Organizing Data and Information

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Organizing Data and Information

• Chapter 3

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Learning Objectives

• Define general data management concepts and
  terms, highlighting the advantages and
  disadvantages of the database approach to data
  management.
• Name three database models and outline their
  basic features, advantages, and disadvantages.

3
Learning Objectives

• Identify the common functions performed by all
  database management systems and identify three
  popular end-user database management systems.
• Identify and briefly discuss recent database
  applications.

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The Hierarchy of Data
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Date entries, attributes, and keys

• Entity Generalized class of people, places,
  systems for which data is collected. (Ex.
  Employees, customers)
• Attribute Characteristic of an entity (Ex. First
  name, last name)
• Key A set of fields used to identify an entity
• Primary Key A key that uniquely identified the
  entity

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Keys and Attributes
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The Traditional Approach To Data Management

• Create new files for each application
• Data redundancy
• Data integrity

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The Database Approach to Data Management
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Advantages of the Database Approach (1)

• Improved strategic use of corporate date
• Accurate information always available
• Reduced data redundancy
• Data is stored in one place
• Improved data integrity
• Changes are reflected throughout
• Easier modification and update
• No need to know where the data is

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Advantages of the Database Approach (2)

• Data and program independence
• Accurate information always available
• Better access to data and information
• Simple instructions to access data
• Standardization of data access
• Each DBMS uses the same set of instructions
• Standardization for programmers
• Should only know how to access the DBMS

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Advantages of the Database Approach (3)

• Better protection of data
• Require authorization on the data
• Shared data resources
• Setup the database once
• Several applications can use it

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Disadvantages of the Database Approach

• Costly
• Specialized DBMS software
• Specialized DBMS administrators and operators
• Increased vulnerability
• Single point of failure
• Targets for attacks

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Data Modeling

• Planned data redundancy
• To have it available in more than one place
• To improve system performance
• Data model
• A diagram of entities and their relationships
• Enterprise data modeling
• Done at the level of enterprise
• Entity-relationship diagrams
• Use graphs to show how data is organized and how
  it is related

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Entity-Relationship Diagram for a Customer
Ordering Database
Entity
Relationship (one-to-many)
Relationship (many-to-one)
Relationship (one-to-one)
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Database Models

• Hierarchical (tree)
• Data is organized top-down
• Network
• Owner-membership relationship
• A member can have many owners
• Relational
• Uses tabular format with 2-dimensional tables
  (relations)
• Relations resemble files

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Hierarchical Database Model
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Network Database Model
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Relational Database Model
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Relational Models

• Describe data using a standard tabular format
  with all data elements placed in two-dimensional
  tables, called relations, that are the logical
  equivalent of files.
• Rows represent data entity
• Columns represent attributes

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Relational Models

• Domain Set of values an attribute can have
• Age Between 0-100
• Gender Male or female
• Selecting
• Pick rows based on certain criteria
• Select those whose gender is female
• Projecting
• Create a new table with a subset of attributes
• Joining
• Combine two or more tables

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Linking Database Tables to Answer an Inquiry
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Building and Modifying a Relational Database
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Database Management Systems
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Providing a User View

• Schema - a description of the entire database
• First create a schema, then create the tables
• Subschema - a file that contains a description of
  a subset of the database and identifies which
  users can modify the data items in that subset
• A sales representative has to see the data for
  her office, not the company stock data

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The Use of Schemas and Subschemas
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Creating and Modifying the Database

• Data definition language (DDL) - a collection of
  instructions and commands used to define and
  describe data and data relationships in a
  specific database
• Used to define the schemas
• Data dictionary detailed description of data in
  a database
• Create a data dictionary when defining the schemas

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Typical Uses of a Data Dictionary

• Provide a standard definition of terms and data
  elements
• Assist programmers in designing and writing
  programs
• Simplify database modification
• Reduce data redundancy
• Increase data reliability
• Speed program development
• Ease modification of data and information

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Storing and Retrieving Data
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Data Access

• Concurrency control Lock the record so that only
  one application can access it at a time
• Data manipulation language (DML)
• Structured Query Language (SQL)
• SELECT FROM Project
• WHERE Project_number155
• UPDATE Project
• SET Project_number156
• WHERE Project_number155

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Structured Query Language
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Database Output
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Popular Database Management Systems

• Oracle
• MySQL
• Paradox database
• FileMaker Pro
• Microsoft Access
• Lotus 1-2-3 Spreadsheet

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Worldwide Database Market Share (2001)
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Selecting a Database Management System (1)

• Database size Number of records in the database
• Number of concurrent users People or
  applications that will access it at the same time
• Performance How fast can the DBMS access or
  update records?

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Selecting a Database Management System (2)

• Integration Which operating system can it run
  under?
• Features Which security procedures or privacy
  policies are in place?
• Vendor Size and reputation of the vendor
• Cost Initial cost, maintenance costs, hardware
  costs, personnel costs

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Database Applications
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Data Warehouses, Data Marts, and Data Mining

• Data Warehouse - a database that collects
  business information from many sources in the
  enterprise, covering all aspects of the companys
  processes, products, and customers.
• Data Mart a subset of a data warehouse.
• For small and medium size businesses
• Used mostly for decision support system
• Data Mining - an information analysis tool that
  involves the automated discovery of patterns and
  relationships in a data warehouse.

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Elements of a Data Warehouse
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Common Data Mining Applications
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Common Data Mining Applications (1)

• Branding and positioning of products
• Customer churn
• Which customers can switch to competitors?
• Direct marketing
• Who would respond to telemarketing?
• Fraud detection
• Predict transactions which are likely to be
  illegal

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Common Data Mining Applications (2)

• Market-based analysis
• Which products are bought at the same time
  (diaper, beer, chips)
• Market segmentation
• Group users based on similarity of products that
  they buy
• Trend analysis
• Analyze how variables change over time (e.g.,
  sales)

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Business Intelligence

• Gathering enough of the right information in a
  timely manner and usable form.
• Competitive intelligence
• What others are doing
• Counterintelligence
• Define trade secret information
• Knowledge management
• Capture companys collective expertise wherever
  it resides
• Record knowledge and share it

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Others

• Distributed databases
• Data is spread over a few database
• On-line analytical processing (OLAP)
• Programs used to store and deliver data
• Used to analyze millions of customer records
• Open database connectivity (ODBC) standards

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Comparison of OLAP and Data Mining
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Advantages of ODBC
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Object-Relational Database Management System

• Stores the following types of data as objects
• audio
• images
• unstructured
• text
• spatial data

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Spatial Technology
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Summary

• Data - one of the most valuable resources a firm
  possesses.
• Entity - a generalized class of objects for which
  data is collected, stored, and maintained.
• Attribute - a characteristic of an entity.
• DBMS - a group of programs used as an interface
  between a database and application programs.
• Data mining - the automated discovery of patterns
  and relationships in a data warehouse.