IDEF1X

IDEFIX ("Integration Definition for Information Modeling") is a data modeling language for the developing of semantic data models. IDEF1X is used to produce a graphical information model which represents the structure and semantics of information within an environment or system. [http://www.itl.nist.gov/fipspubs/idef1x.doc FIPS Publication 184] released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST). 21 December 1993.] Use of the IDEF1X 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. This standard is part of the IDEF family of modeling languages in the field of software engineering, and is build on the Information Modeling Manual IDEF1.

Overview

An data modeling technique is used to model data in a standard, consistent, predictable manner in order to manage it as a resource. It can be used in projects requiring a standard means of defining and analyzing the data resources within an organization. Such projects include the incorporation of a data modeling technique into a methodology, managing data as a resource, integrating information systems, and or designing computer databases. The primary objectives of the IDEF1X standard are to ptovide:
* Means for completely understanding and analyzing an organization's data resources;
* Common means of representing and communicating the complexity of data;
* A technique for presenting an overall view of the data required to run an enterprise;
* Means for defining an application- independent view of data which can be validated by users and transformed into a physical database design; and
* A technique for deriving an integrated data definition from existing data resources.

A principal objective of IDEF1X is to support integration. The approach to integration focuses on the capture, management, and use of a single semantic definition of the data resource referred to as a “Conceptual schema.” The “conceptual schema” provides a single integrated definition of the data within an enterprise which is unbiased toward any single application of data and is independent of how the data is physically stored or accessed. The primary objective of this conceptual schema is to provide a consistent definition of the meanings and interrelationship of data which can be used to integrate, share, and manage the integrity of data. A conceptual schema must have three important characteristics. It must be:
* Consistent with the infrastructure of the business and be true across all application areas.
* Extendible, such that, new data can be defined without altering previously defined data.
* Transformable to both the required user views and to a variety of data storage and access structures.

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:
* 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”

The initial approach to IDEF information modeling (IDEF1) was published by the ICAM program in 1981, based on current research and industry needs. The theoretical roots for this approach stemmed from the early work of Edgar F. Codd on relational theory and Peter Chen on the entity-relationship model. The initial IDEF1 technique was based on the work of Dr. R.R. Brown and Mr. T.L. Ramey of Hughes Aircraft and Mr. D.S. Coleman of D. Appleton & Company, with critical review and influence by Charles Bachman, Peter Chen, Dr. M.A. Melkanoff, and Dr. G.M. Nijssen.

In 1983, the U.S. Air Force initiated the Integrated Information Support System (I2S2) project under the ICAM program. The objective of this project was to provide the enabling technology to logically and physically integrate a network of heterogeneous computer hardware and software. As a result of this project, and industry experience, the need for an enhanced technique for information modeling was recognized.

Application within industry had led to the development in 1982 of a Logical Database Design Technique (LDDT), based on the relational model, the entity-relationship model and more generalization concepts. It provided multiple levels of models and a set of graphics for representing the conceptual view of information within an enterprise. The LDDT had a high degree of overlap with IDEF1 features, introduced enhanced semantic and graphical constructs, and addressed information modeling enhancement requirements identified under the I2S2 program. Eventually a substantial subset of LDDT was combined with the methodology of IDEF1, and published by the ICAM program in 1985. This technique was called IDEF1 Extended or, simply, IDEF1X.

IDEF1X Building blocks

;Entities : The representation of a set of real or abstract things (people, objects, places, events, ideas, combination of things, etc.) that are recognized as the same type because they share the same characteristics and can participate in the same relationships.; Domains: A named set of data values (fixed, or possibly infinite in number) all of the same data type, upon which the actual value for an attribute instance is drawn. Every attribute must be defined on exactly one underlying domain. Multiple attributes may be based on the same underlying domain.; Attributes: A property or characteristic that is common to some or all of the instances of an entity. An attribute represents the use of a domain in the context of an entity. ; Keys: An attribute, or combination of attributes, of an entity whose values uniquely identify each entity instance.; Primary Keys: The candidate key selected as the unique identifier of an entity.; Foreign Keys: An attribute, or combination of attributes of a child or category entity instance whose values match those in the primary key of a related parent or generic entity instance. A foreign key results from the migration of the parent or generic entities primary key through a specific connection or categorization relationship.

;Relationships: An association between two entities or between instances of the same entity.; Connection Relationships: The number of entity instances that can be associated with each other in a relationship. See Constraint, Cardinality.; Categorization Relationships: A relationship in which instances of both entities represent the same real or abstract thing. One entity (generic entity) represents the complete set of things the other (category entity) represents a sub-type or sub-classification of those things. The category entity may have one or more characteristics, or a relationship with instances of another entity not shared by all generic entity instances. Each instance of the category entity is simultaneously an instance of the generic entity.; Non-Specific Relationships: An relationship in which an instance of either entity can be related to a number of instances of the other.

IDEF1X Topics

The Three Schema Approach

The three-schema approach in software engineering is an approach to building information systems and systems information management, that promotes the conceptual model as the key to achieving data integration. [http://www.fas.org/irp/doddir/army/strap/strpsec2.htm STRAP SECTION 2 APPROACH] . Retrieved 30 September 2008.]

A schema is a model, usually depicted by a diagram and sometimes accompanied by a language description. The three-schema approach has three types of schemas: [Mary E.S. Loomis (1987). "The Database Book". p. 26.]
* External schema for user views
* Conceptual schema integrates external schemata
* Internal schema that defines physical storage structures

At the center, the conceptual schema defines the ontology of the concepts as the users think of them and talk about them. The physical schema describes the internal formats of the data stored in the database, and the external schema defines the view of the data presented to the application programs. John F. Sowa (2004). [ "The Challenge of Knowledge Soup"] . published in: "Research Trends in Science, Technology and Mathematics Education". Edited by J. Ramadas & S. Chunawala, Homi Bhabha Centre, Mumbai, 2006.] The framework attempted to permit multiple data models to be used for external schemata. [Gad Ariav & James Clifford (1986). "New Directions for Database Systems: Revised Versions of the Papers". New York University Graduate School of Business Administration. Center for Research on Information Systems, 1986.]

Modeling Guidelines

The modeling process can be devided into five stages of model developing.

;Phase Zero - Project Initiation:These objectives of the project initiation phase include::* Project definition — a general statement of what has to be done, why, and how it will get done.:* Source material — a plan for the acquisition of source material, including indexing and filing.:* Author conventions — a fundamental declaration of the conventions (optional methods) by which the author chooses to make and manage the model.
;Phase One – Entity Definition:The objective of this phase is to identify and define the entities that fall within the problem domain being modeled. The first step in this process is the identification of entities.
;Phase Two – Relationship Definition:The objective of Phase Two is to identify and define the basic relationships between entities. At this stage of modeling, some relationships may be non-specific and will require additional refinement in subsequent phases. The primary outputs from Phase Two are::* Relationship matrix:* Relationship definitions:* Entity-level diagrams

;Phase Three - Key Definitions:The objectives of Phase Three are to::* Refine the non-specific relationships from Phase Two.:* Define key attributes for each entity.:* Migrate primary keys to establish foreign keys.:* Validate relationships and keys

;Phase Four - Attribute Definition:Phase Four is the final stage of model developing. The objectives of this plan are to::* Develop an attribute pool:* Establish attribute ownership:* Define nonkey attributes:* Validate and refine the data structure

IDEF1X Meta Model

IDEF1X can be used to model IDEF1X itself. Such meta models can be used for various purposes, such as repository design, tool design, or in order to specify the set of valid IDEF1X models. Depending on the purpose, somewhat different models result. There is no “one right model.” For example, a model for a tool that supports building models incrementally must allow incomplete or even inconsistent models. The meta model for formalization emphasizes alignment with the concepts of the formalization. Incomplete or inconsistent models are not provided for. There are two important limitations on meta models. First, they specify syntax, not semantics. Second, a meta model must be supplemented with constraints in natural or formal language. The formal theory of IDEF1X provides both the semantics and a means to precisely express the necessary constraints.

A meta model for IDEF1X is given here. The name of the view is mm. The domain hierarchy and constraints are also given. The constraints are expressed as sentences in the formal theory of the meta model. The meta model informally defines the set of valid IDEF1X models in the usual way. The meta model also formally defines the set of valid IDEF1X models in the following way. The meta model, as an IDEF1X model, has a corresponding formal theory. The semantics of the theory are defined in the standard way. That is, an interpretaion of a theory consists of a domain of individuals and a set of assignments::To each constant in the theory, an individual in the domain is assigned.:To each n-ary function symbol in the theory, an n-ary function over the domain is assigned.:To each n-ary predicate symbol in the theory, an n-ary relation over the domain is assigned.In the intended interpretation, the domain of individuals consists of views, such as production; entities, such as part and vendor; domains, such as qty_on_hand; connection relationships; category clusters; and so on. If every axiom in the theory is true in the interpretation, then the interpretation is called a model for the theory. Every model for the IDEF1X theory corresponding to the IDEF1X meta model and its constraints is a valid IDEF1X model.

See also

* CA ERwin Data Modeler
* Conceptual model (computer science)
* ER/Studio
* IDEF0
* IDEF5
* ISO 10303
* Logic Works
* Weak entity

References

Further reading

* Thomas A. Bruce (1992). "Designing Quality Databases With Idef1X Information Models". Dorset House Publishing.
* Y. Tina Lee & Shigeki Umeda (2000). [http://www.mel.nist.gov/msidlibrary/doc/idefx.pdf "An IDEF1x Information Model for a Supply Chain Simulation"] .
* U.S. Department of Interior (2005). [http://www.doi.gov/ocio/architecture/drmsg/doi_drm_overview_5_2005.doc "Data Reference Model Overview"] . May 4, 2005

External links

* [http://www.itl.nist.gov/fipspubs/idef1x.doc FIPS Publication 184] Announcing the IDEF1X Standard December 1993 by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST).
* [http://www.idef.com/idef1X.html Overview of IDEF1X] at www.idef.com
* [http://www.essentialstrategies.com/publications/modeling/idef1x.htm IDEF1X] Overview from Essential Strategies, Inc.
* [http://www.cio.gov.bc.ca/other/daf/IDEF1X_Cheat_Sheet.pdf IDEF1X "Cheat Sheet"]