Databases and File Systems

• Database: Shared, integrated computer structure storing a collection of end-user data and metadata.
• End-user data: Raw facts of interest to the end user.
• Metadata: Data about data; integrates and manages end-user data, describes data characteristics and relationships.
• Database Management System (DBMS): Collection of programs that manages database structure and controls data access.
• Data inconsistency: Different versions of same data appear in different places.
• Data quality: Promotes accuracy, validity, and timeliness of data.
• Evolution of File System Data Processing:
  • Manual File Systems used file folders and filing cabinets.
  • Computerized File Systems employed a data processing (DP) specialist to create systems for tracking data and producing reports.
  • File System Redux: Modern End-User Productivity Tools includes spreadsheet programs like Microsoft Excel.
• Structural dependence: Access to a file depends on its structure.
• Structural independence: Changes to file structure affect application's ability to access data.
• Data Redundancy: Storing the same data unnecessarily in different places.
• Data anomaly: Develops when all required changes in redundant data aren't made successfully.
• Data dictionary: Stores definitions of data elements and their relationships.

Database Development Process

• Enterprise Modeling: Reviews the current system and describes the data needed at a high level of abstraction.
• Information Systems Architecture (ISA): Blueprint for information systems.

Information Systems Architecture

• Reasons
• Data

Term Definitions

• Data: Raw facts like telephone numbers, birth dates, customer names, and year-to-date sales value, which lack meaning unless organized.
• Field: A character or group of characters (alphabetic or numeric) with a specific meaning, used to define and store data.
• Record: Logically connected set of one or more fields describing a person, place, or thing.
• File: Collection of related records.

Information Engineering

• Formal top-down methodology: Uses data orientation to create and maintain information systems.
• Data-oriented methodology: Creates and maintains information systems.

Steps:

• Planning
• Analysis
• Design
• Implementation

Developing an Enterprise Model Components

• Functional breakdown
• Enterprise Data Model
• Planning Matrices

System Development Life Cycle

• Planning
• Analysis
• Design
• Development
• Testing
• Implementation
• Maintenance

SDLC Development Activities

• Enterprise Modeling: Analyzes current data processing.
• Conceptual Data Modeling: Identifies scope of database requirements for proposed information system.
• Logical Database Design: Analyzes transactions, forms, displays, and inquiries in detail.
• Physical Database Design and Definition: Requires knowledge of the specific DBMS to implement the database.
• Database Implementation: Involves a designer writing, testing, and installing programs to process data.
• Database Maintenance: Analyzes data and database applications to ensure evolving information requirements are met.
• Rapid Application Development: Iterative system creation process transforming specifications into a working system.