Data modeling

Data modeling in software engineering is the process of creating a data model for an information system .


Data modeling is a process used to define and analyze data requirements needed to supporting the business processes dans le scope of Corresponding information systems in organisms. Therefore, the process of data modeling involves professional data modeling.

There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system. [2] The data requirements are INITIALLY Recorded as a conceptual data model qui est Essentially a set of technology specifications about the independent data and is used to the Chat initial requirements with the business stakeholders. The conceptual model is then translated into a logical data model, which documents can be implemented in databases. Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is the transformation of the logical data model to a physical data model that organizes the data into tables, and accounts for access, performance and storage details. Data modeling defines not just data elements, but also their structures and the relationships between them. [3]

Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard definition of data analysis.

  • To assist business analysts, programmers, testers, manual writers, IT package selectors, engineers, managers, related organizations and customers to understand and use an agreement
  • To manage data as a resource
  • For the integration of information systems
  • For designing databases / data warehouses (aka data repositories)

Data modeling may be carried out in various phases of projects. Data models are progressive; There is no such thing as the final data model for a business or application. Instead, a business model is required. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten et al. (2004) determined two types of data modeling: [4]

  • Strategic data modeling: This is a part of the creation of an information system, which defines an overall vision and architecture for information systems. Information engineering is a methodology that embraces this approach.
  • Data modeling During systems analysis: In systems analysis logical data models are created as share of the development of new databases.

Data modeling is also used as a technique for detailing business requirements for specific databases . It is sometimes called database modeling because a data model is eventually implemented in a database. [4]

Data modeling topics

Data models

Data models Provide a structure for data used Within information systems by providing good specific definition and format. If a data model is used consistently across systems then compatibility of data can be achieved. If this is the case, then the data can be seamless. The results of this are shown in the diagram. However, systems and interfaces are often expensive to build, operate, and maintain. They may also constrain the business rather than support it. This paper presents the results of the study. [1]

  • Business rules, specific to how things are done in a particular place, are often fixed in the structure of a data model. This paper presents the results of a large number of changes in computer systems and interfaces. So, business rules need to be implemented in a flexible way that does not result in complicated dependencies, rather the data model should be flexible enough so that changes in the business can be done within the data model in a relatively quick and efficient way.
  • Entity types are often not identified, or are identified incorrectly. This duplication can be used to reduce the cost of duplication and the duplication of data.
  • Data models for different systems are arbitrarily different. The result of this study is that it is not possible to use the data. These interfaces can account for between 25-70% of the cost of current systems. Required interfaces should be considered inherently while designing a data model, as a data model.
  • Data can not be shared electronically with customers and suppliers, because the structure and meaning of data has not been standardized. To obtain optimal value from an applied data model, it is very important to define the requirements of the business model. [1]

Conceptual, logical and physical schemes

The ANSI / SPARC three level architecture. A model of a model, or a physical model. This is not the only way to look at models, but it is a useful way, especially when comparing models. [1]

In 1975 ANSI introduced three kinds of data-model instance : [5]

  • Conceptual schema : describe the semantics of a domain. For example, it may be a model of the interest of an organization or of an industry. This consists of entity classes, which are types of things in the domain, and assertions about associations between pairs of entity classes. A conceptual schema specifies the kinds of facts or propositions that can be expressed using the model. In that sense, it defines the allowed expressions in an artificial “language” with a scope that is limited by the scope of the model. Simply described, a conceptual schema is the first step in organizing the data requirements.
  • Logical schema : describes the structure of some domain of information. Tables, columns, object-oriented classes, and XML tags. The logical schema and the same. [2]
  • Physical scheme : describes the physical means used to store data. This is concerned with partitions, CPUs, tablespaces , and the like.

According to ANSI, this approach allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual schema. The table / column structure can change without necessarily affecting the conceptual schema. In each case, of course, the structures must remain consistent all the schemes of the same data model.

Data modeling process

Data modeling in the context of Business Process Integration. [6]

In the context of business process integration (see figure), data modeling complements business process modeling , and ultimately results in database generation. [6]

The process of designing a database involves producing the previously described three types of schemas – conceptual, logical, and physical. The Definition Language, which can be used to generate a database. A fully attributed data model contains detailed attributes for each entity within it. The term “database design” can describe many different parts of the design of an overall database system . Principally, and most correctly, it can be thought of as the logical design of the database data structures used to store the data. In the relational model, these are the tables and views . In an object database the entities and relationships directly to object classes and named relationships. However, the term “database design” could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database within the Database Management System or DBMS.

In the process, system interfaces account for 25% to 70% of the development and support costs of current systems. The primary reason for this is that it is a common data model. If the data is not available, it should be used to create the interfaces between them. Most systems within a given organization. Therefore, an efficiently designed basic data model can minimize rework with minimal modifications for the purposes of different systems within the organization [1]

Modeling methodologies

Data models represent information areas of interest. While there are Many Ways to create data models, selon Len Silverston (1997) [7] only two modeling methodologies stand out, top-down and bottom-up:

  • Bottom-up models or View Integration models are often the result of a reengineering effort. They may start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an enterprise perspective . They may not promote data sharing, especially if they are built without reference to other parts of the organization. [7]
  • Top-down logical data models , on the other hand, are created in an abstract way by getting information from people who know the subject area. A system may not implement all the entities in a logical model, but the model serves as a reference point or template. [7]

Sometimes models are created in a mixture of the two methods: by considering the data requirements and structure of an application and by consistently referencing a subject-area model. Unfortunately, in many environments the distinction between a logical data model and a physical data model is blurred. In addition, some CASE tools do not make a distinction between logical and physical data models . [7]

Entity relationship diagrams

Main article: Entity-relationship model
Example of an IDEF1X Entity relationship diagrams used to model IDEF1X itself. 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. [8]

There are several notations for data modeling. The actual model is often called “Entity relationship model”, because it depicts data in terms of the entities and relationships described in the data . [4] An entity-relationship model (ERM) is an abstract conceptual representation of structured data. Entity-relationship modeling is a relational database modeling method, used in software engineeringto produce a type of conceptual data model (or semantic data model ) of a system, often a relational database , and its requirements in a top-down fashion.

These models are being used in the first stage of information system design during the requirements to describe information needs or the type of information that is to be stored in a database . The data modeling technique can be used to describe any ontology (ie an overview and classifications of used terms and their relationships) for a certain universe of discourse ie area of ​​interest.

Several techniques have been developed for the design of data models. While these methodologies guide the data modelers in their work, two different people using the same methodology will often come up with very different results. Most notable are:

  • Bachman diagrams
  • Barker’s rating
  • Chen’s Notation
  • Data Vault Modeling
  • Extended Backus-Naur form
  • IDEF1X
  • Object-relational mapping
  • Object-Role Modeling
  • Relational Model
  • Relational Model / Tasmania

Generic data modeling

Main article: Generic data model
Example of a Generic data model. [9]

Generic data models are generalizations of conventional data models . They define standardized general relationship types, together with the kinds of things that may be related by such a relationship type. The definition of a generic model is similar to the definition of a natural language. For example, a generic data model may define a relationship between an individual thing and a kind of thing. Two things, one with the role of part, the other with the role of whole, regardless of the kind of things that are related.

Given an extensible list of classes, this allows the classification of any individual thing and to specify part-whole relations for any individual object. By standardization of an extensible list of relation types, a generic data model allows the expression of an unlimited number of kinds of facts and will approach the capabilities of natural languages. Conventional data models, on the other hand, have a fixed and limited domain scope, because the instantiation (usage) of such a model only allows expressions of kinds of facts that are predefined in the model.

Semantic data modeling

Main article: Semantic data model

The logical data structure of a DBMS, whether hierarchical, network, or relational, can not totally satisfy the requirements for a conceptual definition of data because it is limited in scope and biased towards the implementation strategy employed by the DBMS. The results of this study are summarized in the following table.

Semantic data models. [8]

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 real representation of the real world. [8]

A semantic data model can be used to serve many purposes, such as: [8]

  • Planning of data resources
  • Building of shareable databases
  • Evaluation of vendor software
  • Integration of existing databases

The overall goal of semantic data models is to capture more meaning of data by Integrating relational concepts with More Powerful abstract concepts Known from the Artificial Intelligence field. The idea is to provide high level modeling primitives as integral parts of a model in order to facilitate the representation of real world situations. [10]

See also

  • Architectural pattern (computer science)
  • Comparison of data modeling tools
  • Data (computing)
  • Data dictionary
  • Document modeling
  • Information Management
  • Informative modeling
  • Metadata modeling
  • Three schema approach
  • Zachman Framework


This article incorporates public domain material from the National Institute of Standards and Technology website .

  1. ^ Jump up to:f Matthew West and Julian Fowler (1999). Developing High Quality Data Models . The European Process Industries STEP Technical Liaison Executive (EPISTLE).
  2. ^ Jump up to:b Simison, Graeme. C. & Witt, Graham. C. (2005). Data Modeling Essentials .3rd Edition. Morgan Kauffman Publishers. ISBN 0-12-644551-6
  3. Jump up^ Data Integration Glossary ArchivedMarch 20, 2009 at theWayback Machine., US Department of Transportation, August 2001.
  4. ^ Jump up to:c Whitten, Jeffrey L .; Lonnie D. Bentley , Kevin C. Dittman . (2004). Systems Analysis and Design Methods . 6th edition. ISBN 0-256-19906-X .
  5. Jump up^ American National Standards Institute. 1975.ANSI / X3 / SPARC Study Group on Data Base Management Systems; Interim Report. FDT (Bulletin of ACM SIGMOD) 7: 2.
  6. ^ Jump up to:b Paul R. Smith & Richard Sarfaty (1993). Creating a strategic plan for configuration management using Computer Aided Software Engineering (CASE) tools. Paper for 1993 National DOE / Contractors and Facilities CAD / CAE User’s Group.
  7. ^ Jump up to:d Len Silverston, WHInmon, Kent Graziano (2007). The Data Model Resource Book . Wiley, 1997. ISBN 0-471-15364-8 . Reviewed by Van Scott on . Accessed November 1, 2008.
  8. ^ Jump up to:d FIPS Publication 184 released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST). December 21, 1993.
  9. Jump up^ Amnon Shabo (2006). Clinical genomics data standards for pharmacogenetics and pharmacogenomics.
  10. Jump up^ “Semantic data modeling” In:Metaclasses and Their Application. Book Series Lecture Notes in Computer Science. Publisher Springer Berlin / Heidelberg. Volume Volume 943/1995.

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