Data Modeling PDF

Data Modeling © Copyright 2011 John Wiley & Sons, Inc. 6-0  Outline  The Entity Relationship Diagram (ERD). - Elements of ERD - The Data Dictionary and Metadata  Creating an Entity Relationship Diagram.  Validating an ERD. © Copyright 2011 John Wiley & Sons...

Data Modeling © Copyright 2011 John Wiley & Sons, Inc. 6-0  Outline  The Entity Relationship Diagram (ERD). - Elements of ERD - The Data Dictionary and Metadata  Creating an Entity Relationship Diagram.  Validating an ERD. © Copyright 2011 John Wiley & Sons, Inc. 6-1  INTRODUCTION  In this chapter, we discuss how the data are organized and presented.  A data model is a formal way of representing the data that are used and created by a business system.  During analysis , analysts draw a Logical data model which shows the logical organization of data without indicating how it is stored, created, or manipulated.  During design , analysts draw a physical data model to reflect how the data will physically be stored in databases or files. © Copyright 2011 John Wiley & Sons, Inc. 6-2 (cont’d)  Topics of this chapter: - Creating an entity relationship diagram (ERD). - Normalization, a technique that helps analysts validate the data models. - How data models balance, or interrelate, with the process models. © Copyright 2011 John Wiley & Sons, Inc. 6-3 THE ENTITY-RELATIONSHIP DIAGRAM (ERD)  An entity-relationship diagram (ERD) is a picture showing the information that is created, stored, and used by a business system.  Entities lists similar kinds of information  Lines drawn between entities represent relationships among the data.  Special symbols communicate high-level business rules. © Copyright 2011 John Wiley & Sons, Inc. 6-4 Reading an Entity Relationship Diagram 6-5 Elements of an Entity Relationship Diagram 6-6  Entity The entity is the basic building block for a data model. It is a person, place, event, or thing about which data is collected.  Entities represent something for which there exist multiple instances, or occurrences. - E.g., John Smith could be an instance of the customer entity. © Copyright 2011 John Wiley & Sons, Inc. 6-7 (cont’d) 6-8  Attributes  An attribute is some type of information that is captured about an entity.  Attributes are nouns that are listed with an entity.  One or more attributes can serve as the entity identifier - the attribute(s) that can uniquely identify one instance of an entity.  Concatenated identifier - several attributes are combined to uniquely identify an instance. © Copyright 2011 John Wiley & Sons, Inc. 6-9 Choices for Identifiers 6-10  Relationships Relationships are associations between entities. Every relationship has a parent entity and a child entity. Relationships should be labeled with active verbs. © Copyright 2011 John Wiley & Sons, Inc. 6-11  Cardinality  A relationship has cardinality which is the ratio of parent instances to child instances.  The 1:1 relationship means that one instance of the parent entity is associated with one instance of the child entity.  The 1:N relationship means that a single instance of a parent entity is associated with many instances of a child entity.  The M:N relationship means that many instances of a parent entity can relate to many instances of a child entity. © Copyright 2011 John Wiley & Sons, Inc. 6-12 Example of M:N Relationship 6-13  Modality  A relationship has modality of null or not null, which refers to whether or not an instance of a child entity can exist without a related instance in the parent entity.  Null means that an instance of a child entity can exist without a related instance in the parent entity.  Not Null means that an instance of a child entity can’t exist without a related instance in the parent entity. © Copyright 2011 John Wiley & Sons, Inc. 6-14 The Data Dictionary and Metadata  A data dictionary contains the information about the entities, attributes, and relationships on the ERD, or metadata.  Metadata is data about data.  Metadata is stored in the data dictionary so it can be shared by developers and users throughout the SDLC. © Copyright 2011 John Wiley & Sons, Inc. 6-15 Example of Data Dictionary Entry for Entity 6-16 Example of Data Dictionary Entry for Attributes 6-17 Example of Data Dictionary of Entry for Relationship 6-18 Types of Metadata Captured by the Data Dictionary 6-19 6-20 © Copyright 2011 John Wiley & Sons, Inc. 6-21  CREATING AN ENTITY RELATIONSHIP DIAGRAM (ERD) Drawing the ERD is an iterative process of trial and revision. The basic steps in building an ERD: 1. Identify the entities; 2. add the appropriate attributes to each entity; and 3. draw relationships among entities. © Copyright 2011 John Wiley & Sons, Inc. 6-22  Step 1: Identify the Entities  The entities should represent the major categories of information that you need to store in your system.  If you begin the data model using a use case, look at the major inputs and outputs of the use case.  If the process models are available, look at the data stores, external entities, and data flows. © Copyright 2011 John Wiley & Sons, Inc. 6-23  Step 2: Add Attributes and Assign Identifiers  The information that describes each entity becomes its attributes. – Check in the CASE repository of the process model for details on data flows and data stores. – Check the data requirements of the requirements definition. – Use requirements elicitation techniques (e.g., interview and document analysis).  One or more of the attributes will become the entity’s identifier. © Copyright 2011 John Wiley & Sons, Inc. 6-24  Step3: Identify Relationships The last step in creating ERDs is to determine how the entities are related to each other. Lines are drawn between the entities that have relationships. Each relationship is labeled, and cardinality and modality are assigned. © Copyright 2011 John Wiley & Sons, Inc. 6-25  Advanced Syntax  Three special types of entities:  Independent Entity – Can exist without the help of another entity. – The identifiers is created from the entity’s own attributes. – Attributes from other entities are not needed to uniquely identify instances of these entities. © Copyright 2011 John Wiley & Sons, Inc. 6-26  (cont’d)  Dependent Entity – There are situations when a child entity does require attributes from the parent entity to uniquely identify an instance. In these cases, the child entity is called a dependent entity, and its identifier consists of at least one attributes from the parent entity. (E.g., the Chemical Request entity in Figure 6.1). © Copyright 2011 John Wiley & Sons, Inc. 6-27 © Copyright 2011 John Wiley & Sons, Inc. 6-28 (cont’d)  Intersection Entity –It exists in order to capture some information about the relationship between two other entities. Typically, intersection entities are added to a data model to store information about two entities sharing an M : N relationship. © Copyright 2011 John Wiley & Sons, Inc. 6-29 (cont’d)  There are three steps involved in adding an intersection entity: Step 1. Remove the M:N relationship line and insert a new entity (intersection entity) in between the two existing ones. Step 2. Add two 1:N relationships to the model. Step 3. Name the intersection entity. © Copyright 2011 John Wiley & Sons, Inc. 6-30 Resolving an M:N Relationship 6-31  VALIDATING AN ERD  General design guidelines.  Normalization. Check the ERD against the process models to make sure that both model balance each other. © Copyright 2011 John Wiley & Sons, Inc. 6-32 Design Guidelines  Normalization Normalization is a technique that can help analysts validate the data models. It is a process whereby a series of rules are applied to a logical data model to determine how well formed it is. © Copyright 2011 John Wiley & Sons, Inc. 6-34 Normalization Steps © Copyright 2011 John Wiley & Sons, Inc. 6-35 (cont’d)- part of 2NF © Copyright 2011 John Wiley & Sons, Inc. 6-40  6- (cont’d)-part of 3NF  Balancing ERDs with DFDs  The process models and data models are interrelated.  Although the process model focuses on the business processes, it contains two data components – the data and the data store.  These two data components of the DFDs need to balance with the ERDs.  The DFD data components need to correspond with the ERD’s data stores (i.e., entities) and the data elements that comprise the data flows (i.e., attributes) depicted on the data model. © Copyright 2011 John Wiley & Sons, Inc. 6-43 (cont’d)  Many CASE tools provide features of identifying problems with balance between DFDs and ERDs; however, it is important to understand how to identify problems on your own.  Check your DFDs and ERDs to make sure all data components correspond between DFDs and ERDs.  A useful tools to clearly depict the interrelationship between process and data models is the CRUD matrix (create, read, update, delete matrix). © Copyright 2011 John Wiley & Sons, Inc. 6-44 (cont’d)- A Portion of a DFD and the CRUD Matrix © Copyright 2011 John Wiley & Sons, Inc. 6-45 (cont’d) © Copyright 2011 John Wiley & Sons, Inc. 6-46  SUMMARY  Basic Entity Relationship Diagram Syntax - Entity describes people, places, or things. - Attribute is some type of information about the entity. - Relationship conveys the associations between entities.  Creating an ERD - Identify the entities. - Add the attributes to each entity. - Draw relationships among entities.  Validating an ERD - Normalization - CRUD matrix © Copyright 2011 John Wiley & Sons, Inc. 6-47