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Systems Analysis & Design Chapter 7 Conceptual Data Modeling ● ● ● ● ● ● Representation of organizational data Purpose is to show rules about the meaning and interrelationships among data Entity-Relationship (E-R) diagrams are commonly used to show how data are organized Main goal of conceptual data modeling is to create accurate E-R diagrams Methods such as interviewing, questionnaires and JAD are used to collect information Consistency must be maintained between process flow, decision logic and data modeling descriptions 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 ● Project repository links all design and data modeling steps performed during SDLC Deliverables and Outcome ● Primary deliverable is the entity-relationship diagram ● There may be as many as 4 E-R diagrams produced and analyzed during conceptual data modeling – Covers just data needed in the project’s application – E-R diagram for system being replaced – An E-R diagram for the whole database from which the new application’s data are extracted – An E-R diagram for the whole database from which data for the application system being replaced is drawn 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 Data Design Concepts ● Data Structures – A file or table contains data about people, places, things, or events that interact with the system – File-oriented system – File processing system – Database system Data Design Concepts ● Overview of File Processing – Potential problems • Data redundancy • Data integrity • Rigid data structure Data Design Concepts ● Overview of File Processing – Uses various types of files • • • • • • Master file Table file Transaction file Work file – scratch file Security file History file Data Design Concepts ● Overview of Database Systems – A properly designed database system offers a solution to the problems of file processing – Provides an overall framework that avoids data redundancy and supports a real-time, dynamic environment – Database management system (DBMS) – The main advantage of a DBMS is that it offers timely, interactive, and flexible data access Data Design Concepts ● Overview of Database Systems – Advantages • • • • • Scalability Better support for client/server systems Economy of scale Flexible data sharing Enterprise-wide application – database administrator (DBA) • Stronger standards Data Design Concepts ● Overview of Database Systems – Advantages • • • • Controlled redundancy Better security Increased programmer productivity Data independence Data Design Concepts ● Database Tradeoffs – Because DBMSs are powerful, they require more expensive hardware, software, and data networks capable of supporting a multi-user environment – More complex than a file processing system – Procedures for security, backup, and recovery are more complicated and critical DBMS Components ● Interfaces for Users, Database Administrators, and Related Systems – Users • Query language • Query by example (QBE) • SQL (structured query language) – Database Administrators • A DBA is responsible for DBMS management and support DBMS Components ● Interfaces for Users, Database Administrators, and Related Systems – Related information systems • A DBMS can support several related information systems that provide input to, and require specific data from, the DBMS ODBC – open database connectivity JDBC – Java database connectivity DBMS Components ● Data Manipulation Language – A data manipulation language (DML) controls database operations, including storing, retrieving, updating, and deleting data ● Schema – The complete definition of a database, including descriptions of all fields, tables, and relationships, is called a schema DBMS Components ● Physical Data Repository – The data dictionary is transformed into a physical data repository, which also contains the schema and subschemas – The physical repository might be centralized, or distributed at several locations Web-Based Database Design ● Characteristics of Web-Based Design – In a Web-based design, the Internet serves as the front end, or interface, for the database management system – Web-based systems are popular because they offer ease of access, cost-effectiveness, and worldwide connectivity Web-Based Database Design ● Data Security – Web-based data must be totally secure, yet easily accessible to authorized users – To achieve this goal, well-designed systems provide security at three levels: the database itself, the Web server, and the telecommunication links that connect the components of the system Data Design Terminology ● Definitions – Entity – Table or file – Field • Attribute • Common field – Record • Tuple Data Design Terminology ● Key Fields – Primary key • Combination key Composite key Concatenated key Multi-valued key – Candidate key • Nonkey field – Foreign key – Secondary key Data Design Terminology ● Referential Integrity – Validity checks can help avoid data input errors Entity-Relationship Diagrams ● An entity is a person, place, thing, or event for which data is collected and maintained ● Provides an overall view of the system, and a blueprint for creating the physical data structures ● Entity-relationship diagram Introduction to Entity-Relationship (E-R) Modeling ● Notation uses three main constructs – Data entities – Relationships – Attributes ● Entity-Relationship (E-R) Diagram – A detailed, logical representation of the entities, associations and data elements for an organization or businessxxxxxxxx Entity-Relationship (E-R) Modeling Key Terms ● Entity – A person, place, object, event or concept in the user environment about which the organization wishes to maintain data – Represented by a rectangle in E-R diagrams ● Entity Type – A collection of entities that share common properties or characteristics ● Attribute – A named property or characteristic of an entity that is of interest to an organization Entity-Relationship (E-R) Modeling Key Terms ● Candidate keys and identifiers – Each entity type must have an attribute or set of attributes that distinguishes one instance from other instances of the same type – Candidate key • Attribute (or combination of attributes) that uniquely identifies each instance of an entity type Entity-Relationship (E-R) Modeling Key Terms ● Identifier – – A candidate key that has been selected as the unique identifying characteristic for an entity type Selection rules for an identifier 1. Choose a candidate key that will not change its value 2. Choose a candidate key that will never be null 3. Consider substituting single value surrogate keys for large composite keys Entity-Relationship (E-R) Modeling Key Terms ● Relationship – An association between the instances of one or more entity types that is of interest to the organization – Association indicates that an event has occurred or that there is a natural link between entity types – Relationships are always labeled with verb phrases Conceptual Data Modeling and the ER Diagram ● Goal – Capture as much of the meaning of the data as possible ● Result – A better design that is easier to maintain Degree of Relationship ● Degree – Number of entity types that participate in a relationship ● Three cases – Unary • A relationship between two instances of one entity type – Binary • A relationship between the instances of two entity types – Ternary • A simultaneous relationship among the instances of three entity types • Not the same as three binary relationships Example relationships of different degrees Cardinality ● The number of instances of entity B that can be associated with each instance of entity A ● Minimum Cardinality – The minimum number of instances of entity B that may be associated with each instance of entity A ● Maximum Cardinality – The maximum number of instances of entity B that may be associated with each instance of entity A Naming and Defining Relationships ● Relationship name is a verb phrase ● Avoid vague names ● Guidelines for defining relationships – Definition explains what action is being taken and why it is important – Give examples to clarify the action – Optional participation should be explained – Explain reasons for any explicit maximum cardinality Naming and Defining Relationships ● Guidelines for defining relationships – Explain any restrictions on participation in the relationship – Explain extent of the history that is kept in the relationship – Explain whether an entity instance involved in a relationship instance can transfer participation to another relationship instancexxxxxxxxx Entity-Relationship Diagrams ● Drawing an ERD – The first step is to list the entities that you identified during the fact-finding process and to consider the nature of the relationships that link them Normalization ● Table design ● Involves four stages: unnormalized design, first normal form, second normal form, and third normal form ● Most business-related databases must be designed in third normal form Normalization ● Standard Notation Format – Designing tables is easier if you use a standard notation format to show a table’s structure, fields, and primary key Example: NAME (FIELD 1, FIELD 2, FIELD 3) Normalization ● Repeating Groups and Unnormalized Designs – Repeating group • Often occur in manual documents prepared by users – Unnormalized design Normalization ● First Normal Form – A table is in first normal form (1NF) if it does not contain a repeating group – To convert, you must expand the table’s primary key to include the primary key of the repeating group ● Second Normal Form – To understand second normal form (2NF), you must understand the concept of functional dependence – Functionally dependent Normalization ● Second Normal Form – A standard process exists for converting a table from 1NF to 2NF 1. Create and name a separate table for each field in the existing primary key 2. Create a new table for each possible combination of the original primary key fields 3. Study the three tables and place each field with its appropriate primary key Normalization ● Second Normal Form – Four kinds of problems are found with 1NF designs that do not exist in 2NF • Consider the work necessary to change a particular product’s description • 1NF tables can contain inconsistent data • Adding a new product is a problem • Deleting a product is a problem Normalization ● Third Normal Form – 3NF design avoids redundancy and data integrity problems that still can exist in 2NF designs – A table design is in third normal form (3NF) if it is in 2NF and if no nonkey field is dependent on another nonkey field – To convert the table to 3NF, you must remove all fields from the 2NF table that depend on another nonkey field and place them in a new table that uses the nonkey field as a primary key Normalization ● A Normalization Example – To show the normalization process, consider the familiar situation, which depicts several entities in a school advising system: ADVISOR, COURSE, and STUDENT Using Codes During Data Design ● Overview of Codes – Because codes often are used to represent data, you encounter them constantly in your everyday life – They save storage space and costs, reduce transmission time, and decrease data entry time – Can reduce data input errors Using Codes During Data Design ● Types of Codes 1. Sequence codes 2. Block sequence codes 3. Alphabetic codes a. Category codes b. Abbreviation codes – mnemonic codes 4. 5. 6. 7. Significant digit codes Derivation codes Cipher codes Action codes Using Codes During Data Design ● Developing a Code 1. 2. 3. 4. 5. 6. 7. 8. 9. Keep codes concise Allow for expansion Keep codes stable Make codes unique Use sortable codes Avoid confusing codes Make codes meaningful Use a code for a single purpose Keep codes consistent Steps in Database Design 1. Create the initial ERD 2. Assign all data elements to entities 3. Create 3NF designs for all tables, taking care to identify all primary, secondary, and foreign keys 4. Verify all data dictionary entries 5. After creating your final ERD and normalized table designs, you can transform them into a database Database Models ● Relational Databases – The relational model was introduced during the 1970s and became popular because it was flexible and powerful – Because all the tables are linked, a user can request data that meets specific conditions – New entities and attributes can be added at any time without restructuring the entire database Database Models ● Object-Oriented Databases – Many systems developers are using objectoriented database (OODB) design as a natural extension of the object-oriented analysis process • Object Data standard • Object Database Management Group (ODMG) • Each object has a unique object identifier Data Storage and Access ● Data storage and access involve strategic business tools ● Strategic tools for data storage and access – Data warehouse - dimensions Data Storage and Access ● Strategic tools for data storage and access – Data Mining • • • • • Increase average pages viewed per session Increase number of referred customers Reduce clicks to close Increase checkouts per visit Increase average profit per checkout Data Storage and Access ● Logical and Physical Storage – Logical storage • Characters • Data element or data item • Logical record – Physical storage • Physical record or block • Buffer • Blocking factor Data Storage and Access ● Data Storage Formats – – – – – Binary digits Bit Byte EBCDIC and ASCII Unicode Data Storage and Access ● Data Storage Formats – Binary • • • • Binary storage format Integer format Long integer format Other binary formats exist for efficient storage of exceedingly long numbers Data Storage and Access ● Selecting a Data Storage Format – In many cases, a user can select a specific data storage format – For example, when using Microsoft Office, you can store documents, spreadsheets, and databases in Unicode-compatible form by using the font called Arial Unicode MS – Best answer is it depends on the situation Data Storage and Access ● Date Fields – Most date formats now are based on the model established by the International Organization for Standardization (ISO) – Can be sorted easily and used in comparisons – Absolute date – Best method depends on how the specific date will be printed, displayed, or used in a calculation Data Control ● File and database control must include all measures necessary to ensure that data storage is correct, complete, and secure ● A well-designed DBMS must provide built-in control and security features, including subschemas, passwords, encryption, audit trail files, and backup and recovery procedures to maintain data Data Control ● ● ● ● ● ● ● ● User ID Password Permissions Encryption Backup Recovery procedures Audit log files Audit fields