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Data Models

Data Models. Overview. Why data models are important Basic data-modeling building blocks What are business rules and how do they influence database design How the major data models evolved How data models can be classified by level of abstraction. Importance of Data Models. Data models

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Data Models

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  1. Data Models

  2. Overview • Why data models are important • Basic data-modeling building blocks • What are business rules and how do they influence database design • How the major data models evolved • How data models can be classified by level of abstraction

  3. Importance of Data Models • Data models • Representations, usually graphical, of complex real-world data structures • Facilitate interaction among the designer, the applications programmer and the end user • End-users have different views and needs for data • Data model organizes data for various users

  4. Data Model Basic Building Blocks • Entity • Anything about which data will be collected/stored • Attribute • Characteristic of an entity • Relationship • Describes an association among entities • One-to-one (1:1) relationship • One-to-many (1:M) relationship • Many-to-many (M:N or M:M) relationship • Constraint • A restriction placed on the data

  5. Business Rules • Brief, precise and unambiguous descriptions of policies, procedures or principles within the organization • Apply to any organization that stores and uses data to generate information • Description of operations that help to create and enforce actions within that organization’s environment

  6. Business Rules (continued) • Must be put in writing • Must be kept up to date • Sometimes external to the organization • Must be easy to understand and widely disseminated • Describe characteristics of the data as viewed by the company

  7. Discovering Business Rules • Company managers • Policy makers • Department managers • Written documentation • Procedures • Standards • Operations manuals • Direct interviews with end users

  8. Translating Business Rules to Data Model Components • Standardize company’s view of data • Communication tool between users and designers • Allow designer to understand the nature, role and scope of data • Allow designer to understand business processes • Allow designer to develop appropriate relationship participation rules and constraints • Promote creation of an accurate data model • Nouns translate into entities • Verbs translate into relationships among entities • Relationships are bi-directional

  9. The Evolution of Data Models We will look briefly at each Model Type

  10. The Hierarchical Model • Developed in 1960s to manage large amounts of data for complex manufacturing projects • Logical structure represented as an upside-down “tree” • Hierarchical structure contains levels or segments • Depicts a set of one-to-many (1:M) relationships • Between a parent and it’s children segments • Each parent can have many children • each child has only one parent

  11. Hierarchical Model • Advantages • Many features form the foundation for current data models • Generated a large installed base of programmers • Who developed solid business applications • Disadvantages • Complex to implement • Difficult to manage • Lacks structural independence • Implementation limitations • Lack of standards (Company vs Industry or Open)

  12. The Network Model • Resembles hierarchical model • Difference child can have multiple parents • Collection of records in 1:M relationships • Set – Relationship of at least two record types • Owner – Equivalent to the hierarchical model’s parent • Member – Equivalent to the hierarchical model’s child

  13. Network Model Terms • Schema • Conceptual organization of entire database • As viewed by the database administrator • Subschema • Defines database as “seen” by the application programs • Schema Data Definition Language (DDL) • Enables database administrator to define schema components • Subschema Data Definition Language (DDL) • Allows applications to define database components to be used • Data Management Language (DML) • Defines the environment in which data can be managed • Works with the data in the database

  14. Network Model • Advantages • Represents complex data relationships better than Hierarchical Model • Improved database performance • Impose a database “industry” standard • Conference on Data Systems Languages (CODASYL) • Database Task Group (DBTG) • Disadvantages • Too cumbersome • Lack of “ad hoc” query capability • Put heavy pressure on programmers • Any structural change in the database could produce havoc in all application programs that drew data from the database

  15. The Relational Model • Conceptually simple – Linked Tables • Developed by Edgar F. Codd (IBM 1970 ) • Considered ingenious but impractical in 1970 • Computers lacked power to implement the relational model • Today’s PCs run sophisticated relational databases

  16. Relational Model – Tables • Also called relations • Matrix of row and column intersections • Stores a collection of similar entities • Resembles a file or spreadsheet • Purely logical structure • How data are physically stored is of no concern to the user or the designer • The source of a real database revolution

  17. Relational Model – Relational Diagram • Representation of relational database’s • Entities (Tables) • Attributes within those entities (Fields) • Relationships between those entities (Links)

  18. Relational Model – RDBMS • Relational Database Management System • All the system components • User interface • Tables • Method of querying the tables • Performs same basic functions as • Hierarchical and • Network DBMS models • Plus many other functions • Most important –hides the complexities of the relational model from the user

  19. The Relational Model – SQL • Structured Query Language (SQL) • Allows ad hoc queries – questions of the data • User can specify what must be done without specifying how it must be done • Dominance due in great part to its powerful and flexible query language • SQL-based relational database application: • User interface • A set of tables stored in the database • SQL engine

  20. Entity Relationship Model (ERM) • Introduced by Peter Chen in 1976 • Widely accepted and adapted graphical tool for data modeling • Graphical representation of entities and their relationships in a database structure

  21. Entity Relationship Model – Terms • Entity Relationship Diagram (ERD) • Graphic representations to model database components • Entity is mapped to a relational table • Entity instance (or occurrence) – A row in table • Entity set (table) – Collection of like entities • Connectivity labels • Diamond connected to related entities through a relationship line • Types of relationships

  22. ERM Notation Symbols • Three symbols to represent element relationships • Ring represents "zero" • Dash represents "one" • Crow's foot represents "more" or "many" • Used in pairs to represent the four types of relationships • Ring and dash → zero or one • Dash and dash → exactly one • Ring and crow's foot → zero or more • Dash and crow's foot → one or more

  23. The Object Oriented (OO) Model • Models both data and their relationships in a single structure known as an object • Object described by its factual content • Like relational model’s entity • Includes info about relationships between facts within object and relationships with other objects • Unlike relational model’s entity

  24. Object Oriented Model – Terms • Object-oriented data model (OODM) • Semantic data model • Basis of object-oriented database management system (OODBMS) • Evolved to allow an object to also contain all operations • Object – abstraction of a real-world entity • Basic building block for autonomous structures • Attributes – properties of an object • Class - objects that share similar • Classes are organized in a class hierarchy • Inheritance – an object within the class hierarchy inherits the attributes and methods of class

  25. Extended Relational Data Model (ERDM) • Semantic data model • Developed in response to increasing complexity of applications • Based heavily on relational model • Relational DB response to OODM • Primarily geared to business applications • Typically scientific or engineering apps • Object/relational database management system (O/RDBMS) • DBMS based on the ERDM

  26. Object Role Modeling (ORM)

  27. ORM • Not to be confused with Object-relational mapping • Provides a conceptual approach to modeling • Models the application area or universe of discourse (UoD) • Relevant set of entities that are being dealt with by quantifiers • Requires a good understanding of the UoD • Means of specifying this understanding in a clear, unambiguous way • Simplifies design process with natural language and intuitive diagrams • Can be populated with examples • Evolved from the Natural language Information Analysis Method • Mid-1970s • G. M. Nijssen and Dr. Terry Halpin first joint papers in 1989 • Capable of capturing many business rules typically unsupported in other popular data modeling notations • Software tool support include Microsoft Visio for Enterprise Architects, CaseTalk, Infagon and NORMA

  28. Database Models and the Internet • Internet drastically changed role and scope of database market • Growing need to manage unstructured information • The data found in today’s: • Online documents • Web pages • Most modern DBMS incorporate Internet-age technologies such as Extended Markup Language (XML) support

  29. Data Models: Summary • Each new data model capitalized on the shortcomings of previous models • Common characteristics: • Conceptual simplicity without compromising the semantic completeness of the database • Represent the real world as closely as possible • Representation of real-world transformations (behavior) must comply with consistency and integrity characteristics of any data model

  30. Data Models: Summary

  31. Degrees of Data Abstraction • Way of classifying data models • Many processes begin at high level of abstraction • Proceed to an ever-increasing level of detail • Designing a usable database follows the same basic process

  32. Degrees of Data Abstraction • American National Standards Institute (ANSI) • Standards Planning and Requirements Committee (SPARC) • Developed standards 1970 • Framework for data modeling based on degrees of data abstraction: • External • Conceptual • Internal • Physical

  33. The External Model • Each end users’ view of the data environment • Modeler subdivides requirements and constraints into functional (Business unit’s) modules • These can be examined within the framework of their external models

  34. External Model – Advantages • Easy to identify specific data required to support each business unit’s operations • Facilitates designer’s job by providing feedback about the model’s adequacy • Creation of external models helps to identify and ensure security constraints in the database design • Simplifies application program development

  35. The Conceptual Model (1 of 2) • Global view of the entire database • Representation of data as viewed by the entire organization • Basis for identification and high-level description of main data objects, avoiding details

  36. The Conceptual Model (2 of 2) • Software and hardware independent • Independent of DBMS software • Independent of hardware to be used • Changes in either hardware or DBMS software have no effect on the database design at the conceptual level • Most widely used conceptual model is the Entity Relationship (ER) model • Provides a relatively easily understood macro level view of data environment

  37. The Internal Model • The database as “seen” by the DBMS • Maps the conceptual model to the DBMS • Depicts a specific representation of an internal model • Logical independence • Can change the internal model without affecting the conceptual model

  38. The Physical Model • Lowest level of abstraction • Describes the way data are saved on storage media such as disks or tapes • Software and hardware dependent • Requires database designers to have a detailed knowledge of the hardware and software used to implement database design • Physical independence • Can change the physical model without affecting the internal model

  39. Degrees of Data Abstraction - Summary

  40. THANK YOU

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