Data Resource Management

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Data Resource Management

• Lecture 9

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Database Structures

1. Hierarchical Structure
2. Network Structure
3. Relational Structure
4. Multidimensional Structure
5. Object-Oriented Structure

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3 Relational Structure

• All data elements within the database are viewed
  as being stored in the form of simple tables
• Represents data as two-dimensional tables called
  relations
• Relates data across tables based on common data
  element
• Relation is described as a table

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Relational Structure

• Table ? Relation
• Row ? Tuple
• Column ? Attribute

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Three basic operations in Relational Database

• Select
• Creates subset of rows that meet specific
  criteria
• Join
• Combines relational tables to provide users with
  information
• Project
• Enables users to create new tables containing
  only relevant information

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Relational Database
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Relational Database
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Three basic operations in RDBMS
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4 Multidimensional Data Model

• Multidimensional databases combine data from a
  multitude of data sources.
• Multi-dimensional databases are especially useful
  in sales and marketing applications that involve
  time series.
• Large volumes of sales and inventory data can be
  stored to ultimately be used for logistics and
  executive planning.
• For example, data can be more readily segregated
  by sales region, product, or time period.

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Multidimensional Data Model

• The data cube is a conceptual representation of
  database which can be implemented in a variety of
  ways, including top-down, bottom-up, and arrays.

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5 Object-Oriented Databases

• Object-oriented DBMS Stores data and procedures
  as objects that can be retrieved and shared
  automatically
• Object-relational DBMS Provides capabilities of
  both object-oriented and relational DBMS

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Object-Oriented Databases

• Can accommodate more complex data types including
  graphics, pictures, voice and text
• Encapsulation data values and operations that
  can be performed on them are stored as a unit
• Inheritance automatically creating new objects
  by replicating some or all of the characteristics
  of one or more existing objects

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Object-Relational DBMS

• An object-relational database (ORD) or
  object-relational database management system
  (ORDBMS) is a database management system (DBMS)
  similar to a relational database, but with an
  object-oriented database model objects, classes
  and inheritance
• In addition, it supports extension of the data
  model with custom data-types and methods.

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Evaluation of Database Structures

• Hierarchical data structure is best for
  structured, routine types of transaction
  processing.
• Network data structure is best when many-to-many
  relationships are needed.
• Relational data structure is best when ad hoc
  reporting is required.

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Types of Databases

• Conceptual categories of databases
• Operational Databases
• Distributed Databases
• External Databases
• Hypermedia Databases

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1 Operational Databases

• Store detailed data needed to support the
  business processes and operations of a company
• Also called Subject Area Databases (SADB),
  Transaction Databases, Production Databases

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2 Distributed Databases

• Many organizations replicate and distribute
  copies or parts of databases to network servers
  at variety of sites
• Distributed databases can reside on network
  servers on world wide web, corporate intranets or
  extranets, or on any other company network
• Distributed databases may be copies of
  operational or any other type of databases.
• Replication and distribution is done to improve
  database performance at end user worksites

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Distributed Databases

• Centralized Database
• Used by single central processor or multiple
  processors in client/server network
• Distributed Database
• Stored in more than one physical location
• Partitioned database
• Duplicated database

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Distributed Databases
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3 External Databases

• Contain a wealth of information available from
  commercial online services and from many sources
  on the World Wide Web

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4 Hypermedia Databases

• Hypermedia database consists of hyperlinked pages
  of multimedia (text, graphic, and images, video
  clips etc.)

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Data Warehouse

• Definition
• Large database that stores data that has been
  extracted from the various operational, external,
  and other databases of an organization
• Central source of data that has been
• Cleaned
• Transformed
• Cataloged

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Data Warehouse

• Stored data is used for
• Data Mining
• Online Analytical Processing
• Market Research
• Decision Support
• Data Warehouses are subdivided into
• Data Marts
• Databases that hold subsets of data from a data
  warehouse that focus on specific aspects of a
  company, such as a department or a business
  process

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Data Warehouse

• Supports reporting and query tools
• Stores current and historical data
• Consolidates data for management analysis and
  decision making
• Improved and easy accessibility to information
• Ability to model and remodel the data

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Data Warehouse Data Marts
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Data Warehouse Data Marts
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Retrieving Information from Data Warehouse
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Database Management Software (DBMS)

• Definition
• Software that controls the creation, maintenance,
  and use of databases
• Use of DBMS

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Database Maintenance

• Updating a database continually to reflect new
  business transactions and other events
• Updating a database to correct data and ensure
  accuracy of the data

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Database Interrogation

• Definition
• Capability of a DBMS to report information from
  the database in response to end users requests
• Query Language allows easy, immediate access to
  ad hoc data requests
• Report Generator - allows quick, easy
  specification of a report format for information
  users have requested

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Database Query vs. Report
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References

• Management Information Systems, 7th Edition,
  James A. OBrien, George M. Marakas. Chapter 5

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Data Mining

• Definition
• Analyzing the data in a data warehouse to reveal
  hidden patterns and trends in historical business
  activity

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Data Mining Uses

• Perform market-basket analysis to identify new
  product bundles.
• Find root causes to quality or manufacturing
  problems.
• Prevent customer attrition and acquire new
  customers.
• Cross-sell to existing customers.
• Profile customers with more accuracy.

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