dss & otp
TRANSCRIPT
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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.