precision. Sensitivity analysis answers
the question, "if these variables deviate from expectations, what will the
effect be (on the business, model, system, or whatever is being
analyzed)?"
4.3 Goal-Seeking
analysis: it compiles all of the given
data and determines what inputs are required to reach specific goals.
Sensitivity analysis is great and can be used to determine what portions of
DSS, effect one and other. However, it does not look at the bottom line. It
just demonstrates how portions interact with one and other. In addition,
What-If analysis just looks as the possibilities and given scenarios. It
attempts to determine how well things could or could not go. These are both
great however, they fail to look at the overall picture. In order to reach
goals, these specific requirements need to be met. In addition, What-if
analysis uses Goal-seeking analysis. It looks at what numbers and goals are
required in order to well, just average, and to do poorly. The whole purpose of the DSS is to compile
raw data into useful information that managers can use effectively and apply to
organizational and business decisions.
Goal-seeking
analysis - Making repeated changes to selected variables until a chosen
variable reaches a target value.
Example, Increase advertising until sales reach $5,000,000 USD.
Optimization
analysis - Finds an optimum value for selected variable given certain
constraints: Example: What is the best amount of advertising to
have, given budget and media choice?
Unit
-6
6
DSS User
Interface
6.1 Issue
related to building an interface
An
effective user interface is an important component of any type of Decision
Support System, but it is especially
important for systems that will be used directly by managers. In a Decision Support System, the user interface
is sometimes called the dialogue or "front-end" component. Some might
ask "why is the user interface or dialogue component of a DSS so
important?" Research indicates that the easier it is to use a DSS
interface, then the more "user friendly" most people will consider
the system and hence the greater the chance that managers will actually use the
DSS. The user interface is what managers see when they work with a DSS.
The
goal of user interface design is developing screen layouts and interfaces that
are easy to use and that are visually attractive (cf., Galitz, 1985). Both the
intended users of a Decision Support System and information systems designers
need to participate actively in designing and evaluating DSS user interfaces.
Let’s examine the concept of a user interface.
Most
of these users do NOT want to learn a command language interface like
Structured Query language (SQL) that may be used by an expert or by a more
technically-oriented user. According to Bennett (1986), for a non-technical
user the design of an appropriate DSS user interface is the most important
determinant of the success of a decision support implementation.
A
well-designed user interface can increase human processing speed, reduce
errors, increase productivity and create a sense of user control. The quality of
the system interface, from the user's perspective, depends upon what the user
sees or senses, what the user must know to understand what is sensed, and what
actions the user can and in some cases must take to obtain needed results.
Both
groups need to be familiar with the following important issues and topics
related to building and evaluating a user interface:
·
User
interface style – Is the style or combination of styles appropriate? What
styles are used in the user interface?
·
Screen
design and layout -- Is the design easy to understand and attractive? Is the
design symmetric and balanced?
·
The
Human-Software interaction sequence -- Is the interaction developed by the
software logical and intuitive? Do people respond predictably to the
interaction sequence?
·
Use
of colors, lines and graphics -- Are colors used appropriately? Do graphics
improve the design or distract the user? 5.
Information density -- Is too much information presented on a screen?
Can users control the information density?
·
Use
of icons and symbols -- Are icons understandable?
·
Choice
of input and output devices -- Do devices fit the task?
Managers and analysts should
focus on these seven design issues when they evaluate a DSS prototype or the
proposed screens for a DSS.
6.2 Issue
related to building an interface
The user interface determines how
information is entered in a DSS and how it is displayed by a DSS. The interface also determines
the ease and simplicity of learning and using the system. There are four
general structures or interface styles that can be used to control interactions
with computerized information systems. These styles are
·
command-line
interfaces,
·
menu interfaces,
·
point-and-click
graphical interfaces, and
·
question-and-
answer interfaces.
Each style can be used in creating DSS user
interfaces. The styles can often be combined usefully in a single application
or set of related applications [see Galitz (1985); Shneiderman (1992); and
Turban (1993)]. When building a user interface a designer should try to provide
multiple ways to perform the same task. For example, a design may include a
command-line interface, pull down menus for commands, and keyboard command
equivalents. Many input devices including keyboard, mouse, touch pad, and voice
inputs can be used to manipulate these four general interface styles.
Unit -7
6
Constructing
a DSS :
6.1 DSS architecture
DSS
for Management Support: DSS structure – DSS architecture [Turban 2011]
Many configurations exist, based on
management-decision situation or specific technologies used for support
DSS have three basic components
1. Data
2. Model
3. User interface
4. (+ optional) Knowledge
Each component: has several variations; is typically deployed online, managed by a
commercial of custom software
Unit
8
6
Organizational DSS (ODSS) and Advanced Topics
8.1
Introduction and Characteristics
Organizational
decision support systems (ODSS) are a class of decision support systems that
promise to provide support at a higher organizational level for businesses than
preceding forms of decision support. The existing literature provides many
different descriptions of ODSS and its functionality. This leads to considerable
confusion as to what is necessary for a system to be called an ODSS. This
confusion has inhibited the development of ODSS both conceptually and in terms
of implementation. We begin by considering the existing conceptual base upon
which ODSS
The
concept of organization decision support system (ODSS) is defined according to
practical applications and novel understanding. And a framework for ODSS is
designed. The framework has three components: infrastructure, decision-making
process and decision execution process. Infrastructure is
responsible to transfer data and information. Decision-making process is the
ODSS's soul to support decision-making. Decision execution process is to
evaluate and execute decision results derived from decision-making process. The
framework presents a kind of logic architecture. An example is given to verify
and analyze the framework
There is
some controversy in the MIS literature concerning the potential impact of
computer systems on interpersonal communication in organizations. Generally,
MIS researchers have found that effective communication between users and
designers is an important factor in determining user satisfaction and MIS success.
Recently,
however, Naylor [8] has argued that decision support systems (DSS) may isolate
managers from interaction with others. This is in marked contrast to the
contentions of Wagner [16] and Huber [6] that DSS may lead to more effective
managerial communication.
We would
argue that DSS encourage communication for several reasons: adaptive DSS
development requires continued manager-analyst interaction; DSS-based decisions
often require groups of managers; and there is a greater need for human
information processing in a DSS environment.
This
paper reports the results of a study of the relationships between DSS usage and
organizational communications. It was found that users in three different
categories (managers, financial or planning analysts (FPAs), and “others”) felt
that DSS usage encouraged communication. Also, significant positive
relationships were found between increased communication and overall
satisfaction with the DSS for all three groups. Finally, for managers and FPAs,
significant positive relationships were found between increased communication
and satisfaction with the DSS in decision-making activities.
Unit
9
7
Group Decision Support Systems (GDSS)
Group
decision support systems (GDSS) are interactive, computer-based systems that
facilitate solution of semi-structured and unstructured problems by a
designated set of decision-makers working together as a group. A GDSS can
assist groups, especially groups of managers, in analyzing problem situations
and in performing group decision making tasks. GDSS include structured decision
tools for tasks like brainstorming, commenting on ideas, and rating and ranking
of alternatives (cf., DeSanctis and Gallupe, 1987).
Decision
making is frequently a shared process. For example, meetings among groups of
managers from different areas are an essential element for reaching consensus.
The group may be involved in making a decision or in a decision-related task,
like creating a short list of acceptable alternatives or deciding on criteria
for accepting an alternative. When a decision-making group is supported
electronically, the support is referred to as group decision support. Two types
of groups are considered: a one-room group whose members are in one place
(e.g., a meeting room), and a virtual group, whose members are in different
location.
An increasing number of companies are useng
GDSSs, especially when virtual groups are involved. One example is the Internal
Revenue Service, which used a one-room GDSS to implement its
quality-improvement programs based on the participation of a number of its
quality teams. The GDSS was helpful in identifying problems, generating and
evaluating ideas, and developing and implementing solutions. Another example is
the European automobile industry, which used a one-room GDSS to examine the
competitive automotive business environment and make ten-year forecasts, needed
for strategic planning.
A
group decision support system (GDSS) is an interactive computer-based system
that facilitates the solution of semistructured and unstructured problems when
made by a group of decision makers. The objective of a GDSS is to support the
process of arriving at a decision. The first generation of GDSSs was designed
to support face-to-face meetings in what is called decision room.
Group
Decision Support Systems (GDSS) - An interactive, computer-based system that
facilitates solution of unstructured problems by a set of decision-makers
working together as a group. It aids groups, especially groups of managers, in
analyzing problem situations and in performing group decision making tasks. Group
Support Systems has come to mean computer software and hardware used to support
group functions and processes.
Characteristics
of Group Decision Support System
·
Designed
with the goal of supporting groups of decision-makers in their work.
·
The
GDSS accommodates users with varying levels of knowledge regarding computing
and decision support.
·
Can
be designed for one type of problem or for a variety of group-level
organizational decisions.
·
Encourages
generation of ideas, resolution of conflicts, and freedom of expression.
·
Supporting parallel processing of information
and idea generation by participants.
·
Enabling
larger groups with more complete information, knowledge, and skills to
participate in the same meeting.
·
Permitting
the group to use structured or unstructured techniques and methods of arriving
at decisions.
·
Offering
rapid and easy access to external information.
·
Producing instant, anonymous voting results.
·
Enabling
several users to interact simultaneously.
Automatically
recording all information that passes through the system for future analysis
(it
The Advantage, or process gains, from using a GDSS (over more traditional
group techniques) are:
·
More precise communication;
·
Synergy: members are empowered to
build on ideas of others;
·
More objective evaluation of ideas;
·
Stimulation of individuals to
increase participation;
·
More
participation
·
Group
synergy
·
Automated
record keeping
Disadvantages
There are some disadvantages to the technology, however, and they include:
· Slow Communication
· Not all Tasks are Amenable to GDSSs
• Decision Room– For decision makers located in the same geographic area or building– Use of computing devices, special software, networking capabilities, display equipment, and a session leader– Collect, coordinate, and feed back organized information to help a group make a decision– Combines face-to-face verbal interaction with technology-aided formalizationThe Decision Room refers to the physical arrangement where the use of decision room software or Group DSS is normally utilized: For decision makers located in the same geographic area or building With the use of computing devices, special software, networking capabilities, display equipment and a session leader. To collect, coordinate and feedback organized information to help a group make a decision andCombines face-to-face verbal interaction with technology-aided formalization.The objective in using a Decision Room is to enhance and improve the group's decision-making process. Characteristics of a Decision Room include:Each participant has a computer workstation or terminal for anonymous input;A leader (facilitator) coordinates the meeting;The room has a display screen that all participants can view;Computers are networked and client/server architecture is used; andSpecialized software is available to all participants for “brainstorming” and to support voting.Wide Area Decision Network• Characteristics– Location of group members is distant– Decision frequency is high– Virtual workgroups• Groups of workers located around the world working on common problems via a GDSSTYPES OF GDSS:connection management systemso providing a physical mechanism through which people involved in a decision can communicateo e.g.: WAN architecture communication management:o enhance information flow by means of facilities to store, reply, forward etco e.g. electronic mail packages and discussion groups· content management systemso provides intelligent routing – the system knows where a document goes after its current user finishes with it, or where the messages should go once it is enteredo e.g. decision conference systems process managemento considering the content of the information in the flow in deciding what to do with itLevels of Group Decision Support SystemsThere are three levels of Group Decision Support Systems:· Level 1 GDSS - provide technical features aimed at removing common communication barriers such as voting, electronic message exchange· Level 2 GDSS - provide decision modeling and group decision techniques aimed at reducing uncertainty that occur in the group’s decision process.· Level 3 GDSS - machine-induced group communication patterns and can include expert advice in the selecting and arranging of rules to be applied during a meeting.Unit- 1010. Distributed Group Support SystemsDistributed Group Support Systems use asynchronous computer mediated communication to support anytime/anywhere group discussions and decision making. GDSS type tools generally appear to improve both objective and subjective outcomes, various process interventions have had little or no effect on these groups, which had one to four weeks to adapt the use of the system features to their own expectations and preferences.One type of computer-based system to support collaborative work is most often called a Group Support System, or GSS. Other terms that have been used include "Group Decision Support Systems" ("GDSS;" and "Electronic Meeting Systems.Benefit of Group work· To facilitate communication: faster, clearer, more persuasive Telecommuting· Cut down on travel costs· Bring together multiple perspectives and expertise· Knowledge management· Form groups with common interests where it wouldn’t be possible to gather a sufficient number of people face-to-face· Save time and cost in coordinating group work· Facilitate group problem-solving· Enable new modes of communication, such as anonymous interchanges or structured interactions· Saves meeting time· Leveraging professional expertiseProblems of Distributed support system· Significantly more difficult to get right than traditional software· System can’t succeed unless most or all the target group is willing to adopt the system· Integration into existing systems can be difficult· Testing is difficult and roles change due to volatility of groups· Resistance to change· Problems are not technical, but socialDistance learning is a process of transferring knowledge to learners (students) who are separated from the instructor (teacher) by time and/or physical distance and are making use of technology components, such as the Internet, video, CD’s, tapes, and other forms of technology to accomplish learning.Is there any difference between “distance learning” and “distance education”? These two terms are used synonymously in education and learning technologies. It is believed that distance learning has been a method of teaching and learning for many individuals for at least one hundred years (Moore & Kearsley, 1996) starting with print technology and the postal service (correspondence education) all the way up to the electronic communication that is used today.Distance Learning (DL) is an instructional delivery system that connects learners with educational resources. DL provides educational access to learners not enrolled in educational institutions and can augment the learning opportunities of current students. The implementation of DL is a process that uses available resources and will evolve to incorporate emerging technologiesThere are two distance education delivery system categories - synchronous and asynchronous. Synchronous instruction requires the simultaneous participation of all students and instructors. The advantage of synchronous instruction is that interaction is done in "real time" and has an immediacy. Examples include interactive tele-courses, teleconferencing and web conferencing, and Internet chats.Asynchronous instruction does not require the simultaneous participation of all students and instructors. Students do not need to be gathered together in the same location at the same time. Rather, students may choose their own instructional time frame and interact with the learning materials and instructor according to their schedules. Asynchronous instruction is more flexible than synchronous instruction, but experience shows that time limits are necessary to maintain focus and participation. The self-paced format accommodates multiple learning levels and schedules. Examples of asynchronous delivery include e-mail, audiocassette courses, videotaped courses, correspondence courses, and WWW-based courses.Computer conferencing occurs when two or more users are teleconferencing using two or more computers. Usually, one of the conference participants is in charge of handling what is on the computer screen for all others to see. That person is referred to as a host or moderator. Individuals can all participate in a conference call while looking at one presentation on the computer. They can also communicate by instant messaging. Some computer conferences occur simultaneously with a telephone conference. That way, participants can chat via telephone while they watch what's occurring on the screen.They only need an Internet connection. A host sends out an email to participants with a link to the conference room or web room and the date and time of the conference. If someone plans to participate, he can accept the invitation right from the emailThere are several features of computer conferencing. First, most participants have the ability to set their notifications if they're going to be busy, on the phone, or away from their computer screen during the conference. The instant message feature allows participants to ask questions. The questions may go to all of the participants or can simply go to the host of the conference. Participants can also download manuals or notes in pdf format to accompany the conference. Quizzes and exercises can be taken during breaks in the conference. This is perfect for eLearning, as students have hands-on activities they can perform that will help them retain what they learn. Electronic bulletin boards can be set up for information or questions to be posted.A misconception of computer conferencing is that people may be under the impression that they'll receive answers immediately. This is not always the case. Often, websites and forums are not moderated 100 percent of the time. You may post a question but not receive an answer quickly, or even at all. Follow-ups may be necessary.Unit- 1111. Executive Information and Support SystemsAn Executive Support System ("ESS") is designed to help senior management make strategic decisions. It gathers, analyses and summarises the key internal and external information used in the business.An executive support system also referred to as ESS is a smart and useful reporting tool that turns your organization’s data into summarized reports.Reports are usually needed by executive managers in order to have quick access to company details at all levels and also information on departments such as billing, staffing, scheduling and others. Executive support systems are also used for making analysis in order to predict performance outcomes and reports. Executives mainly use ESS to quickly see statistics and numbers that are needed for decision-making.An Executive Information Systems (EIS) is a type of management information system intended to facilitate and support the information and decision making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization. It is commonly considered as a specialized form of a Decision Support System (DSS) and otherwise referred to as an Executive Support System (ESS).In recent years and in the USA, the term EIS has lost popularity in and the terms “business intelligence” and “online analytical processing” are often used for these types of applications.Types of Executive Information System· Corporate Management - responsible for business and fiscal planning, budgetary control, as well as for ensuring the corporate information technology needs are met in a co-ordinated and cost effective manner. E.g., Management functions, human resources, financial data, correspondence, performance measures, etc. (whatever is interesting to executives)· Technical Information Dissemination – for the purpose of disseminating the latest information on relevant technologies, products, processes and markets E.g., Energy, environment, aerospace, weather, etc.Executive Support Systems Characteristics.A number of definitions have been put forward to describe EISs. While a definition is useful, in a complex area such as EISs a better understanding is obtained by looking at their characteristics. Some of these are given below:· Executive support systems are end-user computerised information systems operated directly by executive managers. They utilise newer computer technology in the form of data sources, hardware and programs, to place data in a common format, and provide fast and easy access to information.· They integrate data from a variety of sources both internal and external to the organisation.· They focus on helping executives assimilate information quickly to identify problems and opportunities. In other words, EISs help executives track their critical success factors.Capabilities of Executive Support SystemsMost executive support systems offer the following capabilities:· Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information· Drill-down – enables users to get details, and details of details, of information· Slice-and-dice – looks at information from different perspectives· Digital dashboard – integrates information from multiple components and presents it in a unified display.Advantages of Executive Information System· As more executives come up through the ranks, they are more familiar with and rely more on technology to assist them with their jobs. Executive Support Systems don't provide executives with ready- made decisions. They provide the information that helps them make their decisions. Executives use that information, along with their experience, knowledge, education, and understanding of the corporation and the business environment as a whole, to make their decisions.· Executives are more inclined to want summarized data rather than detailed data (even though the details must be available). ESS rely on graphic presentation of information because it's a much quicker way for busy executives to grasp summarized informationIt provides timely delivery of company summary information.· It provides better understanding of information· It filters data for management.· It provides system for improvement in information tracking· It offers efficiency to decision makers.Disadvantages of Executive Information System· Functions are limited, cannot perform complex calculations.· Hard to quantify benefits and to justify implementation of an EIS.· Executives may encounter information overload.· System may become slow, large, and hard to manage.· Difficult to keep current data.· May lead to less reliable and insecure data.· Small companies may encounter excessive costs for implementation.· Highly skilled personnel requirement can not be fulfilled by the small business.
Unit
-12
12. Overview of Applied Artificial Intelligence (AI) and Problem
Solving
12.1
AI concepts ,characteristics
Intelligence is the ability to think, to imagine,
creating, memorizing, and understanding, recognizing patterns, making choices,
adapting to change and learn from experience. Artificial intelligence is a
human endeavor to create a non-organic machine-based entity that has all the
above abilities of natural organic intelligence
A scientific and
engineering discipline devoted to: understanding principles that make
intelligent behavior possible in natural or artificial systems; developing
methods for the design and implementation of useful, intelligent artifacts.
A field of study that
explores how computers can be used for tasks that require human characteristics
of: intelligence, imagination , intuit ion
In
order for something to be considered an "Artificial Intelligence,"
there are a few different characteristics that are required... Some of these
characteristics include the following abilities:
·
The ability to act intelligently, as a
human.
·
The ability to behave following
"general intelligent action."
·
The ability to artificially simulate the
human brain.
·
The ability to actively learn and adapt
as a human.
·
The ability to process language and symbols.
As
can be seen from just these few examples, Artificial Intelligence primarily
concerns the ability for a computer to mimic human intelligence. That is its
key characteristic.
12.1
Practical Uses and Applications
·
Game playing
You can buy machines that can play master level chess for a few hundred
dollars. There is some AI in them, but they play well against people mainly
through brute force computation--looking at hundreds of thousands of positions.
To beat a world champion by brute force and known reliable heuristics requires
being able to look at 200 million positions per second.
·
speech recognition
In the 1990s, computer speech recognition reached a practical level for limited
purposes. Thus United Airlines has replaced its keyboard tree for flight
information by a system using speech recognition of flight numbers and city
names. It is quite convenient. On the the other hand, while it is possible to
instruct some computers using speech, most users have gone back to the keyboard
and the mouse as still more convenient.
·
Understanding natural language
Just getting a sequence of words into a computer is not enough. Parsing
sentences is not enough either. The computer has to be provided with an
understanding of the domain the text is about, and this is presently possible
only for very limited domains.
·
computer vision
The world is composed of three-dimensional objects, but the inputs to the human
eye and computers' TV cameras are two dimensional. Some useful programs can
work solely in two dimensions, but full computer vision requires partial
three-dimensional information that is not just a set of two-dimensional views.
At present there are only limited ways of representing three-dimensional
information directly, and they are not as good as what humans evidently use.
·
expert systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to
embody their knowledge in a computer program for carrying out some task. How
well this works depends on whether the intellectual mechanisms required for the
task are within the present state of AI. When this turned out not to be so,
there were many disappointing results. One of the first expert systems was
MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested
treatments. It did better than medical students or practicing doctors, provided
its limitations were observed. Namely, its ontology included bacteria,
symptoms, and treatments and did not include patients, doctors, hospitals,
death, recovery, and events occurring in time. Its interactions depended on a
single patient being considered. Since the experts consulted by the knowledge
engineers knew about patients, doctors, death, recovery, etc., it is clear that
the knowledge engineers forced what the experts told them into a predetermined
framework. In the present state of AI, this has to be true. The usefulness of
current expert systems depends on their users having common sense.
·
heuristic classification One
of the most feasible kinds of expert system given the present knowledge of AI
is to put some information in one of a fixed set of categories using several
sources of information. An example is advising whether to accept a proposed
credit card purchase. Information is available about the owner of the credit
card, his record of payment and also about the item he is buying and about the
establishment from which he is buying it (e.g., about whether there have been
previous credit card frauds at this establishment).
4.1 DSS
and Problem Solving
Unit
13
13. Fundamentals
of Expert Systems
An expert system is a computer system that
emulates, or acts in all respects, with the decision-making capabilities of a
human expert. An expert system is functionally equivalent to a human expert in
a specific problem domain of reasonable complexity. The equivalence in qualified
in terms of its capability to:
- Reason
over representations of human knowledge
- Solve
the problem by heuristic or approximation techniques
- Explain
and justify the solution based on known facts
·
Definitions of an Expert System
o A
computer system whose performance is guided by specific, expert knowledge in
solving problems.
o A
computer system that simulates the decision- making process of a human expert
in a specific domain.
o One
of the early (large- scale) successes of artificial intelligence.
o An
expert system is an “intelligent” program that solves problems in a narrow
problem area by using high-quality, specific knowledge rather than an
algorithm.
So why do we need to create an Expert System if a
human expert can solve the same problem? Well here are a few reasons :
- Low
cost of expertise
- Response
without cognitive bias or logical
fallacies
- 24/7
availability with high reliability
- Clone
the expert
- Use
in hazardous environments
- Ability to explain the reasoning without attitude.
Applications of expert systems
Applications tend to cluster into seven major
classes.
Diagnosis and Troubleshooting
of Devices and Systems of All Kinds
This
class comprises systems that deduce faults and suggest corrective actions for a
malfunctioning device or process. Medical diagnosis was one of the first
knowledge areas to which ES technology was applied (for example, see Shortliffe
1976), but diagnosis of engineered systems quickly surpassed medical diagnosis.
There are probably more diagnostic applications of ES than any other type. The
diagnostic problem can be stated in the abstract as: given the evidence
presenting itself, what is the underlying problem/reason/cause?
Planning and Scheduling
Systems
that fall into this class analyze a set of one or more potentially complex and
interacting goals in order to determine a set of actions to achieve those
goals, and/or provide a detailed temporal ordering of those actions, taking
into account personnel, materiel, and other constraints. This class has great
commercial potential, which has been recognized. Examples involve airline
scheduling of flights, personnel, and gates; manufacturing job-shop scheduling;
and manufacturing process planning.
Configuration of Manufactured
Objects from Subassemblies
Configuration,
whereby a solution to a problem is synthesized from a given set of elements
related by a set of constraints, is historically one of the most important of
expert system applications. Configuration applications were pioneered by
computer companies as a means of facilitating the manufacture of semi-custom
minicomputers (McDermott 1981). The technique has found its way into use in
many different industries, for example, modular home building, manufacturing,
and other problems involving complex engineering design and manufacturing.
Financial Decision Making
The
financial services industry has been a vigorous user of expert system
techniques. Advisory programs have been created to assist bankers in
determining whether to make loans to businesses and individuals. Insurance
companies have used expert systems to assess the risk presented by the customer
and to determine a price for the insurance. A typical application in the
financial markets is in foreign exchange trading.
Knowledge Publishing
This
is a relatively new, but also potentially explosive area. The primary function
of the expert system is to deliver knowledge that is relevant to the user's problem,
in the context of the user's problem. The two most widely distributed expert
systems in the world are in this category. The first is an advisor which
counsels a user on appropriate grammatical usage in a text. The second is a tax
advisor that accompanies a tax preparation program and advises the user on tax
strategy, tactics, and individual tax policy.
Process Monitoring and Control
Systems
falling in this class analyze real-time data from physical devices with the
goal of noticing anomalies, predicting trends, and controlling for both
optimality and failure correction. Examples of real-time systems that actively
monitor processes can be found in the steel making and oil refining industries.
Design and Manufacturing
These
systems assist in the design of physical devices and processes, ranging from
high-level conceptual design of abstract entities all the way to factory floor
configuration of manufacturing processes.
Unit 14
14.
Data, Information and Knowledge
The concept of knowledge has been discussed for
centuries and in the works of the ancient Greek philosophers, knowledge
originates with people. Plato, for instance, put forward the idea that correct
belief can be turned into knowledge by fixing it through the means of reason or
a cause. Aristotle thought that knowledge of a thing involved understanding it
in terms of the reasons for it. In Western philosophy knowledge is seen as
abstract, universal, impartial and rational. It is considered as a stand-alone artifact
(a physical record) that could be captured in technology and which will be
truthful in its essence [4]. This understanding of knowledge affected, to a
great extent, the nature of the first KM tools developed during the 90s. Most
tools and KM models during this period tried to manage knowledge as an artifact
rather than as an element deeply rooted in human understanding, human behaviour
and social interactions at work. According to research, the majority of the
first generation of KM tools failed, or at least did not fulfill their initial
aims, due to the lack of focus on human factors. Knowledge has a far more
complex nature than simple data and information and requires the active
contribution of people to manage knowledge systems. Therefore, for proper KM
implementation it is essential to clarify at an early stage, the main
differences between data, information and knowledge.
The academic community has spent years discussing and clarifying what constitutes data, information and knowledge. Variations emerge in the definitions and the basic terminology used depending on the background of the author and the specific aims he pursues.
The relationship between data, information, knowledge and wisdom form a pyramid. The pyramid has data as its base, followed in the hierarchy by information, then knowledge, with wisdom at the top. Figure 1.3 (1) below shows the relationships between data, information knowledge and wisdom.
The academic community has spent years discussing and clarifying what constitutes data, information and knowledge. Variations emerge in the definitions and the basic terminology used depending on the background of the author and the specific aims he pursues.
The relationship between data, information, knowledge and wisdom form a pyramid. The pyramid has data as its base, followed in the hierarchy by information, then knowledge, with wisdom at the top. Figure 1.3 (1) below shows the relationships between data, information knowledge and wisdom.
- Data represents unorganized and unprocessed facts.
- Usually data is static in nature.
- It can represent a set of discrete facts about events.
- Data is a prerequisite to information.
- An organization sometimes has to decide on the nature and volume of data that is required for creating the necessary information.
- Information
- Information can be considered as an aggregation of data (processed data) which makes decision making easier.
- Information has usually got some meaning and purpose.
- Knowledge
- By knowledge we mean human understanding of a subject matter that has been acquired through proper study and experience.
- Knowledge is usually based on learning, thinking, and proper understanding of the problem area.
- Knowledge is not information and information is not data.
- Knowledge is derived from information in the same way information is derived from data.
- We can view it as an understanding of information based on its perceived importance or relevance to a problem area.
- It can be considered as the integration of human perceptive processes that helps them to draw meaningful conclusions.
Unit 15
15.
Knowledge Representation
15.1Production rules
•
A production system is a
model of cognitive processing, consisting of a collection of rules(called
production rules, or just productions).
Each rule has two parts: a condition part and an action part.
•
The meaning of the rule is
that when the condition holds true, then the action is taken.
•
Rule1: if temperature < 20°C →
turn-on heating.
•
Rule2: if temperature > 20°C → turn-off
heating.
When the room temperature is
below 20°C, the condition part of Rule1 is true, so the thermostat takes the
action specified by the rule and turns on the heating. (The rule is said to fire
•
A Production Rule System emulates human
reasoning using a set of ‘productions’
•
Productions have two parts
–
Sensory precondition (“IF” part)
–
Action (“THEN” part)
•
When the state of the ‘world’ matches the IF
part, the production is fired, meaning the action is executed
–
The ‘world’ is the set of data values in the
system’s working memory
–
For a clinical expert systems, this is
usually data about a patient, which, ideally, has come from (and may go back
to) an electronic medical record, or it may be entered interactively (or
usually a little of each)
•
So production rules link facts (“IF”
parts, also called antecedents) to conclusions (“THEN” parts,
also called consequents)
A production system consists of:
–
Working memory (facts
memory)
–
Production rules memory
–
Inference engine, it cycles
through three steps:
•
match facts against rules
•
select a rule
• execute the rule
15.1Semantic networks
Semantic networks are an alternative to predicate logic as a form of
knowledge representation. The idea is that we can store our knowledge in the
form of a graph, with nodes representing objects in the world, and arcs
representing relationships between those objects. The major idea is that:
· The meaning of a concept comes from its relationship to other concepts, and that,
· The information is stored by interconnecting nodes with labeled arcs.
is intended to represent the data:
Tom is a cat.
Tom caught a bird.
Tom is owned by John.
Tom is ginger in colour.
Cats like cream.
The cat sat on the mat.
A cat is a mammal.
A bird is an animal.
All mammals are animals.
Mammals have fur.
It is argued that this form of representation is closer to the way humans structure knowledge by building mental links between things than the predicate logic we considered earlier. Note in particular how all the information about a particular object is concentrated on the node representing that object, rather than scattered around several clauses in logic. There is, however, some confusion here which stems from the imprecise nature of semantic nets. A particular problem is that we haven’t distinguished between nodes representing classes of things, and nodes representing individual objects. So, for example, the node labeled Cat represents both the single (nameless) cat who sat on the mat, and the whole class of cats to which Tom belongs, which are mammals and which like cream. The is_a link has two different meanings – it can mean that one object is an individual item from a class, for example Tom is a member of the class of cats, or that one class is a subset of another, for example, the class of cats is a subset of the class of mammals. This confusion does not occur in logic, where the use of quantifiers, names and predicates makes it clear what we mean so:
Tom is a cat is represented by Cat(Tom)
The cat sat on the mat is represented by $
A cat is a mammal is represented by
We can clean up the representation by distinguishing between nodes representing individual or instances, and nodes representing classes. The is_a link will only be used to show an individual belonging to a class. The link representing one class being a subset of another will be labeled a_kind_of, or ako for short. The names instance and subclass are often used in the place of is_a and ako, but we will use these terms with a slightly different meaning in the section on Frames below.
Note also the modification which causes the link labeled is_owned_by to be reversed in direction. This is in order to avoid links representing passive relationships. In general a passive sentence can be replaced by an active one, so “Tom is owned by John” becomes “John owns Tom”. In general the rule which converts passive to active in English converts sentences of the form “X is Yed by Z” to “Z Ys X”. This is just an example (though often used for illustration) of the much more general principle of looking beyond the immediate surface structure of a sentence to find its deep structure.
The revised semantic net is:
Note that where we had an unnamed member of some class, we have had to introduce a node with an invented name to represent a particular member of the class. This is a process similar to the Skolemisation we considered previously as a way of dealing with existential quantifiers. For example, “Tom caught a bird” would be represented in logic by x (bird(x)
A direct Prolog representation can be used, with classes represented by predicates, thus:
cat(tom).
cat(cat1).
mat(mat1).
sat_on(cat1,mat1).
bird(bird1).
caught(tom,bird1).
like(X,cream) :– cat(X).
mammal(X) :– cat(X).
has(X,fur) :– mammal(X).
animal(X) :– mammal(X).
animal(X) :– bird(X).
owns(john,tom).
is_coloured(tom,ginger).
So, in general, an is_a link between a class c and an individual m is represented by the fact c(m). An a_kind_of link between a subclass c and a superclass s is represented by s(X) :- c(X). If a property p with further arguments a1, … ,an is held by all members of a class c, it is represented by p(X,a1,…,an) :- c(X). If a property p with further arguments a1, … ,an is specified as held by an individual m, rather than a class to which m belongs, it is represented by p(m,a1,…,an).
These values can also be represented in logic as: isa(person, mammal), instance(Mike-Hall, person) team(Mike-Hall, Cardiff)
Inheritance
This Prolog equivalent captures an important property of semantic nets, that they may be used for a form of inference known as inheritance. The idea of this is that if an object belongs to a class (indicated by an is_a link) it inherits all the properties of that class. So, for example as we have a likes link between cats and cream, meaning “all cats like cream”, we can infer that any object which has an is_a link to cats will like cream. So both Tom and Cat1 like cream. However, the is_coloured link is between Tom and ginger, not between cats and ginger, indicating that being ginger is a property of Tom as an individual, and not of all cats. We cannot say that Cat1 is ginger, for example; if we wanted to we would have to put another is_coloured link between Cat1 and ginger.
Figure 6.5: Semantic Networks showing Inheritance
Inheritance also provides a means of dealing with default reasoning, e.g. we could represent:
· Emu are birds.
· Typically birds fly and have wings.
· Emus run.
15.1Logic statements
Logic has a profound impact on computer-science.
Some examples:
•
Propositional
logic – the foundation of computers and circuitry
•
Databases –
query languages
•
Programming
languages (e.g. prolog)
•
Design
Validation and verification
•
AI (e.g.
inference systems)
•
Propositional
Logic
•
First
Order Logic
•
Higher Order
Logic
•
Temporal Logic
•
A logic
consists of syntax and semantics
•
Syntax defines
well formed sentences (Infix) Arithmetic
n x+y=4
n x4y+=
•
Semantics
defines "meaning" of sentences
•
In logic,
defines the truth of each sentence with respect to each possible
world
•
Possible World
1: x=1, y=3
•
Possible World
2: x=2, y=1
n Sentences in Propositional Logic are defined in
Backus-Naur Form (BNF):
n A variable
symbol (P,Q,R,…), and the constants True, False are correct sentences.
n Connectives: (“Sentence-Forming Operators”)
n Ù conjunction , and
n Ú disjunction ,
or
n Ø negation "not", "it is not the
case that"
n ® implication,
n « equivalent to or bioconditional
Unit 16
15.
Inference,
Explanations, and Uncertainty
An
inference engine for rule based expert systems which forms part of the EXPRES
system is developed and presented. It is
shown to be universal, correct, and optimal with respect to time. In addition,
a VLSI implementation of the system is proposed which allows automatic design
of universal as well as special purpose expert systems on a chip.
Expert
systems have become a popular method for representing large bodies of
knowledge
for a given field of expertise and solving problems by use of this
knowledge.
An expert system often consists of three parts, namely: a knowledge
base,
an inference engine, and a user interface
A
dialogue is conducted by the user interface between the user and the system.
The
user provides information about the problem to be solved and the system
then
attempts to provide insights derived (or inferred) from the knowledge
base.
These insights are provided by the
inference engine after examining the
knowledge
base. This interaction is illustrated by
the picture in figure 1.
When
rules are examined by the inference engine, actions are executed if the
information
supplied by the user satisfies the conditions in the rules. Two
methods
of inference often are used, forward and backward chaining. Forward
chaining
is a top-down method which takes facts as they become available and
attempts
to draw conclusions (from satisfied conditions in rules) which lead to
actions
being executed. Backward chaining is the
reverse. It is a bottom-up
procedure
which starts with goals (or actions) and queries the user about
information
which may satisfy the conditions contained in the rules. It is a
verification
process rather than an exploration process.
An example of
backward
chaining is MYCIN [vMS81], and an example of forward chaining is
Expert
[WK81]. A system which uses both is
Prospector [DGH79].
if temperature > 100 and complexion = pale then
cost := cost + 35, print("Patient has the
flu."),
call Specialist, halt;
This
has been a very brief introduction to the
EXPRES system and the
formulation
of rule-based expert systems which can be designed using the
system. There are more options which will are
described elsewhere [Gr87], but
this
should be an adequate amount of material for the presentation of the
inference
engine in the next section.
¢ The inference engine is a computer program designed
to produce a reasoning on rules.
¢ it is the "brain" that expert
systems use to reason about the information in the knowledge base for the
ultimate purpose of formulating new conclusions.
Inference engines are considered to be a special case of reasoning
engines, which can use more general methods of reasoning
¢ In order to produce a reasoning, it is based on logic.
¢ There are several kinds of logic: propositional
logic, predicates of order 1 or more, epistemic
logic, modal logic, temporal logic, fuzzy logic, etc.
¢ Propositional logic is the basic human logic, that is expressed in
syllogisms.
¢ The expert system that uses propositional logic is also called
a zeroth-order expert system.
¢ The inference engine can be described as a form of finite state
machine with a cycle consisting of three action states: match
rules, select rules, and execute rules.
¢ The Forward chaining and Backward
chaining are two techniques often
used by Inference engine for drawing
inferences from the
knowledge base.
¢ Forward Chaining
¢ It is reasoning from facts to the conclusion which lead to actions being
executed.
¢ Forward chaining is a top-down method.
¢ Backward
chaining
¢ It starts with goals (or actions) and queries the user about
information which may satisfy the conditions contained in the rules.
It is a
bottom-up procedure.
Forward
Chaining And Backward Chaining Inference Techniques
Inference
engines all match production rules (from the rule base) with facts (from the
database). Two basic approaches are used
to choose the order in which they are matched: forward chaining and backward
chaining. Inference engines can use
either or both. When both are used,
backward chaining can be used initially, with forward chaining used as facts are
added to the database.
Forward
chaining
An inference technique that
starts with the known data and works forward, matching the facts from the
database with production rules from the rule base until no further rules can be
fired.
Forward chaining is also
referred to as data-driven reasoning.
Rules
|
Database
|
|
Initial
|
A
B C D E
|
|
Pass 1
|
3)
A → X
4) C → L |
|
A
B C D E X L
|
||
Pass 2
|
2)
X & B & E → Y
|
|
A
B C D E X L Y
|
||
Pass 3
|
1)
Y & D → Z
|
|
A
B C D E X L Y Z
|
||
Pass 4
|
||
A
B C D E X L Y Z
|
Backward
chaining
An inference technique that
starts with a hypothetical solution (the goal) and works backward, matching
production rules from the rule base with facts from the database until the goal
is either verified or proven wrong.
·
Backward
chaining is also referred to as goal-driven reasoning
- Backward
chaining is also referred to as goal-driven reasoning.
Rules
|
Database
|
|
Initial
|
A
B C D E
|
|
Pass 1
|
1)
Y & D → Z ?
|
|
A
B C D E
|
||
Pass 2
|
2)
X & B & E → Y ?
|
|
A
B C D E
|
||
Pass 3
|
3)
A → X
|
|
A
B C D E X
|
||
Pass 2
|
2)
X & B & E → Y !
|
|
A
B C D E X Y
|
||
Pass 1
|
1)
Y & D → Z !
|
|
A
B C D E X Y Z
|
conflict
resolution
A method for chosing which
production rule to fire when more than one rule can be fired in a given cycle.
Rule 1 IF
the traffic light is green
THEN the action is go
Rule 2 IF
the traffic light is red
THEN the action is stop
Rule 3 IF
the traffic light is red
THEN the action is go
Bayes Theorem (aka, Bayes Rule)
Bayes'
theorem (also known as Bayes' rule) is a useful tool for calculating conditional probabilities.
Bayes' theorem can be stated as follows:
Bayes' theorem. Let
A1, A2, ... , An be a set of mutually
exclusive events that together form the sample space S. Let B be any event from
the same sample space, such that P(B) > 0. Then,
P(
Ak | B ) =
|
P( Ak∩ B )
P( A1 ∩ B ) + P( A2 ∩ B
) + . . . + P( An ∩ B )
|
Note: Invoking the fact that P( Ak ∩ B ) = P( Ak )P( B | Ak ), Baye's theorem can also be expressed as
P(
Ak | B ) =
|
P( Ak ) P( B | Ak )
P( A1 ) P( B | A1 ) + P( A2 ) P(
B | A2 ) + . . . + P( An ) P( B | An )
|
Unless
you are a world-class statiscian, Bayes' theorem (as expressed above) can be
intimidating. However, it really is easy to use. The remainder of this lesson
covers material that can help you understand when and how to apply Bayes'
theorem effectively.
Part
of the challenge in applying Bayes' theorem involves recognizing the types of
problems that warrant its use. You should consider Bayes' theorem when the
following conditions exist.
- The sample space is
partitioned into a set of mutually exclusive events {
A1, A2, . . . , An }.
- Within the sample space, there exists
an event B, for which P(B) > 0.
- The analytical goal is to compute
a conditional probability of the form: P( Ak | B ).
- You know at least one of the two
sets of probabilities described below.
- P( Ak ∩ B ) for
each Ak
- P( Ak ) and P( B | Ak
) for each Ak
Bayes' theorem describes the relationships that exist
within an array of simple and conditional probabilities. For example: Suppose
there is a certain disease randomly found in one-half of one percent (.005) of
the general population. A certain clinical blood test is 99 percent (.99)
effective in detecting the presence of this disease; that is, it will yield an
accurate positive result in 99 percent of the cases where the disease is
actually present. But it also yields false-positive results in 5 percent (.05)
of the cases where the disease is not present. The following table shows (in red) the probabilities that are stipulated in the
example and (in blue) the probabilities that
can be inferred from the stipulated information:
P(A) = .005
|
the probability that the disease will be present in any particular person
|
P(~A) = 1—.005 = .995
|
the probability that the disease will not be present in any particular
person
|
P(B|A) = .99
|
the probability that the test will yield a positive result [B] if
the disease is present [A]
|
P(~B|A) = 1—.99 = .01
|
the probability that the test will yield a negative result [~B] if
the disease is present [A]
|
P(B|~A) = .05
|
the probability that the test will yield a positive result [B] if
the disease is not present [~A]
|
P(~B|~A) = 1—.05 = .95
|
the probability that the test will yield a negative result [~B] if
the disease is not present [~A]
|
Given this information, Bayes' theorem allows for the derivation of the two simple probabilities
P(B) = [P(B|A) x P(A)] + [P(B|~A) x P(~A)]
= [.99 x .005]+[.05 x .995] = .0547 |
the probability of a positive test result [B], irrespective of whether
the disease is present [A] or not present [~A]
|
P(~B) = [P(~B|A) x P(A)] + [P(~B|~A) x P(~A)]
= [.01 x .005]+[.95 x .995] = .9453 |
the probability of a negative test result [~B], irrespective of whether
the disease is present [A] or not present [~A]
|
which in turn allows for the
calculation of the four remaining conditional probabilities
P(A|B) = [P(B|A) x P(A)] / P(B)
= [.99 x .005] / .0547 = .0905 |
the probability that the disease is present [A] if the test result
is positive [B] (i.e., the probability that a positive test result will be a
true positive)
|
P(~A|B) = [P(B|~A) x P(~A)] / P(B)
= [.05 x .995] / .0547 = .9095 |
the probability that the disease is not present [~A] if the test
result is positive [B] (i.e., the probability that a positive test result
will be a false positive)
|
P(~A|~B) = [P(~B|~A) x P(~A)] / P(~B)
= [.95 x .995] / .9453 = .99995 |
the probability that the disease is absent [~A] if the test result
is negative [~B] (i.e., the probability that a negative test result will be a
true negative)
|
P(A|~B) = [P(~B|A) x P(A)] / P(~B)
= [.01 x .005] / .9453 = .00005 |
the probability that the disease is present [A] if the test result
is negative [~B] (i.e., the probability that a negative test result will be a
false negative)
|
To perform calculations using Bayes' theorem, enter
the probability for one or the other of the items in each of the following
pairs (the remaining item in each pair will be calculated automatically).
A probability value can be entered as either a decimal fraction such
as .25 or a common fraction such as 1/4. Whenever possible, it is
better to enter the common fraction rather than a rounded decimal fraction:
1/3 rather than .3333; 1/6 rather than .1667; and so forth.
P(A) or P(~A)
P(B|~A) or P(~B|~A) P(B|A) or P(~B|A) |
Sample Problem
Bayes'
theorem can be best understood through an example. This section presents an
example that demonstrates how Bayes' theorem can be applied effectively to
solve statistical problems.
Example
1
Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. What is the probability that it will rain on the day of Marie's wedding?
Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. What is the probability that it will rain on the day of Marie's wedding?
Solution:
The sample space is defined by two mutually-exclusive events - it rains or it
does not rain. Additionally, a third event occurs when the weatherman predicts
rain. Notation for these events appears below.
- Event A1. It rains on
Marie's wedding.
- Event A2. It does not
rain on Marie's wedding.
- Event B. The weatherman predicts
rain.
In terms of
probabilities, we know the following:
- P( A1 ) = 5/365 =0.0136985
[It rains 5 days out of the year.]
- P( A2 ) = 360/365 =
0.9863014 [It does not rain 360 days out of the year.]
- P( B | A1 ) = 0.9
[When it rains, the weatherman predicts rain 90% of the time.]
- P( B | A2 ) = 0.1
[When it does not rain, the weatherman predicts rain 10% of the time.]
We
want to know P( A1 | B ), the probability it will rain on the day of
Marie's wedding, given a forecast for rain by the weatherman. The answer can be
determined from Bayes' theorem, as shown below.
P(
A1 | B ) =
|
P( A1 ) P( B | A1 )
P( A1 ) P( B | A1 ) + P( A2 )
P( B | A2 )
|
P(
A1 | B ) =
|
(0.014)(0.9) / [ (0.014)(0.9) + (0.986)(0.1) ]
|
P(
A1 | B ) =
|
0.111
|
Note
the somewhat unintuitive result. Even when the weatherman predicts rain, it
only rains only about 11% of the time. Despite the weatherman's gloomy
prediction, there is a good chance that Marie will not get rained on at her
wedding.
Unit- 18
18. Introduction of Artificial Neural Network
Artificial neural network (ANN), usually called neural network (NN), is a
system of mathematical model or computational model that is inspired by the
structure and/or functional aspects of biological neural networks. There are
many types of artificial neural networks (ANN).
Each artificial neural network is a computational simulation of a biological
neural network model. Artificial neural network models mimic the real life
behavior of neurons and the electrical messages they produce between input
(such as from the eyes or nerve endings in the hand), processing by the brain
and the final output from the brain (such as reacting to light or from sensing
touch or heat). There are other ANNs which are adaptive systems used to model
things such as environments and population. Artificial neural network systems
can be hardware and software based specifically built systems or purely
software based and run in computer models.
Types of Artificial Neural Networks (ANN)
- Feed-forward Neural Network – The feed-forward neural network was the first and arguably most simple type of artificial neural network devised. In this network the information moves in only one direction — forwards: From the input nodes data goes through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.
- Radial Basis Function (RBF) Neural Network – Radial basis functions are powerful techniques for interpolation in multidimensional space. A RBF is a function which has built into a distance criterion with respect to a center. RBF neural networks have the advantage of not suffering from local minima in the same way as Multi-Layer Perceptrons. RBF neural networks have the disadvantage of requiring goo
- Kohonen Self-organizing Neural Network – The self-organizing map (SOM) performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these.
- Learning Vector Quantization Neural Network – Learning Vector Quantization (LVQ) can also be interpreted as a neural network architecture. In LVQ, prototypical representatives of the classes parameterize, together with an appropriate distance measure, a distance-based classification scheme.
- Recurrent Neural Networks – Recurrent neural networks (RNNs) are models with bi-directional data flow. Recurrent neural networks can be used as general sequence processors. Various types of Recurrent neural networks are Fully recurrent network (Hopfield network and Boltzmann machine), Simple recurrent networks, Echo state network, Long short term memory network, Bi-directional RNN, Hierarchical RNN, and Stochastic neural networks.
- Modular Neural Network – Biological studies have shown that the human brain functions not as a single massive network, but as a collection of small networks. This realization gave birth to the concept of modular neural networks, in which several small networks cooperate or compete to solve problems.
- Physical Neural Network – A physical neural network includes electrically adjustable resistance material to simulate artificial synapses.
- Other Special Types of Neural Networks
- Holographic associative memory – Holographic associative memory represents a family of analog, correlation-based, associative, stimulus-response memories, where information is mapped onto the phase orientation of complex numbers operating.
- Instantaneously Trained Neural Networks – Instantaneously trained neural networks (ITNNs) were inspired by the phenomenon of short-term learning that seems to occur instantaneously.
- Spiking Neural Networks – Spiking neural networks (SNNs) are models which explicitly take into account the timing of inputs. The network input and output are usually represented as series of spikes (delta function or more complex shapes). SNNs have an advantage of being able to process information in the time domain (signals that vary over time).
- Dynamic Neural Networks – Dynamic neural networks not only deal with nonlinear multivariate behaviour, but also include (learning of) time-dependent behaviour such as various transient phenomena and delay effects.
- Cascading Neural Networks – Cascade Correlation is an architecture and supervised learning algorithm. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure.
- Neuro-Fuzzy Neural Networks – A neuro-fuzzy network is a fuzzy inference system in the body of an artificial neural network. Depending on the FIS type, there are several layers that simulate the processes involved in a fuzzy inference like fuzzification, inference, aggregation and defuzzification. Embedding an FIS in a general structure of an ANN has the benefit of using available ANN training methods to find the parameters of a fuzzy system.
- Compositional Pattern-producing Neural Networks – Compositional pattern-producing networks (CPPNs) are a variation of ANNs which differ in their set of activation functions and how they are applied. While typical ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs can include both types of functions and many others.
Unit 19
19.1 Neural Network Applications
Applications
of Neural Network
Character Recognition
- The idea of character recognition has become very important as handheld
devices like the Palm Pilot are becoming increasingly popular. Neural networks
can be used to recognize handwritten characters.
The idea of using feedforward
networks to recognize handwritten characters is rather straightforward. As
in most supervised training, the bitmap pattern of the handwritten character is
treated as an input, with the correct letter or digit as the desired output.
Normally such programs require the user to train the network by providing the
program with their handwritten patterns.
Image Compression - Neural networks can receive and process vast amounts of information at once, making them useful in image compression. With the Internet explosion and more sites using more images on their sites, using neural networks for image compression is worth a look.
Image Compression - Neural networks can receive and process vast amounts of information at once, making them useful in image compression. With the Internet explosion and more sites using more images on their sites, using neural networks for image compression is worth a look.
Because neural networks can accept a vast array of input at
once, and process it quickly, they are useful in image compression.
Stock
Market Prediction - The day-to-day business of the stock market
is extremely complicated. Many factors weigh in whether a given stock will go
up or down on any given day. Since neural networks can examine a lot of
information quickly and sort it all out, they can be used to predict stock
prices. Neural networks have been touted as all-powerful tools in stock-market
prediction. Companies such as MJ
Futures claim amazing 199.2% returns over a 2-year period using
their neural network prediction methods. They also claim great ease of use; as
technical editor John Sweeney said in a 1995 issue of "Technical Analysis
of Stocks and Commodities," "you can skip developing complex rules
(and redeveloping them as their effectiveness fades) . . . just define the
price series and indicators you want to use, and the neural network does the
rest."
These may be exaggerated claims, and, indeed, neural networks may be easy to
use once the network is set up, but the setup and training of the network
requires skill, experience, and patience. It's not all hype, though; neural
networks have shown success at prediction of market trends.The idea of stock market prediction is not new, of course. Business people often attempt to anticipate the market by interpreting external parameters, such as economic indicators, public opinion, and current political climate. The question is, though, if neural networks can discover trends in data that humans might not notice, and successfully use these trends in their predictions.
This is a simple back-propagation network of three layers, and it is trained and tested on a high volume of historical market data. The challenge here is not in the network architecture itself, but instead in the choice of variables and the information used for training. I could not find the accuracy rates for this network, but my source claimed it achieved "remarkable success" (this source was a textbook, not a NN-prediction-selling website!).
Even better results have been achieved with a back-propagated neural network with 2 hidden layers and many more than 6 variables. I have not been able to find more details on these network architectures, however; the companies that work with them seem to want to keep their details secret.
Traveling Saleman's Problem - Interestingly enough, neural networks can solve the traveling salesman problem, but only to a certain degree of approximation.
Recall the elastic rubber-band model mentioned above, such a ring shaped map simulates a rubber-band if we consider the weight vectors as points on a plane. We can join these points together according to the position of their respective perceptron in the ring of the top layer of the network.
Suppose the coordinates of a city (x, y) is presented as the input vector of the network, the network will identify the weight vector closest to the city and move it and its neighbors closer to the city. In this manner, the weight vectors behave as points on a rubber band and each "learning" is analogous to pulling the closest point of the band towards a city. The rule that the amount of learning varies inversely with the physical distance between the node and the winner is what leads to the elastic property of the rubber band.
Medicine, Electronic Nose, Security, and Loan Applications - These are some applications that are in their proof-of-concept stage, with the acception of a neural network that will decide whether or not to grant a loan, something that has already been used more successfully than many humans.
Medicine
One of the areas that has gained attention is in cardiopulmonary diagnostics. The ways neural networks work in this area or other areas of medical diagnosis is by the comparison of many different models. A patient may have regular checkups in a particular area, increasing the possibility of detecting a disease or dysfunction.
One of the areas that has gained attention is in cardiopulmonary diagnostics. The ways neural networks work in this area or other areas of medical diagnosis is by the comparison of many different models. A patient may have regular checkups in a particular area, increasing the possibility of detecting a disease or dysfunction.
The data may
include heart rate, blood pressure, breathing rate, etc. to different models.
The models may include variations for age, sex, and level of physical activity.
Each individual's physiological data is compared to previous physiological data
and/or data of the various generic models. The deviations from the norm are
compared to the known causes of deviations for each medical condition. The
neural network can learn by studying the different conditions and models,
merging them to form a complete conceptual picture, and then diagnose a
patient's condition based upon the models.
Electronic
Noses
An actual electronic "nose"
Image courtesy Pacific Northwest Laboratory |
The idea of a chemical
nose may seem a bit absurd, but it has several real-world applications. The
electronic nose is composed of a chemical sensing system (such as a
spectrometer) and an artificial neural network, which recognizes certain
patterns of chemicals. An odor is passed over the chemical sensor array, these
chemicals are then translated into a format that the computer can understand,
and the artificial neural network identifies the chemical.
A list at the
Pacific Northwest Laboratory has several different applications in the
environment, medical, and food industries.
Environment:
identification of toxic wastes, analysis of fuel mixtures (7-11 example),
detection of oil leaks, identification of household odors, monitoring air
quality, monitoring factory emission, and testing ground water for odors.
Medical: The
idea of using these in the medical field is to examine odors from the body to
identify and diagnose problems. Odors in the breath, infected wounds, and body
fluids all can indicate problems. Artificial neural networks have even been
used to detect tuberculosis.
Food: The food
industry is perhaps the biggest practical market for electronic noses,
assisting or replacing entirely humans. Inspection of food, grading quality of
food, fish inspection, fermentation control, checking mayonnaise for rancidity,
automated flavor control, monitoring cheese ripening, verifying if orange juice
is natural, beverage container inspection, and grading whiskey.
Security
One program that has already been started is the CATCH program. CATCH, an acronymn for Computer Aided Tracking and Characterization of Homicides. It learns about an existing crime, the location of the crime, and the particular characteristics of the offense. The program is subdivided into different tools, each of which place an emphasis on a certain characteristic or group of characteristics. This allows the user to remove certain characteristics which humans determine are unrelated.
One program that has already been started is the CATCH program. CATCH, an acronymn for Computer Aided Tracking and Characterization of Homicides. It learns about an existing crime, the location of the crime, and the particular characteristics of the offense. The program is subdivided into different tools, each of which place an emphasis on a certain characteristic or group of characteristics. This allows the user to remove certain characteristics which humans determine are unrelated.
Loans and
credit cards
Loan granting is one area in which neural networks can aid humans, as it is an area not based on a predetermined and preweighted criteria, but answers are instead nebulous. Banks want to make as much money as they can, and one way to do this is to lower the failure rate by using neural networks to decide whether the bank should approve the loan. Neural networks are particularly useful in this area since no process will guarantee 100% accuracy. Even an 85-90% accuracy would be an improvement over the methods humans use.
Loan granting is one area in which neural networks can aid humans, as it is an area not based on a predetermined and preweighted criteria, but answers are instead nebulous. Banks want to make as much money as they can, and one way to do this is to lower the failure rate by using neural networks to decide whether the bank should approve the loan. Neural networks are particularly useful in this area since no process will guarantee 100% accuracy. Even an 85-90% accuracy would be an improvement over the methods humans use.
In fact, in
some banks, the failure rate of loans approved using neural networks is lower
than that of some of their best traditional methods. Some credit card companies
are now beginning to use neural networks in deciding whether to grant an
application.
The process
works by analyzing past failures and making current decisions based upon past
experience. Nonetheless, this creates its own problems. For example, the bank
or credit company must justify their decision to the applicant. The reason
"my neural network computer recommended against it" simply isn't
enough for people to accept. The process of explaining how the network learned
and on what characteristics the neural network made its decision is difficult.
As we alluded to earlier in the history of neural networks, self-modifying code
is very difficult to debug and thus difficult to trace. Recording the steps it
went through isn't enough, as it might be using conventional computing, because
even the individual steps the neural network went through have to be analyzed
by human beings, or possibly the network itself, to determine that a particular
piece of data was crucial in the decision-making process.
Miscellaneous Applications - These are some very interesting (albeit at times a little absurd) applications of neural networks.
Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
But to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multimeaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition.
6.2 Neural networks in medicine
Artificial Neural Networks (ANN) are currently a 'hot' research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognising diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).Neural networks are ideal in recognising diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognise the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the 'quantity'. The examples need to be selected very carefully if the system is to perform reliably and efficiently.
6.2.1 Modelling and Diagnosing the Cardiovascular System
Neural Networks are used experimentally to model the human cardiovascular system. Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient. If this routine is carried out regularly, potential harmful medical conditions can be detected at an early stage and thus make the process of combating the disease much easier.A model of an individual's cardiovascular system must mimic the relationship among physiological variables (i.e., heart rate, systolic and diastolic blood pressures, and breathing rate) at different physical activity levels. If a model is adapted to an individual, then it becomes a model of the physical condition of that individual. The simulator will have to be able to adapt to the features of any individual without the supervision of an expert. This calls for a neural network.
Another reason that justifies the use of ANN technology, is the ability of ANNs to provide sensor fusion which is the combining of values from several different sensors. Sensor fusion enables the ANNs to learn complex relationships among the individual sensor values, which would otherwise be lost if the values were individually analysed. In medical modelling and diagnosis, this implies that even though each sensor in a set may be sensitive only to a specific physiological variable, ANNs are capable of detecting complex medical conditions by fusing the data from the individual biomedical sensors.
6.2.2 Electronic noses
ANNs are used experimentally to implement electronic noses. Electronic noses have several potential applications in telemedicine. Telemedicine is the practice of medicine over long distances via a communication link. The electronic nose would identify odours in the remote surgical environment. These identified odours would then be electronically transmitted to another site where an door generation system would recreate them. Because the sense of smell can be an important sense to the surgeon, telesmell would enhance telepresent surgery.For more information on telemedicine and telepresent surgery click here.
6.2.3 Instant Physician
An application developed in the mid-1980s called the "instant physician" trained an autoassociative memory neural network to store a large number of medical records, each of which includes information on symptoms, diagnosis, and treatment for a particular case. After training, the net can be presented with input consisting of a set of symptoms; it will then find the full stored pattern that represents the "best" diagnosis and treatment.6.3 Neural Networks in business
Business is a diverted field with several general areas of specialisation such as accounting or financial analysis. Almost any neural network application would fit into one business area or financial analysis.There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Most of the funded work in this area is classified as proprietary. Thus, it is not possible to report on the full extent of the work going on. Most work is applying neural networks, such as the Hopfield-Tank network for optimization and scheduling.
6.3.1 Marketing
There is a marketing application which has been integrated with a neural network system. The Airline Marketing Tactician (a trademark abbreviated as AMT) is a computer system made of various intelligent technologies including expert systems. A feedforward neural network is integrated with the AMT and was trained using back-propagation to assist the marketing control of airline seat allocations. The adaptive neural approach was amenable to rule expression. Additionaly, the application's environment changed rapidly and constantly, which required a continuously adaptive solution. The system is used to monitor and recommend booking advice for each departure. Such information has a direct impact on the profitability of an airline and can provide a technological advantage for users of the system. [Hutchison & Stephens, 1987]While it is significant that neural networks have been applied to this problem, it is also important to see that this intelligent technology can be integrated with expert systems and other approaches to make a functional system. Neural networks were used to discover the influence of undefined interactions by the various variables. While these interactions were not defined, they were used by the neural system to develop useful conclusions. It is also noteworthy to see that neural networks can influence the bottom line.