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Data Modeling

Bayu Adhi Tama, ST., MTI. Data Modeling. Thanks to Poniah , Elamasri , and Silberkatz. Review. DDL is usually used by database administrator (DBA) to retrieve or to update data in a DBMS. [T/F]

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Data Modeling

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  1. Bayu Adhi Tama, ST., MTI. Data Modeling Thanks to Poniah, Elamasri, and Silberkatz

  2. Review DDL is usually used by database administrator (DBA) to retrieve or to update data in a DBMS. [T/F] In a “two-tier architecture”, in order to increase transaction processing, an application Server is used beside a database server. [T/F] If the database system is not able to handle the complexity of data because of modeling limitations, it’s enough when no DBMS will use. [T/F]

  3. A model serves two primary purposes: • As a true representation of some aspects of the real world, a model enables clearer communication about those aspects of the real world. • A model serves as a blueprint to shape and construct the proposed structures in the real world. • Data modeling is an integral part of the process of designing and developing a data system. • A data model is a device that : [Poniah] • helps the users or stakeholders understand clearly the database system that is being implemented based on the information requirements of an organization, and • enables the database practitioners to implement the database system exactly conforming to the information requirements. • Data models [Silberkatz] • A collection of tools for describing : data, data relationships, data semantics, and data constraints

  4. Data model: communication tool and database blueprint.

  5. Classification of Information Levels: • Conceptual Level. The highest level consisting of general ideas about the information content • External Level. The data model represents the information requirements for the entire set of user groups in the organization

  6. Process creating data model: • Identify business objects • Identify relationships • Add attributes • Assign identifiers • Incorporate business rules • Validate the data model • Significance of data model quality • Data model completeness • Data model correctness

  7. Data models characteristic • Involves users • Covers the proper enterprise segments • Uses Accepted Standard Rules and Conventions. • Produces High-Quality Design. • Data system development lifecycle (DDLC) • Starting the process • Data-Oriented Approach • Development Framework. • Initiation Report. • Planning • Feasibility study • Requirements definition • Study overall business operations • Observes business processes • Understand business units • Interview users • Determine information requirements • Identify data to be collected and stored • Establish data access pattern and estimate data volume

  8. Design • Implementation

  9. History of Data Models • Network Model • Hierarchical Model • Relational Model • Object-oriented Data Models • Object-Relational Models

  10. History of Data Models • Network Model: • The first network DBMS was implemented by Honeywell in 1964-65 (IDS System). • Adopted heavily due to the support by CODASYL (Conference on Data Systems Languages) (CODASYL - DBTG report of 1971). • Later implemented in a large variety of systems - IDMS (Cullinet - now Computer Associates), DMS 1100 (Unisys), IMAGE (H.P. (Hewlett-Packard)), VAX -DBMS (Digital Equipment Corp., next COMPAQ, now H.P.).

  11. Example of Network Model Schema

  12. Network Model: Basic Concepts • Data are represented by collections of records. • Records and their fields are represented as record type • Relationships among data are represented by links • restrictions on links depend on whether the relationship is many-many, many-to-one, or one-to-one.

  13. Network Model • Advantages: • Network Model is able to model complex relationships and represents semantics of add/delete on the relationships. • Can handle most situations for modeling using record types and relationship types. • Language is navigational; uses constructs like FIND, FIND member, FIND owner, FIND NEXT within set, GET, etc. • Programmers can do optimal navigation through the database.

  14. Network Model • Disadvantages: • Navigational and procedural nature of processing • Database contains a complex array of pointers that thread through a set of records. • Little scope for automated “query optimization”

  15. History of Data Models • Hierarchical Data Model: • Initially implemented in a joint effort by IBM and North American Rockwell around 1965. Resulted in the IMS family of systems. • IBM’s IMS product had (and still has) a very large customer base worldwide • Hierarchical model was formalized based on the IMS system • Other systems based on this model: System 2k (SAS inc.)

  16. Hierarchical Model • Advantages: • Simple to construct and operate • Corresponds to a number of natural hierarchically organized domains, e.g., organization (“org”) chart • Language is simple: • Uses constructs like GET, GET UNIQUE, GET NEXT, GET NEXT WITHIN PARENT, etc. • Disadvantages: • Navigational and procedural nature of processing • Database is visualized as a linear arrangement of records • Little scope for "query optimization"

  17. History of Data Models • Relational Model: • Proposed in 1970 by E.F. Codd (IBM), first commercial system in 1981-82. • Now in several commercial products (e.g. DB2, ORACLE, MS SQL Server, SYBASE, INFORMIX). • Several free open source implementations, e.g. MySQL, PostgreSQL • Currently most dominant for developing database applications. • SQL relational standards: SQL-89 (SQL1), SQL-92 (SQL2), SQL-99, SQL3, …

  18. History of Data Models • Object-oriented Data Models: • Several models have been proposed for implementing in a database system. • One set comprises models of persistent O-O Programming Languages such as C++ (e.g., in OBJECTSTORE or VERSANT), and Smalltalk (e.g., in GEMSTONE). • Additionally, systems like O2, ORION (at MCC - then ITASCA), IRIS (at H.P.- used in Open OODB). • Object Database Standard: ODMG-93, ODMG-version 2.0, ODMG-version 3.0.

  19. Object Oriented Data Model • An object corresponds to an entity • The object-oriented paradigm is based on encapsulating code and data related to an object into single unit. • The object-oriented data model is a logical data model • Object structure: • A set of variables that contain the data for the object. The value of each variable is itself an object. • A set of messages to which the object responds; each message may have zero, one, or more parameters. • A set of methods, each of which is a body of code to implement a message; a method returns a value as the response to the message

  20. History of Data Models • Object-Relational Models: • Most Recent Trend. Started with Informix Universal Server. • Relational systems incorporate concepts from object databases leading to object-relational. • Exemplified in the latest versions of Oracle-10i, DB2, and SQL Server and other DBMSs. • Standards included in SQL-99 and expected to be enhanced in future SQL standards.

  21. Exercise Match the column entries

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