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Decision Support System

A Decision Support System (DSS) is a collection ofintegrated software applications and hardwarethat form the backbone of an organizationsdecision making process. Companies across allindustries rely on decision support tools,techniques, and models to help them assess andresolve everyday business questions. The decisionsupport system is data-driven, as the entireprocess feeds off of the collection and availabilityof data to analyze. Business Intelligence (BI)reporting tools, processes, and methodologiesare key components to any decision supportsystem and provide end users with rich reporting,

monitoring, and data analysis.

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High-level Decision Support System Requirements:

Data collection from multiple sources (sales data, inventory

data, supplier data, market research data. etc.)

Data formatting and collation

A suitable database location and format built for decision

support -based reporting and analysisRobust tools and applications to report, monitor, and

analyze the data

Decision support systems have become critical and ubiquitous

across all types of business. In todays global marketplace, it isimperative that companies respond quickly to market

changes. Companies with comprehensive decision support

systems have a significant competitive advantage.

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Decision Support Systems delivered by MicroStrategy Business Intelligence

Business Intelligence (BI) reporting tools, processes, and methodologies are

key components to any decision support system and provide end users with

rich reporting, monitoring, and data analysis. MicroStrategy providescompanies with a unified reporting, analytical, and monitoring platform that

forms the core of any Decision Support System. The software exemplifies all of

the important characteristics of an ideal Decision Support System:

Supports individual and group decision making: MicroStrategy provides a

single platform that allows all users to access the same information and accessthe same version of truth, while providing autonomy to individual users and

development groups to design reporting content locally.

Easy to Develop and Deploy: MicroStrategy delivers an interactive, scalable

platform for rapidly developing and deploying projects. Multiple projects can

be created within a single shared metadata. Within each project, developmentteams create a wide variety of re-usable metadata objects. As decision support

system deployment expands within an organization, the MicroStrategy

platform effortlessly supports an increasing concurrent user base.

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Comprehensive Data Access: MicroStrategy software allows users to

access data from different sources concurrently, leaving organizations

the freedom to choose the data warehouse that best suits their unique

requirements and preferences.Integrated software: MicroStrategys integrated platform enables

administrators and IT professionals to develop data models, perform

sophisticated analysis, generate analytical reports, and deliver these

reports to end users via different channels (Web, email, file, print and

mobile devices). This eliminates the need for companies to spendcountless effort purchasing and integrating disparate software products

in an attempt to deliver a consistent user experience.

Flexibility: MicroStrategy SDK (Software Development Kit) exposes its

vast functionality through an extensive library of APIs. MicroStrategy

customers can choose to leverage the power of the softwares flexible

APIs to design and deploy solutions tailored to their unique business

needs

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Simon's Model

Simon's Model is based on premise that decision rash null.

Decision making in Simon's Model is characterized by limited

information processing and use of rules. Simmons decision-

making model there are four phases

1) Intelligence phase

2) Design phase

3) Choice phase4) Implementation phase

Initially the problem comes and we are in the intelligence phase

thinking of the problem as it comes and then we try to find out

what the solution to the given problem and then we move to

design phase. In the design phase the way and method to solvethe problem is thought and we actually try analyze the problem,

we try to find the algorithms and the way that can actually solve

the problem and hence we use the genetic algorithm to find the

solution to the given problem .

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After finding the method which is to be applied to

the given problem we move to choice phase and

here the actual work of finding the best algorithmcome .Here we try to find the best algorithm from

the given set of algorithm we have the option of

choosing the algorithms such as "ACO" algorithm

which is called the ant colony optimizationalgorithm or we have the choice of finding the

algorithm such as Simulated annealing (SA) is a

related global optimization technique that traverses

the search space by testing random mutations onan individual solution.

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A mutation that increases fitness is always accepted. A

mutation that lowers fitness is accepted probabilistically

based on the difference in fitness and a decreasingtemperature parameter. In SA parlance, one speaks of

seeking the lowest energy instead of the maximum fitness.

SA can also be used within a standard GA algorithm by

starting with a relatively high rate of mutation anddecreasing it over time along a given schedule. After

deciding that genetic algorithm is the most suitable

algorithm for the programming we move to the next step

which is the implemetation phase here the realimplemeation of the slotuin is done we implemet the

solution to the given problem by using the geneteic

algorithm according to the given problem.

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In the given problem a list of 26 items is given

they all have different price, different weights and

different volumes. The problem says that we haveto find the items which can be fitted in to the

given space of the container the number of items

chosen to be fitted in to the given space should besuch that the weight and the volume of the

selected items should not be more than the total

allowed volume and weight in the container.

The care has to be taken such that the totalweight and volume of the selected items should

not exceed more than the allowed weight and the

volume.

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Structured Query LanguageTo communicate with the database system itself we need a

language. SQL is an international standard language formanipulating relational databases. It is based on an IBM

product. SQL is short for Structured Query Language.

SQL can create schemas, delete them, and change them. It can

also put data into schemas and remove data. It is a datahandling language, but it is not a programming language.

SQL is a DSL (Data Sub Language), which is really a combination

of two languages. These are the Data Definition Language

(DDL) and the Data Manipulation Language (DML). Schemachanges are part of the DDL, while data changes are part of the

DML. We will consider both parts of the DSL in this discussion

of SQL.

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Database Models

A data model comprises

a data structure

a set of integrity constraintsoperations associated with the data structure

Examples of data models include:

hierarchic

networkrelational

Models other than the relational database module used to be

quite popular. Each model type is appropriate to particular

types of problem. The Relational model type is the mostpopular in use today, and the other types are not discussed

further.

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Relational Databases

The relational data model comprises:

relational data structure

relational integrity constraints

relational algebra or equivalent (SQL)

SQL is an ISO language based on relational algebra

relational algebra is a mathematical formulationRelational Data Structure

A relational data structure is a collection of tables or relations.

A relation is a collection of rows or tuples

A tuple is a collection of columns or attributesA domain is a pool of values from which the actual attribute

values are taken

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Online Transaction ProcessingOnline Transaction processing database applications are optimal

for managing changing data, and usually have a large number of

users who will be simultaneously performing transactions that

change real-time data. Although individual requests by users for

data tend to reference few records, many of these requests are

being made at the same time. Common examples of these types of

databases are airline ticketing systems and banking transactionsystems. The primary concerns in this type of application are

concurrency and atomicity.

Concurrency controls in a database system ensure that two users

cannot change the same data, or that one user cannot change a

piece of data before another user is done with it. For example, ifyou are talking to an airline ticket agent to reserve the last available

seat on a flight and the agent begins the process of reserving the

seat in your name, another agent should not be able to tell another

passenger that the seat is available.

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Atomicity ensures that all of the steps involved in a

transaction complete successfully as a group. If anystep fails, no other steps should be completed. For

example, a banking transaction may involve two

steps: taking funds out of your checking account andplacing them into your savings account. If the step

that removes the funds from your checking account

succeeds, you want to make sure that the funds are

placed into your savings account or put back into

your checking account.

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Online Transaction Processing Design Considerations

Transaction processing system databases should be designed to

promote:1)Good data placement.

I/O bottlenecks are a big concern for OLTP systems due to the number

of users modifying data all over the database. Determine the likely

access patterns of the data and place frequently accessed data together.

Use filegroups and RAID (redundant array of independent disks) systemsto assist in this.

2)Short transactions to minimize long-term locks and improve

concurrency.

Avoid user interaction during transactions. Whenever possible, execute

a single stored procedure to process the entire transaction. The order in

which you reference tables within your transactions can affect

concurrency. Place references to frequently accessed tables at the end

of the transaction to minimize the duration that locks are held.

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3)Online backup.

OLTP systems are often characterized by continuous operations (24

hours a day, 7 days a week) for which downtime is kept to an absoluteminimum. Although Microsoft SQL Server 2000 can back up a

database while it is being used, schedule the backup process to occur

during times of low activity to minimize effects on users.

4)High normalization of the database.

Reduce redundant information as much as possible to increase thespeed of updates and hence improve concurrency. Reducing data also

improves the speed of backups because less data needs to be backed

up.

5)Little or no historical or aggregated data.

Data that is rarely referenced can be archived into separate databases,

or moved out of the heavily updated tables into tables containing only

historical data. This keeps tables as small as possible, improving backup

times and query performance.

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6)Careful use of indexes.

Indexes must be updated each time a row is added or

modified. To avoid over-indexing heavily updated

tables, keep indexes narrow. Use the Index Tuning

Wizard to design your indexes.

7)Optimum hardware configuration to handle thelarge numbers of concurrent users and quick response

times required by an OLTP system.

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Group Decision Support SystemThe tools we provide are collectively known as Group Decision Support

Software (GDSS) and they are designed to enable a group of participants

to work interactively in an electronic environment. GDSS systems helpusers to solve complex problems, prepare detailed plans and proposals,

resolve conflicts, and analyze and prioritize issues effectively. They are

excellent in situations involving visioning, planning, conflict resolution,

team building, and evaluation.

Each participant has a computer terminal from which they interact withthe rest of the group. The computers are networked so that each

individual's screen is private, but the information they enter is displayed

anonymously on a public screen.

The Process

A typical GDSS session includes four phases:Idea Generation

Idea Consolidation

Idea Evaluation

Implementation Planning

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GDSS does not replace human interaction, but rather supports and

enhances the group's decision making process; typically 30% of

interactions take place on the computers.

The BenefitsMore information in less time: Since GDSS allows group members to

contribute in parallel, significantly more information can be gathered in a

shorter period.

Greater Participation: The anonymity provided by GDSS enables group

members to express themselves freely, reducing the risk of 'group think'and conformance pressure. The loudest voice need not dominate the

discussion.

More structure: More focused and concentrated discussions result with

GDSS than would be possible in traditional meetings. Irrelevantdigressions are minimized.

Automated Documentation: Comments are never forgotten, results are

available immediately, and excellent graphics make it easy to see (and

therefore discuss) areas of dispute.

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Applications

Strategic Planning - Analyze the environment, develop a

vision, identify objectives, and build action plans.

Project Evaluation - Assess objectives achievement, impacts,relevance, cost effectiveness, and future directions.

Focus Groups and Expert Panels - Elicit opinions and

understand needs.

Conflict Resolution - Compare points-of-view, understanddifferences, and seek common ground.

Problem Solving - Identify causes, suggest alternatives,

choose solutions, and develop implementation plans.

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Online Analytical Processing

OLAP (OnlineAnalytical Processing) is a methodology to provide end users

with access to large amounts of data in an intuitive and rapid manner to assistwith deductions based on investigative reasoning. OLAP (online analytical

processing) is computer processing that enables a user to easily and selectively

extract and view data from different points of view. For example, a user can

request that data be analyzed to display a spreadsheet showing all of a

company's beach ball products sold in Florida in the month of July, compare

revenue figures with those for the same products in September, and then see a

comparison of other product sales in Florida in the same time period. To

facilitate this kind of analysis, OLAP data is stored in a multidimensional

database. Whereas a relational database can be thought of as two-

dimensional, a multidimensional database considers each data attribute (such

as product, geographic sales region, and time period) as a separate"dimension." OLAP software can locate the intersection of dimensions (all

products sold in the Eastern region above a certain price during a certain time

period) and display them. Attributes such as time periods can be broken down

into subattributes.

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OLAP can be used for data mining or the discovery of

previously undiscerned relationships between data items. An

OLAP database does not need to be as large as a datawarehouse, since not all transactional data is needed for

trend analysis. Using Open Database Connectivity (ODBC),

data can be imported from existing relational databases to

create a multidimensional database for OLAP.Two leading OLAP products are Hyperion Solution's Essbase

and Oracle's Express Server. OLAP products are typically

designed for multiple-user environments, with the cost of the

software based on the number of users.

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Online Analytical Processing (OLAP) Systems for Decision Support

IT organizations are faced with the challenge of delivering systems that

allow knowledge workers to make strategic and tactical decisions based

on corporate information. These decision support systems are referred

to as Online Analytical Processing (OLAP) systems, and they allow

knowledge workers to intuitively, quickly, and flexibly manipulate

operational data using familiar business terms, in order to provideanalytical insight.

OLAP systems need to:

1.Support the complex analysis requirements of decision-makers,

2.Analyze the data from a number of different perspectives (business

dimensions), and3.Support complex analyses against large input (atomic-level) data sets

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Expert systemsan expert system is a computer program that simulates the thought

process of a human expert to solve complex decision problems in a specific

domain. This chapter addresses the characteristics of expert systems that

make them different from conventional programming and traditionaldecision support tools. The growth of expert systems is expected to

continue for several years. With the continuing growth, many new and

exciting applications will emerge. An expert system operates as an

interactive system that responds to questions, asks for clarification, makes

recommendations, and generally aids the decision-making process. Expert

systems provide expert advice and guidance in a wide variety of activities,

from computer diagnosis to delicate medical surgery.

Various definitions of expert systems have been offered by several authors.

A general definition that is representative of the intended functions of

expert systems is:

An expert system is an interactive computer-based decision tool that uses

both facts and heuristics to solve difficult decision problems based on

knowledge acquired from an expert.

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An expert system may be viewed as a computer simulation of a

human expert. Expert systems are an emerging technology with

many areas for potential applications. Past applications range from

MYCIN, used in the medical field to diagnose infectious blooddiseases, to XCON, used to configure computer systems. These

expert systems have proven to be quite successful. Most

applications of expert systems will fall into one of the following

categories:

Interpreting and identifying

Predicting

Diagnosing

Designing

Planning

Monitoring

Debugging and testing

Instructing and training

Controlling

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Applications that are computational or deterministic in nature are not

good candidates for expert systems. Traditional decision support

systems such as spreadsheets are very mechanistic in the way they solve

problems. They operate under mathematical and Boolean operators in

their execution and arrive at one and only one static solution for a given

set of data. Calculation intensive applications with very exacting

requirements are better handled by traditional decision support tools or

conventional programming. The best application candidates for expertsystems are those dealing with expert heuristics for solving problems.

Conventional computer programs are based on factual knowledge, an

indisputable strength of computers. Humans, by contrast, solve

problems on the basis of a mixture of factual and heuristic knowledge.

Heuristic knowledge, composed of intuition, judgment, and logicalinferences, is an indisputable strength of humans. Successful expert

systems will be those that combine facts and heuristics and thus merge

human knowledge with computer power in solving problems.