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Data Modeling

Data Modeling. Introduction. Learning Objectives. Define key data modeling terms Entity type Attribute Multivalued attribute Relationship Degree Cardinality Business Rule Associative entity Trigger Supertype Subtype. Learning Objectives.

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Data Modeling

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  1. Data Modeling Introduction

  2. Learning Objectives • Define key data modeling terms • Entity type • Attribute • Multivalued attribute • Relationship • Degree • Cardinality • Business Rule • Associative entity • Trigger • Supertype • Subtype

  3. Learning Objectives • Review the role of conceptual data modeling in overall design and analysis of an information system • Learn to draw Entity-Relationship (E-R) Diagrams • Understand basic and advanced concepts of ER Diagrams • Understand various types of relationships, such as unary, binary, and ternary that can exist

  4. Data Modeling Vs. Process Modeling • Data models are similar to process and logic models, but there are differences. • Similarities include: • Data modeling is a “Requirements Structuring” technique along with process and logic models. • Data modeling is based on requirements gathering techniques such as interviewing, questionnaires and JAD. • There are many ways to model data, e.g. ERDs or class diagrams. • ERDS are very popular as they are semantically very rich. • There are many versions of ERD notation, but no official standard exists.

  5. Data Modeling Vs. Process Modeling • Data models are similar to process and logic models, but there are differences. • Whereas business processes are fairly dynamic, the fundamental data for an organization do not normally change over time. • For this reason, many people hold data models in the highest regard compared to process models. • Think of an ERD as depicting the skeleton of the information system • A Data Flow Diagram depicts the blood flowing through the system • Consistency must be maintained between process flow, decision logic and data modeling descriptions.

  6. Conceptual Data Modeling • Data can be modeled at many levels, including the conceptual, logical and physical level. • What is conceptual data modeling? It is a very high level representation of organizational data. • The purpose is to show the basic building blocks for the organization, i.e. the entities and rules about their meaning and interrelationships • Logical data modeling adds more detail to conceptual modeling, but is still concerned only with how the organization/business uses data. • Physical data modeling adds more detail, but is especially concerned with the actual physical implementation of the data.

  7. Gathering Information for Conceptual Data Modeling • Two perspectives • Top-down • Data model is derived from an intimate understanding of the business • Bottom-up • Data model is derived by reviewing specifications and business documents

  8. Process of Conceptual Data Modeling • First step is to develop a data model for the system being replaced • Next, a new conceptual data model is built that includes all the requirements of the new system • In the design stage, the conceptual data model is translated into a physical design • Project repository links all design and data modeling steps performed during SDLC

  9. Four Types of Data Models Pgs. 308-309

  10. Figure 10-3Sample conceptual data model diagram

  11. Deliverables and Outcome • Second deliverable is a set of entries about data objects to be stored in repository or project dictionary • Repository links data, process and logic models of an information system • Data elements that are included in the DFD must appear in the data model and visa versa • Each data store in a process model must relate to business objects represented in the data model

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