Data Modeling Introduction Learning Objectives Define key data

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 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

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.

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.

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.

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

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

Four Types of Data Models Current System Proposed System Project. Level E-R diagram for system being replaced Covers just data needed in the project’s application Enterprise -Level An E-R diagram for the whole database from which data for the application system being replaced is drawn An E-R diagram for the whole database from which the new application’s data are extracted Pgs. 308 -309

Figure 10 -3 Sample conceptual data model diagram

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