Data model - Knowledge

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models. For example, different modelers usually produce different conventional data models of the same domain. This can lead to difficulty in bringing the models of different people together and is an obstacle for data exchange and data integration. Invariably, however, this difference is attributable to different levels of abstraction in the models and differences in the kinds of facts that can be instantiated (the semantic expression capabilities of the models). The modelers need to communicate and agree on certain elements that are to be rendered more concretely, in order to make the differences less significant.

225: 251:: describes the semantics of a domain, being the scope of the model. For example, it may be a model of the interest area of an organization or industry. This consists of entity classes, representing kinds of things of significance in the domain, and relationship 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. 790: 1411: 539: 1206: 778: 617: 1618: 567: 839: 933: 1180: 1349: 980: 20: 525: 183: 553: 3040: 679: 1220: 581: 1192: 593: 1131: 3030: 268:

the conceptual model. In each case, of course, the structures must remain consistent with the other model. The table/column structure may be different from a direct translation of the entity classes and attributes, but it must ultimately carry out the objectives of the conceptual entity class structure. Early phases of many software development projects emphasize the design of a

1472: 1070:, but in the original ANSI three schema architecture, it is called "logical". In that architecture, the physical model describes the storage media (cylinders, tracks, and tablespaces). Ideally, this model is derived from the more conceptual data model described above. It may differ, however, to account for constraints like processing capacity and usage patterns. 1425:

can be performed on them. The entity types in the model may be kinds of real-world objects, such as devices in a network, or they may themselves be abstract, such as for the entities used in a billing system. Typically, they are used to model a constrained domain that can be described by a closed set of entity types, properties, relationships and operations.

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The significance of this approach, according to ANSI, is that it allows the three perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual model. The table/column structure can change without (necessarily) affecting

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A data architecture describes the data structures used by a business and/or its applications. There are descriptions of data in storage and data in motion; descriptions of data stores, data groups, and data items; and mappings of those data artifacts to data qualities, applications, locations, etc.

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at the conceptual level. The quality of a database application depends critically on its design. To help ensure correctness, clarity, adaptability and productivity, information systems are best specified first at the conceptual level, using concepts and language that people can readily understand.

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An Information model is not a type of data model, but more or less an alternative model. Within the field of software engineering, both a data model and an information model can be abstract, formal representations of entity types that include their properties, relationships and the operations that

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for a dedicated artificial language for that domain. A data model represents classes of entities (kinds of things) about which a company wishes to hold information, the attributes of that information, and relationships among those entities and (often implicit) relationships among those attributes.

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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

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A semantic data model in software engineering is a technique to define the meaning of data within the context of its interrelationships with other data. A semantic data model is an abstraction that defines how the stored symbols relate to the real world. A semantic data model is sometimes called a

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by providing the definition and format of data. According to West and Fowler (1999) "if this is done 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. The results of this

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and relationships found in a particular application domain: for example the customers, products, and orders found in a manufacturing organization. At other times it refers to the set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or

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are specified inside the entity boxes rather than outside of them, while relationships are drawn as boxes composed of attributes which specify the constraints that bind entities together. DSDs differ from the ER model in that the ER model focuses on the relationships between different entities,

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An information model provides formalism to the description of a problem domain without constraining how that description is mapped to an actual implementation in software. There may be many mappings of the information model. Such mappings are called data models, irrespective of whether they are

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Generic data models are generalizations of conventional data models. They define standardized general relation types, together with the kinds of things that may be related by such a relation type. Generic data models are developed as an approach to solving some shortcomings of conventional data

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is a database model based on first-order predicate logic. Its core idea is to describe a database as a collection of predicates over a finite set of predicate variables, describing constraints on the possible values and combinations of values. The power of the relational data model lies in its

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A method of data modeling that has been defined as "attribute free", and "fact-based". The result is a verifiably correct system, from which other common artifacts, such as ERD, UML, and semantic models may be derived. Associations between data objects are described during the database design

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paradigm brought about a fundamental change in the way we look at data and the procedures that operate on data. Traditionally, data and procedures have been stored separately: the data and their relationship in a database, the procedures in an application program. Object orientation, however,

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compared a data model to a map of a territory, emphasizing that in the real world, "highways are not painted red, rivers don't have county lines running down the middle, and you can't see contour lines on a mountain". In contrast to other researchers who tried to create models that were

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This model organizes data using two fundamental constructs, called records and sets. Records contain fields, and sets define one-to-many relationships between records: one owner, many members. The network data model is an abstraction of the design concept used in the implementation of

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are specified inside the entity boxes rather than outside of them, while relationships are drawn as lines, with the relationship constraints as descriptions on the line. The E-R model, while robust, can become visually cumbersome when representing entities with several attributes.

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are indicated above. However, systems and interfaces often cost more than they should, to build, operate, and maintain. They may also constrain the business rather than support it. A major cause is that the quality of the data models implemented in systems and interfaces is poor".

305:, an IT industry consortium formed in 1959, who essentially aimed at the same thing as Young and Kent: the development of "a proper structure for machine-independent problem definition language, at the system level of data processing". This led to the development of a specific IS 917:

Essential to realizing the target state, Data architecture describes how data is processed, stored, and utilized in a given system. It provides criteria for data processing operations that make it possible to design data flows and also control the flow of data in the system.

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An object model in computer science is a collection of objects or classes through which a program can examine and manipulate some specific parts of its world. In other words, the object-oriented interface to some service or system. Such an interface is said to be the

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This may not strictly qualify as a data model. The flat (or table) model consists of a single, two-dimensional array of data elements, where all members of a given column are assumed to be similar values, and all members of a row are assumed to be related to one

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There are several styles for representing data structure diagrams, with a notable difference in the manner of defining cardinality. The choices are between arrow heads, inverted arrow heads (crow's feet), or numerical representation of the cardinality.

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A data model explicitly determines the structure of data. Typical applications of data models include database models, design of information systems, and enabling exchange of data. Usually, data models are specified in a data modeling language.

208:"Data cannot be shared electronically with customers and suppliers, because the structure and meaning of data has not been standardized. For example, engineering design data and drawings for process plant are still sometimes exchanged on paper". 512:

The simplest style of data warehouse schema. The star schema consists of a few "fact tables" (possibly only one, justifying the name) referencing any number of "dimension tables". The star schema is considered an important special case of the

199:"Business rules, specific to how things are done in a particular place, are often fixed in the structure of a data model. This means that small changes in the way business is conducted lead to large changes in computer systems and interfaces". 1640:

The conceptual design may include data, process and behavioral perspectives, and the actual DBMS used to implement the design might be based on one of many logical data models (relational, hierarchic, network, object-oriented, etc.).

205:"Data models for different systems are arbitrarily different. The result of this is that complex interfaces are required between systems that share data. These interfaces can account for between 25-70% of the cost of current systems". 202:"Entity types are often not identified, or incorrectly identified. This can lead to replication of data, data structure, and functionality, together with the attendant costs of that duplication in development and maintenance". 1395:

is designed to show how a system is divided into smaller portions and to highlight the flow of data between those parts. This context-level data-flow diagram is then "exploded" to show more detail of the system being modeled

301:". Their work was the first effort to create an abstract specification and invariant basis for designing different alternative implementations using different hardware components. The next step in IS modeling was taken by 1142:

A data structure is a way of storing data in a computer so that it can be used efficiently. It is an organization of mathematical and logical concepts of data. Often a carefully chosen data structure will allow the most

232:. This shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models. 158:. Data models describe the structure, manipulation, and integrity aspects of the data stored in data management systems such as relational databases. They may also describe data with a looser structure, such as 288:

One of the earliest pioneering works in modeling information systems was done by Young and Kent (1958), who argued for "a precise and abstract way of specifying the informational and time characteristics of a

67:. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. 257:: describes the semantics, as represented by a particular data manipulation technology. This consists of descriptions of tables and columns, object oriented classes, and XML tags, among other things. 1157:

A data model describes the structure of the data within a given domain and, by implication, the underlying structure of that domain itself. This means that a data model in fact specifies a dedicated

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mathematically clean and elegant, Kent emphasized the essential messiness of the real world, and the task of the data modeler to create order out of chaos without excessively distorting the truth.

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A data model instance is created by applying a data model theory. This is typically done to solve some business enterprise requirement. Business requirements are normally captured by a semantic

1101:} Data modeling strives to bring the data structures of interest together into a cohesive, inseparable, whole by eliminating unnecessary data redundancies and by relating data structures with 967:. The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects. This is then used as the start point for interface or 879:

physical data stores. A semantic data model is an abstraction that defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world.

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or other data management technology. It describes, for example, relational tables and columns or object-oriented classes and attributes. Such a data model is sometimes referred to as the

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The entities represented by a data model can be the tangible entities, but models that include such concrete entity classes tend to change over time. Robust data models often identify

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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

1309:. This is transformed into a physical data model instance from which is generated a physical database. For example, a data modeler may use a data modeling tool to create an 465:

The hierarchical model is similar to the network model except that links in the hierarchical model form a tree structure, while the network model allows arbitrary graph.

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for a chosen domain of discourse. It can provide sharable, stable, and organized structure of information requirements for the domain context. More in general the term

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is the process of creating a data model by applying formal data model descriptions using data modeling techniques. Data modeling is a technique for defining business

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Data architecture is the design of data for use in defining the target state and the subsequent planning needed to hit the target state. It is usually one of several

316:(MIS) concept. According to Leondes (2002), "during that time, the information system provided the data and information for management purposes. The first generation 1169:

of such entities. For example, a data model might include an entity class called "Person", representing all the people who interact with an organization. Such an

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Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of

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tables. So the "data model" of a banking application may be defined using the entity–relationship "data model". This article uses the term in both senses.

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released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST). 21 December 1993 (withdrawn in 2008).

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whereas DSDs focus on the relationships of the elements within an entity and enable users to fully see the links and relationships between each entity.

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The manipulation part: a collection of operators which can be applied to the data structures, to update and query the data contained in the database.

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The structural part: a collection of data structures which are used to create databases representing the entities or objects modeled by the database.

421:. They focused more on the communication part of the semantics. In 1997 they formalized the method Fully Communication Oriented Information Modeling 417:

During the early 1990s, three Dutch mathematicians Guido Bakema, Harm van der Lek, and JanPieter Zwart, continued the development on the work of

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is used for models of individual things, such as facilities, buildings, process plants, etc. In those cases the concept is specialised to

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An entity–relationship model (ERM), sometimes referred to as an entity–relationship diagram (ERD), could be used to represent an abstract

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The reason for these problems is a lack of standards that will ensure that data models will both meet business needs and be consistent.

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The model describes the organization of the data to some extent irrespective of how data might be represented in a computer system.

82:. Data models are typically specified by a data expert, data specialist, data scientist, data librarian, or a data scholar. A data 1271:

The integrity part: a collection of rules governing the constraints placed on these data structures to ensure structural integrity.

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class is typically more appropriate than ones called "Vendor" or "Employee", which identify specific roles played by those people.

398:(ORM). However, it was Terry Halpin's 1989 PhD thesis that created the formal foundation on which Object–Role Modeling is based. 2375: 1746: 1671:

of a software-intensive system. The Unified Modeling Language offers a standard way to write a system's blueprints, including:

3033: 2899: 2828: 263:: describes the physical means by which data are stored. This is concerned with partitions, CPUs, tablespaces, and the like. 390:

developed "Natural Language Information Analysis Method" (NIAM) method, and developed this in the 1980s in cooperation with

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There are several styles for representing data structure diagrams, with the notable difference in the manner of defining

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According to Lee (1999) an information model is a representation of concepts, relationships, constraints, rules, and

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Example of the application of Object–Role Modeling in a "Schema for Geologic Surface", Stephen M. Richard (1999)

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can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of the

2858: 2785: 2775: 2620: 2549: 755: 2101:"The Data Model Resource Book: Universal Patterns for Data Modeling" Len Silverstone & Paul Agnew (2008). 3043: 2909: 2838: 2780: 2502: 2371: 1461: 1445: 1310: 737: 696: 672: 656: 652:, representing relationships. Data structure diagrams are most useful for documenting complex data entities. 352: 313: 538: 2848: 2707: 2574: 2458: 1719: 1077:

is a common term for data modeling, the activity actually has more in common with the ideas and methods of

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is data organized according to an explicit data model or data structure. Structured data is in contrast to

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Similar to a relational database model, but objects, classes, and inheritance are directly supported in

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W. Stevens, G. Myers, L. Constantine, "Structured Design", IBM Systems Journal, 13 (2), 115-139, 1974.

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is a mathematical construct for representing geographic objects or surfaces as data. For example,

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of the corporate data repository of some business enterprise. This model is transformed into a

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Young, J. W., and Kent, H. K. (1958). "Abstract Formulation of Data Processing Problems". In:

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A data-flow diagram (DFD) is a graphical representation of the "flow" of data through an

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to be stored, and is of primary use when the final product is the generation of computer

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the raster data model represents geography as cell matrixes that store numeric values;

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A database model is a specification describing how a database is structured and used.

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to represent structured data. There are several notations used for ERMs. Like DSD's,

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emerged as a new type of conceptual data modeling, originally formalized in 1976 by

2990: 2934: 2712: 2604: 2599: 2473: 2381: 2058: 1951:"Derivability, Redundancy, and Consistency of Relations Stored in Large Data Banks" 1698: 1676: 1633: 1599:. There is an extensive literature on formalized object models as a subset of the 1532: 1314: 1279: 1102: 964: 867: 731: 637: 616: 572: 514: 477: 341: 102: 2085:

Beynon-Davies P. (2004). Database Systems 3rd Edition. Palgrave, Basingstoke, UK.

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to be used. The choice of the data structure often begins from the choice of an

1020:: the availability of data at the time required and how up-to-date that data is. 2914: 2818: 2559: 2468: 2361: 2319: 1702: 1535:

Telescope Driver is an object model for controlling an astronomical telescope.

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first which shows the interaction between the system and outside entities. The

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In the 1960s data modeling gained more significance with the initiation of the

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Patterns are common data modeling structures that occur in many data models.

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Some important properties of data for which requirements need to be met are:

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In the 1980s, according to Jan L. Harrington (2000), "the development of the

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Database and Data Communication Network Systems: Techniques and Applications

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object model for controlling Microsoft Excel from another program, and the

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The figure illustrates the way data models are developed and used today. A

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for the application that is being developed, perhaps in the context of an

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procedure, such that normalization is an inevitable result of the process.

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make-or-buy decision. The figure is an example of the interaction between

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ANSI/X3/SPARC Study Group on Data Base Management Systems; Interim Report

1555: 1520: 1211: 1166: 1135: 1083:(inferring general concepts from particular instances) than it does with 979: 945: 598: 507: 368: 280:. However, it is also possible to implement a conceptual model directly. 24: 19: 2939: 2494: 2478: 1465: 1245:, i.e. a formal description of how data may be structured and accessed. 1197: 678: 356: 182: 1963: 1809: 1655:

The Unified Modeling Language (UML) is a standardized general-purpose

1539: 1361: 1147: 1009:: the compatibility of the same type of data from different sources. 2687: 1516: 372: 359:. Entity–relationship models were being used in the first stage of 293:

problem". They wanted to create "a notation that should enable the

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how they relate to one another and to the properties of real-world

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The Data Model Resource Book: Universal Patterns for data Modeling

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flow of the program. A data-flow diagram can also be used for the

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Another kind of data model describes how to organize data using a

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and notation are often represented in graphical form as diagrams.

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The National Geologic Map Database Project: Overview and Progress

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programs to examine and dynamically change the page. There is a

1003:: the availability of a clear and shared definition for the data. 625: 302: 1629:, and can be used as a tool for information and rules analysis. 447:

Several such models have been suggested. Common models include:

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for visualizing, specifying, constructing, and documenting the

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Object–Role Modeling is a fact-oriented method for performing

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because a data model is eventually implemented in a database.

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data model represents geography as points, lines, and polygons

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worked out his theories of data arrangement, and proposed the

2744: 2264: 997:: the usefulness of the data in the context of your business. 671:. The choices are between arrow heads, inverted arrow heads ( 649: 190:

The main aim of data models is to support the development of

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The corresponding professional activity is called generally

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has a distinct second meaning of the general properties of

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Paul R. Smith & Richard Sarfaty Publications, LLC 2009

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mathematical foundations and a simple user-level paradigm.

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entity–relationship diagrams used to model IDEF1X itself

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Conceptual Modelling in Information Systems Engineering

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that bind them. The basic graphic elements of DSDs are

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Object Role Modeling: An Overview (msdn.microsoft.com)

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that can autonomously create implicit models of data.

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Grady Booch, Ivar Jacobson & Jim Rumbaugh (2005)

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the represented service or system. For example, the

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at General Electric. Two famous database models, the

276:. In later stages, this model may be translated into 2137:"Information modeling from design to implementation" 1644: 675:), or numerical representation of the cardinality. 1138:, a simple type of branching linked data structure 1031:properties related to both definition and content 2184:. U.S. Geological Survey Open-File Report 99–386. 1979:Object-oriented Database Design Clearly Explained 1290:and the manipulation part is expressed using the 3056: 655:Data structure diagrams are an extension of the 632:by providing graphical notations which document 2139:National Institute of Standards and Technology. 2041:U.S. Geological Survey Open-File Report 03–471. 1264:A data model theory has three main components: 414:combined an entity's procedure with its data." 89:A data model can sometimes be referred to as a 2510: 2280: 2112:Introduction to Systems Engineering Practices 1888:American National Standards Institute. 1975. 1037:: how much of the required data is available. 367:to describe information needs or the type of 2193:Joachim Rossberg and Rickard Redler (2005). 2037:David R. Soller1 and Thomas M. Berg (2003). 1515:is a collection of objects that represent a 690: 297:to organize the problem around any piece of 150:Managing large quantities of structured and 2237:Data Model Patterns: Conventions of Thought 2220:OMG Unified Modeling Language Specification 1625:Object–Role Modeling (ORM) is a method for 1237:The term data model can have two meanings: 31:for an application or the preparation of a 2517: 2503: 2287: 2273: 2195:Pro Scalable .NET 2.0 Application Designs. 1993: 1991: 1989: 1987: 1836:"A Conceptual Data Model of Datum Systems" 177: 1601:formal semantics of programming languages 605: 170:, for example, provides a data model for 2708:Software development process/methodology 2524: 2250:Len Silverston & Paul Agnew (2008). 2240:. New York:Dorset House Publishers, Inc. 2131: 2129: 2033: 2031: 2029: 2027: 1616: 1470: 1409: 1347: 1129: 978: 931: 837: 771:Groups relate to process of making a map 725: 677: 615: 223: 181: 97:. Data models are often complemented by 18: 1984: 1913: 1911: 1875:Matthew West and Julian Fowler (1999). 1871: 1606: 948:for a database. It is sometimes called 272:. Such a design can be detailed into a 240:may be one of three kinds according to 108:A data model explicitly determines the 3057: 2095: 1953:, E.F. Codd, IBM Research Report, 1969 1869: 1867: 1865: 1863: 1861: 1859: 1857: 1855: 1853: 1851: 1810:"UML Domain Modeling - Stack Overflow" 827: 166:, pictures, digital audio, and video: 2498: 2268: 2212: 2174: 2126: 2044: 2024: 1971: 1286:; the integrity part is expressed in 1026:: how close to the truth the data is. 812: 219: 3029: 2723:Software verification and validation 2626:Component-based software engineering 1908: 1828: 1399: 1337: 1232: 1055: 887: 624:A data structure diagram (DSD) is a 2259:Developing High Quality Data Models 2200: 2063:Systems Analysis and Design Methods 1892:. FDT (Bulletin of ACM SIGMOD) 7:2. 1877:Developing High Quality Data Models 1848: 1768: 620:Example of a Data Structure Diagram 13: 2247:Volume 1/2. John Wiley & Sons. 2228: 1747:Distributional–relational database 974: 375:. This technique can describe any 14: 3076: 2703:Software configuration management 2570:Search-based software engineering 2555:Experimental software engineering 1905:. Nov-Dec 1958. 9(6), pp. 471-479 1903:Journal of Industrial Engineering 1686:statements, database schemas, and 1558:that uses them. For example, the 1332: 1256:to create a practical data model 1119: 983:Some important properties of data 433: 344:for database management based on 3039: 3038: 3028: 2484:Data Format Description Language 2254:Volume 3. John Wiley & Sons. 1741:Data Format Description Language 1645:Unified Modeling Language models 1387:It is common practice to draw a 1260:for some particular application. 1218: 1204: 1190: 1178: 921: 854:The logical data structure of a 807:Representing 3D map information 800: 795:NGMDB databases linked together 788: 776: 764: 709:or physical data model) used in 628:and data model used to describe 591: 579: 565: 551: 537: 523: 2187: 2153: 2142: 2117: 2104: 2010: 1956: 1944: 1493: 1389:context-level data-flow diagram 1108:A different approach is to use 959:is developed based on the data 186:How data models deliver benefit 101:, especially in the context of 93:, especially in the context of 2550:Empirical software engineering 2294: 2209:. Retrieved 19 September 2008. 1931: 1895: 1882: 1802: 1777: 991:definition-related properties 783:NGMDB data model applications 756:Triangulated irregular network 738:Geographic information systems 1: 2016:Wade, T. and Sommer, S. eds. 1937:Cornelius T. Leondes (2002). 1762: 1727:, any standardised data model 1252:, i.e. applying a data model 1091:Presumably we call ourselves 870:, cannot totally satisfy the 648:, representing entities, and 314:management information system 2575:Site reliability engineering 2459:Core architecture data model 2245:The Data Model Resource Book 2161:"ASCOM General Requirements" 1720:Core architecture data model 1317:, which in turn generates a 902:that form the pillars of an 401:Bill Kent, in his 1978 book 353:entity–relationship modeling 7: 2580:Social software engineering 2180:Stephen M. Richard (1999). 2149:Excel Object Model Overview 1834:Michael R. McCaleb (1999). 1708: 1511:Document Object Model (DOM) 1324: 1014:content-related properties 346:first-order predicate logic 131: 55:that organizes elements of 10: 3081: 2718:Software quality assurance 1977:Jan L. Harrington (2000). 1675:Conceptual things such as 1648: 1610: 1554:, technology, notation or 1497: 1462:entity–relationship models 1446:Building Information Model 1442:Facility Information Model 1403: 1341: 1123: 1114:artificial neural networks 1062:database management system 925: 891: 856:database management system 831: 816: 729: 694: 609: 495:and in the query language. 437: 371:that is to be stored in a 283: 3024: 2983: 2948: 2887: 2801: 2794: 2753: 2613: 2532: 2454:Business process modeling 2441: 2433:Unified Modeling Language 2405: 2372:Entity–relationship model 2354: 2328: 2302: 2182:Geologic Concept Modeling 1651:Unified Modeling Language 1352:Data-Flow Diagram example 1311:entity–relationship model 936:The data modeling process 882: 697:Entity–relationship model 691:Entity–relationship model 657:entity–relationship model 154:is a primary function of 2874:Model-driven engineering 2673:Functional specification 2656:Software incompatibility 2565:Requirements engineering 1682:Concrete things such as 428: 230:three level architecture 33:functional specification 2668:Enterprise architecture 2243:Len Silverston (2001). 1785:"What is a Data Model?" 1550:in a specific computer 1095:because no one can say 904:enterprise architecture 586:Concept-oriented model 487:Object–relational model 334:hierarchical data model 324:(IDS), was designed by 178:The role of data models 76:or, more specifically, 2879:Round-trip engineering 2636:Backward compatibility 2631:Software compatibility 2367:Data structure diagram 2110:John Azzolini (2000). 1731:Data collection system 1715:Business process model 1622: 1490: 1421: 1360:. It differs from the 1353: 1226:Stack (data structure) 1139: 984: 937: 843: 687: 630:conceptual data models 621: 612:Data structure diagram 606:Data structure diagram 233: 187: 44: 2698:Software architecture 2651:Forward compatibility 2234:David C. Hay (1996). 1620: 1476:Document Object Model 1474: 1413: 1351: 1284:mathematical relation 1133: 982: 957:conceptual data model 935: 908:solution architecture 849:conceptual data model 841: 726:Geographic data model 703:conceptual data model 681: 659:(ER model). In DSDs, 619: 365:requirements analysis 322:Integrated Data Store 270:conceptual data model 249:Conceptual data model 227: 185: 95:programming languages 22: 2996:Computer engineering 2693:Software archaeology 2683:Programming paradigm 2595:Software maintenance 2540:Computer programming 2526:Software engineering 2464:Enterprise modelling 2428:Object–role modeling 2257:Matthew West (2011) 2135:Y. Tina Lee (1999). 1998:FIPS Publication 184 1816:. Stack Exchange Inc 1697:UML offers a mix of 1684:programming language 1679:and system functions 1661:software engineering 1613:Object–role modeling 1607:Object–role modeling 1574:the object model of 1552:programming language 1368:flow instead of the 1278:For example, in the 942:software engineering 900:architecture domains 842:Semantic data models 711:software engineering 499:Object–role modeling 396:Object–Role Modeling 125:semi-structured data 3016:Systems engineering 3001:Information science 2781:Service orientation 2733:Structured analysis 2641:Compatibility layer 2585:Software deployment 2051:Whitten, Jeffrey L. 1928:et al. eds. pp 1-18 1918:Janis A. Bubenko jr 1701:, data models, and 1691:software components 1627:conceptual modeling 1319:relational database 1097:systems synthesists 1067:physical data model 834:Semantic data model 828:Semantic data model 707:semantic data model 307:information algebra 278:physical data model 261:Physical data model 192:information systems 156:information systems 3006:Project management 2771:Object orientation 2738:Essential analysis 2646:Compatibility mode 2003:2013-12-03 at the 1841:2008-09-21 at the 1677:business processes 1665:graphical language 1623: 1491: 1422: 1358:information system 1354: 1307:logical data model 1292:relational algebra 1152:abstract data type 1140: 985: 938: 844: 819:Generic data model 813:Generic data model 688: 622: 545:Hierarchical model 461:Hierarchical model 363:design during the 361:information system 330:network data model 274:logical data model 255:Logical data model 234: 220:Three perspectives 188: 45: 3052: 3051: 2979: 2978: 2920:Information model 2824:Incremental model 2678:Modeling language 2492: 2491: 2418:Information model 2413:Data-flow diagram 2055:Lonnie D. Bentley 1725:Common data model 1699:functional models 1657:modeling language 1482:for representing 1438:information model 1419:information model 1406:Information model 1400:Information model 1382:Larry Constantine 1344:Data-flow diagram 1338:Data-flow diagram 1288:first-order logic 1233:Data model theory 1056:Data organization 950:database modeling 940:Data modeling in 894:Data architecture 888:Data architecture 403:Data and Reality, 152:unstructured data 119:unstructured data 110:structure of data 103:enterprise models 84:modeling language 37:computer software 3072: 3042: 3041: 3032: 3031: 2991:Computer science 2799: 2798: 2713:Software quality 2605:Systems analysis 2600:Software testing 2519: 2512: 2505: 2496: 2495: 2474:Process modeling 2289: 2282: 2275: 2266: 2265: 2223: 2216: 2210: 2204: 2198: 2191: 2185: 2178: 2172: 2171: 2169: 2168: 2157: 2151: 2146: 2140: 2133: 2124: 2121: 2115: 2108: 2102: 2099: 2093: 2083: 2074: 2059:Kevin C. Dittman 2048: 2042: 2035: 2022: 2014: 2008: 1995: 1982: 1975: 1969: 1968: 1965:Data and Reality 1960: 1954: 1948: 1942: 1935: 1929: 1915: 1906: 1899: 1893: 1886: 1880: 1873: 1846: 1832: 1826: 1825: 1823: 1821: 1806: 1800: 1799: 1797: 1795: 1781: 1775: 1772: 1659:in the field of 1634:systems analysis 1364:as it shows the 1315:relational model 1280:relational model 1222: 1208: 1194: 1182: 1110:adaptive systems 1093:systems analysts 858:(DBMS), whether 804: 792: 780: 768: 736:A data model in 732:Data model (GIS) 595: 583: 573:Relational model 569: 555: 541: 527: 515:snowflake schema 493:database schemas 478:Relational model 381:area of interest 342:relational model 43:and data models. 3080: 3079: 3075: 3074: 3073: 3071: 3070: 3069: 3055: 3054: 3053: 3048: 3020: 3011:Risk management 2975: 2944: 2883: 2864:Waterfall model 2834:Prototype model 2829:Iterative model 2790: 2766:Aspect-oriented 2749: 2728:Software system 2609: 2590:Software design 2528: 2523: 2493: 2488: 2449:Database design 2437: 2401: 2350: 2324: 2298: 2293: 2261:Morgan Kaufmann 2231: 2229:Further reading 2226: 2217: 2213: 2205: 2201: 2192: 2188: 2179: 2175: 2166: 2164: 2159: 2158: 2154: 2147: 2143: 2134: 2127: 2122: 2118: 2109: 2105: 2100: 2096: 2084: 2077: 2065:. 6th edition. 2049: 2045: 2036: 2025: 2015: 2011: 2005:Wayback Machine 1996: 1985: 1976: 1972: 1962: 1961: 1957: 1949: 1945: 1936: 1932: 1916: 1909: 1900: 1896: 1887: 1883: 1874: 1849: 1843:Wayback Machine 1833: 1829: 1819: 1817: 1808: 1807: 1803: 1793: 1791: 1783: 1782: 1778: 1773: 1769: 1765: 1736:Data dictionary 1711: 1703:database models 1653: 1647: 1615: 1609: 1529:Microsoft Excel 1507:object model of 1502: 1496: 1408: 1402: 1378:data processing 1346: 1340: 1335: 1327: 1300:domain calculus 1235: 1228: 1223: 1214: 1209: 1200: 1195: 1186: 1183: 1171:abstract entity 1128: 1122: 1058: 977: 975:Data properties 969:database design 930: 924: 896: 890: 885: 876:conceptual view 836: 830: 821: 815: 808: 805: 796: 793: 784: 781: 772: 769: 734: 728: 699: 693: 614: 608: 601: 596: 587: 584: 575: 570: 561: 556: 547: 542: 533: 528: 442: 436: 431: 411:object-oriented 326:Charles Bachman 318:database system 291:data processing 286: 228:The ANSI/SPARC 222: 180: 160:word processing 134: 114:structured data 99:function models 79:database design 17: 12: 11: 5: 3078: 3068: 3067: 3050: 3049: 3047: 3046: 3036: 3025: 3022: 3021: 3019: 3018: 3013: 3008: 3003: 2998: 2993: 2987: 2985: 2984:Related fields 2981: 2980: 2977: 2976: 2974: 2973: 2968: 2963: 2958: 2952: 2950: 2946: 2945: 2943: 2942: 2937: 2932: 2927: 2922: 2917: 2915:Function model 2912: 2907: 2902: 2897: 2891: 2889: 2885: 2884: 2882: 2881: 2876: 2871: 2866: 2861: 2856: 2851: 2846: 2841: 2836: 2831: 2826: 2821: 2819:Executable UML 2816: 2811: 2805: 2803: 2796: 2792: 2791: 2789: 2788: 2783: 2778: 2773: 2768: 2763: 2757: 2755: 2751: 2750: 2748: 2747: 2742: 2741: 2740: 2730: 2725: 2720: 2715: 2710: 2705: 2700: 2695: 2690: 2685: 2680: 2675: 2670: 2665: 2660: 2659: 2658: 2653: 2648: 2643: 2638: 2628: 2623: 2617: 2615: 2611: 2610: 2608: 2607: 2602: 2597: 2592: 2587: 2582: 2577: 2572: 2567: 2562: 2560:Formal methods 2557: 2552: 2547: 2542: 2536: 2534: 2530: 2529: 2522: 2521: 2514: 2507: 2499: 2490: 2489: 2487: 2486: 2481: 2476: 2471: 2469:Function model 2466: 2461: 2456: 2451: 2445: 2443: 2439: 2438: 2436: 2435: 2430: 2425: 2420: 2415: 2409: 2407: 2406:Related models 2403: 2402: 2400: 2399: 2394: 2389: 2384: 2379: 2369: 2364: 2358: 2356: 2352: 2351: 2349: 2348: 2343: 2338: 2332: 2330: 2326: 2325: 2323: 2322: 2317: 2312: 2306: 2304: 2300: 2299: 2292: 2291: 2284: 2277: 2269: 2263: 2262: 2255: 2248: 2241: 2230: 2227: 2225: 2224: 2211: 2199: 2186: 2173: 2152: 2141: 2125: 2116: 2103: 2094: 2075: 2043: 2023: 2009: 1983: 1970: 1955: 1943: 1930: 1907: 1894: 1881: 1847: 1827: 1814:Stack Overflow 1801: 1776: 1766: 1764: 1761: 1760: 1759: 1754: 1749: 1744: 1738: 1733: 1728: 1722: 1717: 1710: 1707: 1695: 1694: 1687: 1680: 1649:Main article: 1646: 1643: 1611:Main article: 1608: 1605: 1498:Main article: 1495: 1492: 1434:data semantics 1414:Example of an 1404:Main article: 1401: 1398: 1342:Main article: 1339: 1336: 1334: 1333:Related models 1331: 1326: 1323: 1296:tuple calculus 1276: 1275: 1272: 1269: 1262: 1261: 1246: 1234: 1231: 1230: 1229: 1224: 1217: 1215: 1210: 1203: 1201: 1196: 1189: 1187: 1184: 1177: 1126:Data structure 1124:Main article: 1121: 1120:Data structure 1118: 1057: 1054: 1053: 1052: 1051: 1050: 1044: 1038: 1029: 1028: 1027: 1021: 1012: 1011: 1010: 1004: 998: 976: 973: 965:activity model 926:Main article: 923: 920: 892:Main article: 889: 886: 884: 881: 832:Main article: 829: 826: 817:Main article: 814: 811: 810: 809: 806: 799: 797: 794: 787: 785: 782: 775: 773: 770: 763: 760: 759: 752: 749: 730:Main article: 727: 724: 695:Main article: 692: 689: 682:Example of an 610:Main article: 607: 604: 603: 602: 597: 590: 588: 585: 578: 576: 571: 564: 562: 557: 550: 548: 543: 536: 534: 529: 522: 519: 518: 510: 505: 501: 496: 489: 484: 480: 475: 471: 466: 463: 458: 454: 440:Database model 438:Main article: 435: 434:Database model 432: 430: 427: 285: 282: 265: 264: 258: 252: 221: 218: 210: 209: 206: 203: 200: 179: 176: 164:email messages 133: 130: 112:; conversely, 91:data structure 53:abstract model 16:Abstract model 15: 9: 6: 4: 3: 2: 3077: 3066: 3065:Data modeling 3063: 3062: 3060: 3045: 3037: 3035: 3027: 3026: 3023: 3017: 3014: 3012: 3009: 3007: 3004: 3002: 2999: 2997: 2994: 2992: 2989: 2988: 2986: 2982: 2972: 2969: 2967: 2964: 2962: 2959: 2957: 2954: 2953: 2951: 2947: 2941: 2938: 2936: 2935:Systems model 2933: 2931: 2928: 2926: 2923: 2921: 2918: 2916: 2913: 2911: 2908: 2906: 2903: 2901: 2898: 2896: 2893: 2892: 2890: 2886: 2880: 2877: 2875: 2872: 2870: 2867: 2865: 2862: 2860: 2857: 2855: 2852: 2850: 2847: 2845: 2842: 2840: 2837: 2835: 2832: 2830: 2827: 2825: 2822: 2820: 2817: 2815: 2812: 2810: 2807: 2806: 2804: 2802:Developmental 2800: 2797: 2793: 2787: 2784: 2782: 2779: 2777: 2774: 2772: 2769: 2767: 2764: 2762: 2759: 2758: 2756: 2752: 2746: 2743: 2739: 2736: 2735: 2734: 2731: 2729: 2726: 2724: 2721: 2719: 2716: 2714: 2711: 2709: 2706: 2704: 2701: 2699: 2696: 2694: 2691: 2689: 2686: 2684: 2681: 2679: 2676: 2674: 2671: 2669: 2666: 2664: 2663:Data modeling 2661: 2657: 2654: 2652: 2649: 2647: 2644: 2642: 2639: 2637: 2634: 2633: 2632: 2629: 2627: 2624: 2622: 2619: 2618: 2616: 2612: 2606: 2603: 2601: 2598: 2596: 2593: 2591: 2588: 2586: 2583: 2581: 2578: 2576: 2573: 2571: 2568: 2566: 2563: 2561: 2558: 2556: 2553: 2551: 2548: 2546: 2543: 2541: 2538: 2537: 2535: 2531: 2527: 2520: 2515: 2513: 2508: 2506: 2501: 2500: 2497: 2485: 2482: 2480: 2477: 2475: 2472: 2470: 2467: 2465: 2462: 2460: 2457: 2455: 2452: 2450: 2447: 2446: 2444: 2440: 2434: 2431: 2429: 2426: 2424: 2421: 2419: 2416: 2414: 2411: 2410: 2408: 2404: 2398: 2395: 2393: 2390: 2388: 2385: 2383: 2380: 2377: 2373: 2370: 2368: 2365: 2363: 2360: 2359: 2357: 2353: 2347: 2344: 2342: 2339: 2337: 2334: 2333: 2331: 2327: 2321: 2318: 2316: 2313: 2311: 2308: 2307: 2305: 2301: 2297: 2290: 2285: 2283: 2278: 2276: 2271: 2270: 2267: 2260: 2256: 2253: 2249: 2246: 2242: 2239: 2238: 2233: 2232: 2221: 2215: 2208: 2203: 2196: 2190: 2183: 2177: 2162: 2156: 2150: 2145: 2138: 2132: 2130: 2120: 2113: 2107: 2098: 2092: 2091:1-4039-1601-2 2088: 2082: 2080: 2072: 2071:0-256-19906-X 2068: 2064: 2060: 2056: 2052: 2047: 2040: 2034: 2032: 2030: 2028: 2021: 2020: 2013: 2006: 2002: 1999: 1994: 1992: 1990: 1988: 1980: 1974: 1967: 1966: 1959: 1952: 1947: 1940: 1934: 1927: 1926:John Krogstie 1923: 1919: 1914: 1912: 1904: 1898: 1891: 1885: 1878: 1872: 1870: 1868: 1866: 1864: 1862: 1860: 1858: 1856: 1854: 1852: 1844: 1840: 1837: 1831: 1815: 1811: 1805: 1790: 1789:princeton.edu 1786: 1780: 1771: 1767: 1758: 1757:Process model 1755: 1753: 1750: 1748: 1745: 1742: 1739: 1737: 1734: 1732: 1729: 1726: 1723: 1721: 1718: 1716: 1713: 1712: 1706: 1704: 1700: 1692: 1688: 1685: 1681: 1678: 1674: 1673: 1672: 1670: 1666: 1662: 1658: 1652: 1642: 1638: 1635: 1630: 1628: 1619: 1614: 1604: 1602: 1598: 1597:encapsulation 1594: 1590: 1586: 1582: 1578: 1577: 1571: 1569: 1564: 1562: 1557: 1553: 1549: 1545: 1541: 1536: 1534: 1530: 1526: 1522: 1518: 1514: 1512: 1508: 1501: 1489: 1485: 1481: 1478:, a standard 1477: 1473: 1469: 1467: 1463: 1459: 1455: 1454:object models 1449: 1447: 1443: 1439: 1435: 1431: 1426: 1420: 1417: 1412: 1407: 1397: 1394: 1390: 1385: 1383: 1379: 1375: 1374:visualization 1371: 1367: 1363: 1359: 1350: 1345: 1330: 1322: 1320: 1316: 1312: 1308: 1303: 1301: 1297: 1293: 1289: 1285: 1281: 1273: 1270: 1267: 1266: 1265: 1259: 1255: 1251: 1248:A data model 1247: 1244: 1241:A data model 1240: 1239: 1238: 1227: 1221: 1216: 1213: 1207: 1202: 1199: 1193: 1188: 1181: 1176: 1175: 1174: 1172: 1168: 1163: 1160: 1155: 1153: 1149: 1146: 1137: 1132: 1127: 1117: 1115: 1111: 1106: 1104: 1103:relationships 1100: 1098: 1094: 1088: 1087: 1082: 1081: 1076: 1075:data analysis 1071: 1069: 1068: 1063: 1048: 1045: 1042: 1041:accessibility 1039: 1036: 1033: 1032: 1030: 1025: 1022: 1019: 1016: 1015: 1013: 1008: 1005: 1002: 999: 996: 993: 992: 990: 989: 988: 981: 972: 970: 966: 962: 958: 953: 951: 947: 943: 934: 929: 928:Data modeling 922:Data modeling 919: 915: 911: 909: 905: 901: 895: 880: 877: 873: 869: 865: 861: 857: 852: 850: 840: 835: 825: 820: 803: 798: 791: 786: 779: 774: 767: 762: 761: 757: 753: 750: 747: 743: 742: 741: 739: 733: 723: 719: 716: 712: 708: 704: 698: 685: 680: 676: 674: 670: 665: 662: 658: 653: 651: 647: 643: 639: 638:relationships 635: 631: 627: 618: 613: 600: 594: 589: 582: 577: 574: 568: 563: 560: 559:Network model 554: 549: 546: 540: 535: 532: 526: 521: 520: 516: 511: 509: 506: 502: 500: 497: 494: 490: 488: 485: 481: 479: 476: 472: 470: 469:Network model 467: 464: 462: 459: 455: 453: 450: 449: 448: 445: 441: 426: 424: 420: 415: 412: 407: 404: 399: 397: 393: 389: 386:In the 1970s 384: 382: 378: 374: 370: 366: 362: 358: 354: 351:In the 1970s 349: 347: 343: 339: 338:Edgar F. Codd 335: 331: 327: 323: 319: 315: 310: 308: 304: 300: 296: 292: 281: 279: 275: 271: 262: 259: 256: 253: 250: 247: 246: 245: 243: 239: 236:A data model 231: 226: 217: 213: 207: 204: 201: 198: 197: 196: 193: 184: 175: 173: 169: 165: 161: 157: 153: 148: 145: 141: 140: 129: 127: 126: 121: 120: 115: 111: 106: 104: 100: 96: 92: 87: 85: 81: 80: 75: 74: 73:data modeling 68: 66: 62: 58: 54: 50: 42: 38: 34: 30: 29:software code 26: 21: 2930:Object model 2925:Metamodeling 2904: 2854:Spiral model 2754:Orientations 2423:Object model 2310:Architecture 2295: 2258: 2251: 2244: 2235: 2214: 2202: 2194: 2189: 2176: 2165:. Retrieved 2163:. 2011-05-13 2155: 2144: 2119: 2114:. July 2000. 2106: 2097: 2062: 2046: 2017: 2012: 1978: 1973: 1964: 1958: 1950: 1946: 1938: 1933: 1921: 1902: 1897: 1889: 1884: 1830: 1818:. Retrieved 1813: 1804: 1792:. Retrieved 1788: 1779: 1770: 1696: 1654: 1639: 1631: 1624: 1593:polymorphism 1573: 1570:object model 1566: 1563:object model 1559: 1544:object model 1543: 1537: 1506: 1503: 1500:Object model 1494:Object model 1480:object model 1456:(e.g. using 1450: 1437: 1427: 1423: 1392: 1386: 1369: 1365: 1355: 1328: 1304: 1277: 1263: 1257: 1253: 1249: 1242: 1236: 1167:abstractions 1164: 1158: 1156: 1141: 1107: 1090: 1084: 1078: 1074: 1072: 1065: 1059: 1046: 1040: 1035:completeness 1034: 1023: 1017: 1006: 1000: 994: 986: 961:requirements 954: 949: 946:requirements 939: 916: 912: 897: 872:requirements 860:hierarchical 853: 845: 822: 735: 720: 700: 666: 654: 623: 446: 443: 419:G.M. Nijssen 416: 408: 402: 400: 392:Terry Halpin 388:G.M. Nijssen 385: 350: 311: 287: 266: 237: 235: 214: 211: 189: 149: 138: 137: 135: 123: 117: 113: 109: 107: 88: 77: 71: 69: 61:standardizes 48: 46: 2621:Abstraction 2336:Conceptual 1589:inheritance 1556:methodology 1521:web browser 1466:XML schemas 1432:to specify 1212:Linked list 1136:binary tree 1007:consistency 673:crow's feet 669:cardinality 642:constraints 599:Star schema 508:Star schema 369:information 174:documents. 162:documents, 25:information 2940:View model 2905:Data model 2479:XML schema 2382:Geographic 2296:Data model 2197:. Page 27. 2167:2014-09-25 2061:. (2004). 2019:A to Z GIS 1820:4 February 1763:References 1663:. It is a 1523:, used by 1430:operations 1198:Hash table 1018:timeliness 868:relational 715:attributes 661:attributes 640:, and the 636:and their 531:Flat model 474:databases. 452:Flat model 357:Peter Chen 139:data model 49:data model 2949:Languages 2320:Structure 1689:Reusable 1669:artifacts 1542:the term 1540:computing 1416:EXPRESS G 1362:flowchart 1148:algorithm 1145:efficient 1080:synthesis 995:relevance 320:, called 244:in 1975: 136:The term 35:to aid a 3059:Category 3044:Category 2910:ER model 2776:Ontology 2688:Software 2614:Concepts 2442:See also 2392:Semantic 2376:enhanced 2362:Database 2346:Physical 2315:Modeling 2001:Archived 1941:. 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