data warehousing, access, analysis, mining, and visualization

8/3/2019 Data Warehousing, Access, Analysis, Mining, And Visualization

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CHAPTER 4

Data Warehousing, Access,

Analysis, Mining, and Visualization


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Data Warehousing, Access,Analysis, Mining,and

Visualization

MSS foundation

Many new concepts

Object-oriented databases

Intelligent databases

Data warehouse

Data mining

Online analytical processing

Multidimensionality

Internet / Intranet / Web


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Data Warehousing, Access,Analysis,and Visualization

What to do with all the data that organizationscollect, store, and use?(Information overload!)

Solution

Data warehousing

Data access

Data mining

Online analytical processing (OLAP)

Data visualization

Data sources


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The Natureand SourcesofData

Data: Raw

Information: Data organized to convey meaning

Knowledge: Data items organized and processed to

convey understanding, experience, accumulated

learning, and expertise


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DSS Data Items

Documents

Pictures

Maps

Sound

Animation

Video

Can be hard or soft


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Data Sources Internal

External

Personal


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Data Collection, Problems,and Quality

Problems (Table 4.1)

Quality: determines usefulness of data

Intrinsic data quality

Accessibility data quality

Representation data quality


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Data Quality Issuesin

Data Warehousing Uniformity

Version Completeness check

Conformity check

Genealogy check (drill down)


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TheInternet andCommercialDatabase

Services

For external data

The Internet: major supplier of external data

Commercial Data Banks: sell access to specialized

databases

Can add external data to the MSS in a timely

manner and at a reasonable cost

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition

Copyright 2001, Prentice Hall, Upper Saddle River, NJ


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The InternetandCommercialDatabases

ServersUse Web Browsers to

Access vital information by employees and

customers

Implement executive information systems

Implement group support systems (GSS)

Database management systems provide data in

HTML, on Web servers directly


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Database Management SystemsinDSS

DBMS: Software program for entering (or adding)

information into a database; updating, deleting,

manipulating, storing, and retrieving information

A DBMS + modeling language to develop DSS

DBMS to handle LARGE amounts of information


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Database Organizationand Structure

Relational databases

Hierarchical databases

Network databases Object-oriented databases

Multimedia-based databases

Document-based databases Intelligent databases


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Data Warehousing

Physical separation of operational and decision support

environments

Purpose: to establish a data repository making operational data

accessible

Transforms operational data to relational form

Only data needed for decision support come from the TPS

Data are transformed and integrated into a consistent structure

Data warehousing (information warehousing): solves the data

access problem

End users perform ad hoc query, reporting analysis and

visualization


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Data Warehousing Benefits

Increase in knowledge worker productivity

Supports all decision makers data requirements

Provide ready access to critical data

Insulates operation databases from ad hocprocessing

Provides high-level summary information

Provides drill down capabilities

Yields Improved business knowledge

Competitive advantage

Enhances customer service and satisfaction

Facilitates decision making

Help streamline business processes


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Data WarehouseArchitectureand Process

Two-tier architecture

Three-tier architecture


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Data Warehouse Components Large physical database

Logical data warehouse

Data mart Decision support systems (DSS) and executive

information system (EIS)

Can feed OLAP


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Data Marts


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DW Suitability

For organizations where

Data are in different systems

Information-based approach to management in use

Large, diverse customer base

Same data have different representations in different

systems

Highly technical, messy data formats


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CharacteristicsofDataWarehousing

1. Data organized by detailed subject with

information relevant for decision support

2. Integrated data

3. Time-variant data

4. Non-volatile data


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OLAP: Data AccessandMining, Querying,and

Analysis

Online analytical processing (OLAP)

DSS and EIS computing done by end-users in online

systems

Versus online transaction processing (OLTP)


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OLAP Activities Generating queries

Requesting ad hoc reports

Conducting statistical and other analyses

Developing multimedia applications


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OLAP usesthedata warehouseandasetoftools,usually with

multidimensional capabilities

Query tools

Spreadsheets

Data mining tools

Data visualization tools


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UsingSQL for Querying

SQL (Structured Query Language)

Data language

English-like, nonprocedural, very user friendly

language

Free format

Example:

SELECT Name, Salary

FROM Employees

WHERE Salary >2000


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Data Miningfor

Knowledge discovery in databases

Knowledge extraction

Data archeology

Data exploration

Data pattern processing

Data dredging

Information harvesting


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MajorData MiningCharacteristicsand Objectives

Data are often buried deep

Client/server architecture

Sophisticated new tools--including advanced visualization

tools--help to remove the information ore End-user miner empowered by data drills and other power

query tools with little or no programming skills

Often involves finding unexpected results

Tools are easily combined with spreadsheets, etc.

Parallel processing for data mining


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Data Mining Application Areas

Marketing

Banking

Retailing and sales

Manufacturing and production

Brokerage and securities trading

Insurance

Computer hardware and software

Government and defense Airlines

Health care

Broadcasting

Law enforcement


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IntelligentData Mining

Use intelligent search to discover information withindata warehouses that queries and reports cannoteffectively reveal

Find patterns in the data and infer rules from them

Use patterns and rules to guide decision making andforecasting

Five common types of information that can be yieldedby data mining: 1) association, 2) sequences, 3)classifications, 4) clusters, and 5) forecasting


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Main ToolsUsedinIntelligentData Mining

Case-based Reasoning

Neural Computing

Intelligent Agents

Other Tools

Decision trees Rule induction

Data visualization


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Data VisualizationandMultidimensionality

Data Visualization Technologies

Digital images

Geographic information systems

Graphical user interfaces

Multidimensions

Tables and graphs

Virtual reality

Presentations

Animation


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Multidimensionality

3-D + Spreadsheets (OLAP has this)

Data can be organized the way managers like to seethem, rather than the way that the system analysts do

Different presentations of thesame data can bearranged easily and quickly

Dimensions: products, salespeople, market segments,business units, geographical locations, distributionchannels, country, or industry

Measures: money, sales volume, head count, inventoryprofit, actual versus forecast

Time: daily, weekly, monthly, quarterly, or yearly


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MultidimensionalityLimitations

Extra storage requirements

Higher cost

Extra system resource and time consumption More complex interfaces and maintenance

Multidimensionality is especially popular in

executive information and support systems


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Geographic Information

Systems (GIS) A computer-based system for capturing, storing,

checking, integrating, manipulating, and displaying

data using digitized maps

Spatially-oriented databases

Useful in marketing, sales, voting estimation, planned

product distribution

Available via the Web

Can use with GPS


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Virtual Reality

An environment and/or technology that provides

artificially generated sensory cues sufficient to

engender in the user some willing suspension of

disbelief

Can share data and interact

Can analyze data by creating a landscape

Useful in marketing, prototyping aircraft designs

VR over the Internet through VRML


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Business Intelligence

onthe Web Can capture and analyze data from Web

Tools deployed on Web


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Summary

Data for decision making come from internal andexternal sources

The database management system is one of the

major components of most management supportsystems

Familiarity with the latest developments is critical

Data contain a gold mine of information if they candig it out

Organizations are warehousing and mining data

Multidimensional analysis tools and new enterprise-wide system architectures are useful

OLAP tools are also useful


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Summary (contd.)

New data formats for multimedia DBMS

Internet and intranets via Web browser

interfaces for DBMS access

Built-in artificial intelligence methods inDBMS