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File and Database Design; Logic Modeling

File and Database Design; Logic Modeling. Class 24. Project Identification & Selection. Project Initiation & Planning. Analysis. Logical Design. Physical Design ***. Implementation. Maintenance. SDLC. Databases. File Systems Databases Hierarchical Database Model

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File and Database Design; Logic Modeling

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  1. File and Database Design; Logic Modeling Class 24

  2. Project Identification & Selection Project Initiation & Planning Analysis Logical Design Physical Design *** Implementation Maintenance SDLC

  3. Databases • File Systems • Databases • Hierarchical Database Model • Network Database Model • Relational Database Model • Object Oriented Database Model

  4. File Systems • Each new application is designed with its own set of files. • Problems: • Changes in files require changes to programs. • Uncontrolled redundancy • Inconsistent data • Limited data sharing

  5. Negatives Need new, specialized personnel Need for explicit backups Interference due to shared data Organizational conflict Postives Minimal data redundancy Consistent data Integration of data Sharing of data Data independence Ease of application development Reduced program maintenance Database Approach

  6. Designing a relational database • Create a table (file) for each entity type. • Choose a primary key for each table. • Choose appropriate data types and value restrictions for each field. • Create new tables to represent many-to-many relationships. • Add foreign keys to represent one-to-many relationships. • Define referential integrity constraints. • Evaluate schema quality and make necessary improvements.

  7. What we’re looking at • Databases, Tables (files, entities), Records, Fields (attributes) • How we’ll define above in the Data Dictionary

  8. Tables • Each entity on your ER diagram will become a table in your database.

  9. Records • A row in the table. • A group of fields (or attributes) stored in adjacent memory locations and retrieved together as a unit.

  10. Fields • May also be called an attribute or data element. • All fields (or attributes on your ER diagram) will be defined in the Data Dictionary.

  11. Fields • Name • Data type • Primary key(s) • Data integrity • Handling missing data • Ownership

  12. Data Integrity • Default value • Picture control • Range controls • Null Value controls

  13. Data Dictionary • The repository of all data definitions for all organizational applications.

  14. What we’ll show in a DD: • Table (file) (entity) name • All fields (attributes) within the table. • For each field: • Name • Note if primary key • Definition/description of field • Data type (page 352, or whether its numeric or character plus the length)

  15. TABLE: PIG Primary Key Attribute Name Attribute Definition Attribute Type * PigID The unique identifier of the pig 5 numeric characters PigName The name of the pig 15 alpha characters PigWeight The weight, in pounds and ounces of the pig 6 numeric characters, with 2 numbers after the decimal PigGender The gender of the pig – either male or female 1 alpha character, M or F are allowed

  16. Logic Modeling • Knowing what’s going on inside the programs. What is the logic to create the reports, calculated fields? • We’ll show logic modeling through use of decision trees and decision tables.

  17. Decision Tables • A matrix representation of the logic of a decision, which specifies the possible conditions for the decision and the resulting actions. • Condition stubs • Action stubs • Rules

  18. Steps in creating Decision tables: • Name all conditions and the values each condition can assume. • Name all possible actions that can occur. • List all possible rules based on every possible combination of conditions. • Define the actions for each rule. • Simplify the decision table by removing “indifferent conditions”.

  19. Examples!

  20. Decision Trees • A graphical representation of a decision situation in which decision situation points (nodes) are connected together by arcs (one for each alternative on a decision) and terminate in ovals (the action that is the result of all of the decisions made on the path leading to the oval

  21. Decision trees • Often used for statistical reasons – for calculating probabilities and making choices based on probabilities. • Used more often for “simpler” problems.

  22. Examples!

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