Semantic data models.^[1]

Overview

The logical data structure of a database management system (DBMS), whether hierarchical, network, or relational, cannot totally satisfy the requirements for a conceptual definition of data because it is limited in scope and biased toward the implementation strategy employed by the DBMS. Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in the figure. The real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world.^[1]

According to Klas and Schrefl (1995), the "overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful abstraction concepts known from the Artificial Intelligence field. The idea is to provide high level modeling primitives as integral part of a data model in order to facilitate the representation of real world situations".^[2]

History

The need for semantic data models was first recognized by the U.S. Air Force in the mid-1970s as a result of the Integrated Computer-Aided Manufacturing (ICAM) Program. The objective of this program was to increase manufacturing productivity through the systematic application of computer technology. The ICAM Program identified a need for better analysis and communication techniques for people involved in improving manufacturing productivity. As a result, the ICAM Program developed a series of techniques known as the IDEF (ICAM Definition) Methods which included the following:^[1]

• IDEF0 used to produce a ?function model? which is a structured representation of the activities or processes within the environment or system.
• IDEF1 used to produce an ?information model? which represents the structure and semantics of information within the environment or system.
• IDEF2 used to produce a ?dynamics model? which represents the time varying behavioral characteristics of the environment or system.

Applications

A semantic data model can be used to serve many purposes. Some key objectives include:^[1]

• Planning of Data Resources: A preliminary data model can be used to provide an overall view of the data required to run an enterprise. The model can then be analyzed to identify and scope projects to build shared data resources.
• Building of Shareable Databases: A fully developed model can be used to define an application independent view of data which can be validated by users and then transformed into a physical database design for any of the various DBMS technologies. In addition to generating databases which are consistent and shareable, development costs can be drastically reduced through data modeling.
• Evaluation of Vendor Software: Since a data model actually represents the infrastructure of an organization, vendor software can be evaluated against a company?s data model in order to identify possible inconsistencies between the infrastructure implied by the software and the way the company actually does business.
• Integration of Existing Databases: By defining the contents of existing databases with semantic data models, an integrated data definition can be derived. With the proper technology, the resulting conceptual schema can be used to control transaction processing in a distributed database environment. The U.S. Air Force Integrated Information Support System (I2S2) is an experimental development and demonstration of this type of technology applied to a heterogeneous DBMS environment.

IDEF1X is the semantic data modeling technique. It is used to produce a graphical information model which represents the structure and semantics of information within an environment or system. Use of this standard permits the construction of semantic data models which may serve to support the management of data as a resource, the integration of information systems, and the building of computer databases.

See also

• Conceptual schema
• Entity-relationship model
• Information model
• Relational Model/Tasmania
• Three schema approach
• QuakeSim

References

Further reading

• Johan ter Bekke (1992). Semantic Data Modelling. Prentice Hall.
• Alfonso F. Cardenas and Dennis McLeod (1990). Research Foundations in Object-Oriented and Semantic Database Systems. Prentice Hall.
• Peter Gray, Krishnarao G. Kulkarni and, Norman W. Paton (1992). Object-Oriented Databases: A Semantic Data Model Approach. Prentice-Hall International Series in Computer Science.
• Michael Hammer and Dennis McLeod (1978). "The Semantic Data Model: a Modeling Mechanism for Data Base Applications." In: Proc. ACM SIGMOD Int?l. Conf. on Management of Data. Austin, Texas, May 31 - June 2, 1978, pp. 26-36.

External links

• Semantic Data Models Course ? Object Oriented Datatabase Systems, by Colin Jack & Colin Keillor (2008).
• Semantic Data Modeling Johan ter Bekke tribute site.

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