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Data modeling - Wikipedia
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Implementation of one conceptual data model may require multiple logical data models. The last step in data modeling is transforming 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. 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 means of defining and analyzing data within an organization, e. Data modeling may be performed during various types of projects and in multiple phases of projects. Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time.
Whitten et al. 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. Data models provide a framework for data to be used within information systems by providing specific definition and format. If a data model is used consistently across systems then compatibility of data can be achieved.
If the same data structures are used to store and access data then different applications can share data seamlessly. The results of this are indicated 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 may occur when the quality of the data models implemented in systems and interfaces is poor. In ANSI described three kinds of data-model instance : . 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. In each case, of course, the structures must remain consistent across all schemas of the same data model.
In the context of business process integration see figure , data modeling complements business process modeling , and ultimately results in database generation. The process of designing a database involves producing the previously described three types of schemas - conceptual, logical, and physical. The database design documented in these schemas are converted through a Data Definition Language , which can then be used to generate a database. A fully attributed data model contains detailed attributes descriptions for every 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 base 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 map 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 application within the Database Management System or DBMS.
The primary reason for this cost is that these systems do not share a common data model. If data models are developed on a system by system basis, then not only is the same analysis repeated in overlapping areas, but further analysis must be performed to create the interfaces between them.
Most systems within an organization contain the same basic data, redeveloped for a specific purpose. Therefore, an efficiently designed basic data model can minimize rework with minimal modifications for the purposes of different systems within the organization . Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston  only two modeling methodologies stand out, top-down and bottom-up:. Sometimes models are created in a mixture of the two methods: by considering the data needs 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 don't make a distinction between logical and physical data models. There are several notations for data modeling.
The actual model is frequently called "Entity relationship model", because it depicts data in terms of the entities and relationships described in the data. Entity-relationship modeling is a relational schema database modeling method, used in software engineering to 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 analysis 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 i. Several techniques have been developed for the design of data models. While these methodologies guide data modelers in their work, two different people using the same methodology will often come up with very different results.
Most notable are:. Generic data models are generalizations of conventional data models.