Architecture And Process

Architecture And Process
Two common ones are two tier and three tier architectures.
Data from internal sources and external sources are extracted,
warehouse.
In two tier architecture, there is no multidimensional database or server.
Components of data warehousing
Large physical
database
This is an actual physical database into which all the data for
the data warehouse are gathered, along with the metadata
and the processing logic used to scrub, organize, package &
preprocess the data for end user access.
The logical data
warehouse
This contains all the metadata, business rules, and
processing logic required to scrub, organize, package &
preprocess the data.
It contains the information required to find and access the
actual data, wherever they actually reside.
Data mart
A data mart is a subset of the enterprise wide data
warehouse.
It performs the role of a departmental, regional or functional
data warehouse.
Decision support
systems and an
executive information
system
These are not data warehouses but application that uses the
data warehouse.
Suitability and Characteristics of data warehousing

Online Analytical Data Processing : Data Access and mining querying
and analysis
The latest development in this area is client server architecture.
The term OLAP refers to DSS & EIS computing done by end users in
online system.
In OLTP voluminous data are processed as soon as they are entered.
The OLAP is performed by the end users, where as the OLTP is done by
the IS professionals.
OLAP also enable to generate queries, requesting Adhoc reports,
conducting statistical analysis and building applications.
SQL is a non procedural and very user friendly language.

Data Mining
This refers to the process of knowledge discovery in databases,
knowledge extraction, data archeology etc.
All these are conducted automatically and allow quick discovery even by
the novice users.
Main objectives
 Competitive Advantages
1) Marketing: Predicting which customer will respond to a mailing
or buy a particular product. Helps to classify the customer.
2) Banking : Forecasting levels of bad loans, credit card usage
3) Retailing and Sales 8) Government & defense
4) Manufacturing & production 9) Airlines
5) Brokerage & securities trading 10) Health care
6) Insurance 11) Broadcasting
7) Computer hardware &
software
 Data Visualization
Data visualization refers to technologies that support visualization of
information.
It includes digital images, Geographic information systems, GUI,
multidimensional, tables and graphs, virtual reality and animation etc.
Data visualization is easy to implement when the necessary data are in a
data warehouse.

Intelligent Database and Data mining
AI technologies, especially expert systems (ES) and artificial neural
networks (ANN) can make the access and manipulation of complex
databases simpler.
The main types of tools used in intelligent data mining
Case based
reasoning
Using historical cases, this approach can be used to
recognize patterns
Neural computing It is a machine learning approach by which historical data
can be examined for pattern recognition.
This can be used to identify potential customers of a new
product, financial services and also in manufacturing.
Intelligent agents This is one of the promising issues in retrieving information
from the databases especially external ones.

The Methodology of Simulation

The Methodology of Simulation
Simulation involves setting up a model of a real system and conducting
repetitive experiments on it.
1) Problem
definition
The real world problem is examined & classified.
2) Construction of
the simulation
model
This step involves the determination of the variables & their
relationships & the gathering of necessary data.
3) Testing and
validating the
model
The simulation model must properly represent the system
under study.
Testing and validation ensure this.
4) Design of the
experiments
Once the model has been proven valid, and experiment is
designed.
There are two important & conflicting objectives :
Accuracy & Cost
Best case and worst case scenarios
5) Conducting the
experiments
This involves issues ranging from random number generation
to presentation to presentation of the results.
6) Evaluating the
results
We determine the meaning of the results. In addition to
statistical tools, we may use sensitivity analyses.
7) Implementation The chances of implementation are better because manager
is usually more involved in the simulation process than with
other models.

Types of Simulation
1) Probabilistic
Simulation
One or more of the independent variables (Such as the
demand in an inventory problem) are probabilistic.
2) Discrete
Distributions
Involve a situation with a limited number of events (or
variables) than can take on only a finite number of values.
3) Continuous
Distributions
These are situations with unlimited numbers of possible
events that follow density functions such as the normal
distribution.
Probabilistic simulation is conducted with the aid of a
technique called Monte Carlo.
4) Time dependent
versus time
independent
simulation
Time independent refers to a situation in which it is not
important to know exactly when the event occurred.
5) Simulation
software
These include spreadsheet ad-ins.
6) Visual simulation The graphic display of computerized results, which may
include animation.
7) Object oriented
simulation
Some recent advances in the area of developing simulation
models using the object oriented approach.

Multidimensional Modeling
The original spreadsheets were two dimensional.
Later, with the introduction of windows, spreadsheet packages introduced
what thy called a 3-D approach.
Multidimensional modeling tools provide the solution.
A typical multidimensional tool such as CA-Masterpiece/200.
It has data manipulation and drags and drop capabilities through which
users can change the shape of the spreadsheets.
Financial and Planning Modeling
Definition and background of planning modeling
Financial planning models may have a very short planning horizon and
entail no more than a collection of accounting formulas for producing
proforma statements.
On the other hand, corporate planning models often include complex
quantitative and logical relationships amount a corporation’s financial,
marketing and production activities.
Educom’s financial planning model (DFPM)


Ready made Quantitative software Package
Some DSS tools offer several built in subroutines for constructing
quantitative models in areas such as statistics, financial analysis,
accounting, and management science.
These models can be called up by one command such as SQRT.
Many DSS tools can easily interface with powerful standard quantities
stand alone software package.


Data Management Warehousing Access & Visualization
Data & its management are the foundation on which DSS application are
constructed.
The centralized database of data warehouse collects data from the
different sauces and organizes them, so they are easily accessible by
DSS and EIS applications.
Organizations, private and public, are continuously collecting data,
information, and knowledge at an accelerated rate and storing them in
computerized systems.
The accessed data must be analyzed and presented to the users.

Data Warehousing

There is a need for specialized, localized hardware and software
solutions.
There is a need for a cost effective means of uniting those information
resources into a manageable business asset.
Organizations today have a mixture of older, centralized systems and
newer, distribute systems, a wide variety of technologies is provided by an
even larger number of vendors.
Data Warehousing
Data warehousing (or information warehousing) is a concept designed to
provide a solution to the data access problem.
The data warehouse combines various data sources into a single
resource for end user access.
End users can perform ad hoc queries, reporting analysis, and
visualization of the warehouse information.
There can be several data warehouses in once company.
Benefits
It should provide ready access to critical data, insulate operation
databases from ad hoc processing that can slow TPS systems, and
provide high level summary information as edge, provide competitive
advantage, enhance customer services and satisfaction, facilitate decision
making and help in streamlining business process.

Optimization via Mathematical programming

Optimization via Mathematical programming
Linear programming (LP) is the best known technique in a family of
optimization tolls called mathematical programming.
It is used extensively in DSS.
Characteristics and Assumptions of LP programming
 Linear programming
Every LP problem is composed of the following.
Decision variables Whose values are unknown and are searched for
An objective
function
A linear mathematical function that relates the decision variables
to the goal and measures goal attainment and is to be optimized
Objective function
coefficients
Unit profit or cost coefficients indicating the contribution to the
objective of one nit of a decision variable
Constraints Expressed in the form of linear inequalities or equalities that limit
resources, and/or requirements
These relate the variables through linear relationships
Capacities Which describe the upper and sometimes lower limit on the
constraints & variables
Input-Output
(technology)
coefficients
Which indicate resource utilization for a decision variable
It is easy to interface other optimization software with Excel, database
management system and similar tools.
 Heuristic Programming
The determination of optimal solutions to some complex decision
problems could involve a prohibitive amount of time & cost, or may even
be impossible.
The simulation approach may be lengthy, complex, and even inaccurate.
It is sometimes possible to arrive at satisfactory solutions more quickly &
less expensively by sing heuristics.
Heuristics are used primarily for solving ill-structured problems, thy can
also be used to provide satisfactory solutions to certain complex, well
structured problems.
The main difficulty in using heuristics is that they are not as general as
algorithms.
They can normally be used only for the specific situation for which they
were intended.
Another problem with heuristics is that they may obtain a poor solution.
Heuristic programming is the approach of using heuristics to arrive at
feasible and "good enough" solutions to some complex problems; "god
enough" is usually in the range of 90-99.9 % of the objective value of an
optimal solution.
When to use Heuristics, Advantages and limitations of Heuristics

Simulation
To simulate means to assume the appearance of the characteristics of
reality.
DSS deals with semi-structured or unstructured situations. It involves
complex reality, which may not be easily represented by optimization or
other models.
Simulation is one of the most commonly used tolls of DSS.
Major Characteristics
Simulation is a technique for conducting experiments.
Simulation is a descriptive rather than a normative tool.
Once the characteristics value is computed, the best among several
alternatives can be selected.
Simulation is usually called for only when a problem is too complex to be
treated by numerical optimization techniques.
Advantages
Simulation theory is fairly straightforward.
A great amount of time compression can be attained.
Simulation is descriptive rather than normative. This allows the manager
to ask what if questions.
An accurate simulation model requires an intimate knowledge of the
problem.
The simulation model is built for one particular problem and typically will
now solve any other problem. No generalized understanding is required of
the manager; every component in the model corresponds to a part of the
real life model.
Simulation can handle an extremely wide variety of problem types, such
as inventory and staffing, as well as higher managerial level functions
such as long range planning.
Simulation generally allows for inclusion of the real life complexities of
problems, simplifications are not necessary.
It is very easy to obtain a wide variety of performance measures directly
form the simulation.
Simulation is often the only modeling tool for DSS were problem can be
non-structured.
Limitations of Simulations

Modeling for DSS

Modeling for DSS
A statistical model
(Regression
analysis)
Used for finding relationships among variables
This model is programmed in a DSS development software tool
A financial model Used for developing income statements & projecting financial
data for several years
This model is written with a special DSS financial planning
language called the interactive financial planning system (IFPS)
A linear
programming
Used to determine the best media selection.
It is solved using commercially available management science
software.
Some of the major issues involve din modeling are problem identification
and environmental analysis, variable identification, forecasting, the use of
multiple models, model categories, model management, and knowledgebased
modeling.

Identification of the problem and environment analysis
Identification of
the variables
It is the utmost importance.
Their relationships, influence diagrams, can be helpful in
this process.
Forecasting The results of a decision based on a model will usually
occur in the future.
Multiple Models DSS may include several models.

Categories of Models
DSS models are classified in seven groups.
Model
Management
Models like data must be managed.
Such management is done with the aid of model base
management software.
Knowledge base
modeling
DSS uses mostly quantitative models
Some knowledge is necessary to construct solvable models
Dynamic models
& static models
Dynamic models are used to evaluate scenarios that change
over time.
Example_ 5 year profit & loss projection.
Dynamic models are time dependent.
Example_ in determining how many checkout points should be
open in a supermarket.
They show trends & patterns over time.
They show average per period, moving averages, & comparative
analysis.
Example_ the transpiration model
Treating
certainty,
uncertainty, risk
When we build models, any of these conditions may occur.
Certainty models Everyone likes certainty models because they are easy to work
with and can yield optimal solutions.
Uncertainty
models
Managers attempt to avoid uncertainty as much as possible.
Risk Most major business decisions are made under assumed risk.
Several techniques can be used to deal with risk analysis.
 Influence
Just as a flowchart can be use as a graphic representation of computer
program flow for design purposes, an influence diagram can be used to
map a model’s design.
The term influence refers to the dependency of a variable on the level of
another variable.
Rectangle A decision variable
Circle Uncontrollable or intermediate variable
Oval Result (Outcome) variable
Intermediate or final
The variables are connected with arrows, which indicate the direction of
the influence (relationship).
Arrows can be one way or two-way (Bi-directional), depending on the
direction of influence of a pair of variables.
 Software
1 Analytica Analytica supports hierarchical diagrams, multidimensional
arrays, integrated documentation & parameter analysis
2 DPL This product provides a synthesis of influence diagrams and
decision trees
3 DS Lab
4 INDIA The solution process of this product transforms the original
problem into a new, reduced form in an attempt to determine
optimal policy
5 Precision
Tree
This creates influence diagrams & decision trees directly in
the Excel spreadsheet
Standard computer graphics software packages & computer aided
software engineering (CASE) packages can be used to create and
maintain influence diagram

The Knowledge Management Subsystem

The Knowledge Management Subsystem
The more advanced DSS are equipped with a component called
knowledge management.
Knowledge management software provides the necessary execution and
integration of the intelligent system.
A DSS that includes such a component is called an intelligent DSS, a
DSS/Es, expert support system, or knowledge based DSS.
The current generation of data mining applications include ANN (artificial
neural networks)

The User interface (DIALOG) subsystem
The term user interface covers all aspects of communication between a
user and the DSS.
The most important component because much of he power, flexibility and
ease of use characteristics of DSS is derived from this component.
An inconvenient user interface is one of the major reasons why managers
have not used computers and quantitative analysis to the extent that those
technologies have been available.
 Management of the user interface subsystem
The user interface subsystem is managed by software called the user
interface management system (UMIS),
The UIMS is composed of several programs that provide the capabilities
listed in DSS.
The UIMS is also known as the dialog generation and management
system.
 The user interface process
The user interacts with the computer via an action language processed via
the UIMS.
The UIMS enables the user to interact with the model management and
data management subsystems.

DSS Hardware
DSS run on standard hardware.
The major hardware options are the organization’s mainframe computer, a
workstation, a personal computer, or a client/server system.

Classification of DSS
Classification is based on the "degree of action of systems output"
DSS are made by following seven categories.
Holsapple and Whinston classify DSS into 6 types.
1) Text oriented DSS
2) Database oriented DSS
Data oriented, performing data retrieval or
analysis
3) Spreadsheet oriented DSS Deals with data and models
4) Solver oriented DSS
5) Rule oriented DSS
6) Compound DSS
Model oriented providing simulation capabilities,
optimization or computation that suggest an
answer
 Text Oriented DSS
A text oriented DSS supports a decision maker by electronically keeping
trade of textually represented information that could have a bearing on
decision.
It allows documents to be electronically created, revised and viewed as
needed.
Information technologies such as document emerging, hypertext and
intelligent agents can be incorporated into this type.
 Database oriented DSS
Database plays a major role in the DSS structure.
Rather than being treated as streams of text, data are organized in a
highly structured format.
 Spreadsheet oriented DSS
Spreadsheet is a modeling language that allows the user to write models
to execute DSS analysis.
They create, view and modify procedural knowledge.
The most popular tools used are excel and lotus 1-2-3.
 Solver oriented DSS
A solver is an algorithm or procedure written as a computer program for
performing certain computations for solving and particular problem type.
The solve could be economic order quantity procedure for calculating an
optional ordering quantity or a linear regression routing for calculating trend
excel, lotus 1-2-3 and quatropro can be used to develop such an system.
 Rule oriented DSS
Expert system is one example.
 Compound DSS
It is hybrid system that includes two or more of the five basic structures
explained above.
It can be built by using a set of independent DSS.

Model Base

Model Base
A model base contains routine and special statistical, financial, fore
casting, management science, and other quantitative models that provide
the analysis capabilities in a DSS.
Four major categories are…
Strategic, tactical, operational and Model building blocks and routines.
Strategic
Models
Used to support top
management’s planning
responsibilities
Developing corporate objectives
Planning for mergers &
acquisitions
Plant location selection
Environmental impact analysis
Non routine capital budgeting
Tactical
Models
Used mainly by middle
management to assist in
allocating & controlling the
organization’s resources.
Time horizon varies from 1 month
to less than 2 year
Labor requirement planning
Sales promotion planning
Plant layout determination
Routine capital budgeting
Operational
Models
Used to support the day to day
working activities of the
organization
Operational models support
mainly first line manager’s
decision making with a daily to
monthly time horizons.
Personal loans by a bank
Production scheduling
Inventory control
Maintenance planning &
scheduling
Quality control
Model
Building
Blocks &
Random number generator routine
Curve line fitting routine
Present value computational
routines routine
Regression analysis
Modeling
Language
COBOL, with a spreadsheet or
with outer fourth generation
language
Special modeling languages such
as IFPS/PLUS

The model base management system (MBMS)
MBMS software are model creation using subroutines and other building
blocks, generation of new routines and reports, model updating and
changing and model data manipulation.
 The Model directory
The role of the model directory is similar to that of a database directory.
It is catalog of all the methods and other software in the model base.
It contains the model definitions, and its main function is to answer
questions about the availability and capability of the model.
 Model execution, integration and command
Model execution is the process of controlling the actual running of the
model.
Model integration means combining the operations of several models
when needed.
A model command processor is used to accept and interpret modeling
instructions from the dialog component and to routine them to the MBMS,
the model execution or the integration functions.

The Kno

Implementation Phase

Implementation Phase
It may be defined as putting a recommended solution to work.
The implementation of a proposed solution to a problem is, in effect, the
initiation of a new order of things, or the introduction of a change.

Definition of DSS
Model based set of procedures for processing data & judgments to assist
a manager in his decision making.
System must be simple, robust, easy to control, adaptive, complete on
important issues, and easy to communicate with.
The system is computer based and serves as an extension of the user’s
problem solving capabilities.

Characteristics & Capabilities of DSS

Components of DSS
Data management
subsystems
Includes the database, which contains relevant data for the
situation & is managed by software called Database
management system – DBMS
Model management
subsystems
A software package that includes financial, statistical
management science or other quantitative models that
provide the system’s analytical capabilities & appropriate
software management.
Knowledge
management
subsystems
This system can provide any of the other subsystem or act as
an independent component. It provides intelligence to
augment the decision maker’s own.
User interface
subsystem
The user communications with and commands the DSS
through this subsystem.

The Data Management System
The data management subsystem s composed of the following elements.
1. DSS database
a) Data organization
b) Extraction
2. Database management system
3. Data directory
4. Query facility
 The Database
A database is a collection of interrelated at an organized to meet the
needs and structure of an organization and can be used by more than one
person for more than one application.
Internal data comes mainly from the organization’s transaction processing
system.
Example _ Monthly payroll
Examples of their internal data are machine maintenance scheduling,
forecasts of future sales, cost of out of stock items, and future hiring plans.
External data may include industry data, marketing research data, census
data, regional employment data, government regulations, tax rate
schedules, or national economic data.
This data might come from the Government, trade associations, marketing
research firms, econometric forecasting firms, and the organizations own
efforts in collecting external data.
Internet or from computerized online services. Private data may include
guidelines used by specific decision makers and assessments of specific
data and/or situations.
 Extraction
To create a DSS, database, or a data warehouse, it is often necessary to
capture data from several sources.
This operation is called extraction.
It is basically the importing of files, summarization, filtration, and
condensation of data.
Extraction also occurs when the user produces reports from the data in
the DSS database.
The extraction process is managed by a DBMS.
 Database Management System
The database is created, accessed, and updated by a DBMS.
An effective database and its management can support many managerial
activities; general navigation among records, support for creating and
maintaining a diverse set of data relationships, and report generation are
typical examples.
 The Query Facility
In building and using DSS, it is often necessary to access, manipulate,
and query the data. The query facility accepts requests for data from other
DSS components, determines how this request can be filled, formulates
the detailed request, and returns the results the issuer of the request.
Important functions of DSS query system are the selection and
manipulation operations.
 The Directory (Dictionary)
The Data directory is a catalog of all the data in the database.
It contains the data definitions and its main function is to answer question
about the availability of data items, their source and their exact meaning.
The directory is especially appropriate for supporting the intelligence
phase of the decision making process.

The Data Management System
The model management subsystem of the DSS is composed of the
following elements.
1. Model base
a) Strategic
b) Tactical
c) Operational
d) Model building blocks and routines
2. Model base management system
3. Modeling language
4. Model directory
5. Model execution, integration and command processor