Structured Data Is Another Term For What?

Model that organizes elements of information and how they relate to ane another and to real-globe entities.

A data model (or datamodel)^[ane] ^[2] ^[three] ^[four] ^[5] is an abstract model that organizes elements of data and standardizes how they relate to i another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in plough, represent the color and size of the car and ascertain its possessor.

The term information model can refer to two singled-out but closely related concepts. Sometimes it refers to an abstruse formalization of the objects and relationships establish in a particular awarding domain: for example the customers, products, and orders found in a manufacturing organization. At other times it refers to the fix of concepts used in defining such formalizations: for example concepts such equally entities, attributes, relations, or tables. Then the "information model" of a banking application may be defined using the entity-relationship "data model". This article uses the term in both senses.

Overview of a information-modeling context: Information model is based on Information, Data relationship, Data semantic and Information constraint. A data model provides the details of information to be stored, and is of primary employ when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a calculator software make-or-buy determination. The figure is an example of the interaction between procedure and information models.^[half-dozen]

A information model explicitly determines the construction of data. Data models are typically specified by a information specialist, data librarian, or a digital humanities scholar in a data modeling note. These notations are oftentimes represented in graphical class.^[7]

A data model can sometimes be referred to every bit a information structure, particularly in the context of programming languages. Information models are often complemented past function models, particularly in the context of enterprise models.

Overview [edit]

Managing large quantities of structured and unstructured data is a primary part of information systems. Data models describe the construction, manipulation and integrity aspects of the data stored in data management systems such equally relational databases. They typically practice non draw unstructured information, such equally word processing documents, email messages, pictures, digital sound, and video.

The office of data models [edit]

How information models deliver benefit^[8]

The principal aim of data models is to back up the evolution of data systems past providing the definition and format of data. According to West and Fowler (1999) "if this is washed consistently across systems then compatibility of information tin can be achieved. If the aforementioned data structures are used to store and admission data then different applications tin can share information. The results of this are indicated higher up. However, systems and interfaces often cost more than they should, to build, operate, and maintain. They may also constrain the business rather than support information technology. A major cause is that the quality of the data models implemented in systems and interfaces is poor".^[eight]

"Business rules, specific to how things are done in a particular place, are often fixed in the construction of a data model. This means that small changes in the style business is conducted lead to large changes in reckoner systems and interfaces".^[8]
"Entity types are often non identified, or incorrectly identified. This tin lead to replication of data, information construction, and functionality, together with the attendant costs of that duplication in development and maintenance".^[8]
"Data models for unlike systems are arbitrarily different. The result of this is that complex interfaces are required betwixt systems that share data. These interfaces tin can account for between 25-seventy% of the cost of current systems".^[viii]
"Information cannot be shared electronically with customers and suppliers, because the structure and significant of data has not been standardized. For case, engineering design data and drawings for process establish are however sometimes exchanged on paper".^[viii]

The reason for these problems is a lack of standards that will ensure that data models volition both meet business needs and be consistent.^[8]

A data model explicitly determines the construction of data. Typical applications of data models include database models, design of information systems, and enabling exchange of information. Unremarkably data models are specified in a information modeling linguistic communication.[three]

Three perspectives [edit]

The ANSI/SPARC 3 level architecture. This shows that a data model tin be an external model (or view), a conceptual model, or a physical model. This is not the just way to look at information models, only information technology is a useful way, particularly when comparing models.^[viii]

A data model example may be 1 of three kinds according to ANSI in 1975:^[9]

Conceptual information model: describes the semantics of a domain, being the scope of the model. For example, information technology 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 tin be expressed using the model. In that sense, information technology defines the allowed expressions in an artificial 'language' with a scope that is limited by the telescopic of the model.
Logical data model: describes the semantics, as represented by a item data manipulation engineering. This consists of descriptions of tables and columns, object oriented classes, and XML tags, among other things.
Physical data model: describes the concrete means by which data are stored. This is concerned with partitions, CPUs, tablespaces, and the similar.

The significance of this arroyo, according to ANSI, is that it allows the iii perspectives to be relatively independent of each other. Storage technology can change without affecting either the logical or the conceptual model. The table/cavalcade construction can modify without (necessarily) affecting the conceptual model. In each example, of course, the structures must remain consistent with the other model. The table/column structure may exist different from a direct translation of the entity classes and attributes, merely it must ultimately conduct out the objectives of the conceptual entity class construction. Early phases of many software development projects emphasize the design of a conceptual data model. Such a blueprint tin can be detailed into a logical data model. In later stages, this model may be translated into physical information model. All the same, it is also possible to implement a conceptual model directly.

History [edit]

One of the earliest pioneering works in modeling information systems was done past Young and Kent (1958),^[ten] ^[11] who argued for "a precise and abstruse fashion of specifying the informational and time characteristics of a data processing problem". They wanted to create "a notation that should enable the annotator to organize the problem around whatever slice of hardware". Their work was the get-go effort to create an abstract specification and invariant basis for designing different alternative implementations using different hardware components. The side by side footstep in IS modeling was taken by CODASYL, an Information technology industry consortium formed in 1959, who essentially aimed at the aforementioned thing equally Young and Kent: the development of "a proper structure for motorcar-independent problem definition language, at the organisation level of data processing". This led to the development of a specific IS data algebra.^[xi]

In the 1960s data modeling gained more significance with the initiation of the management information organisation (MIS) concept. According to Leondes (2002), "during that time, the information arrangement provided the information and information for management purposes. The commencement generation database system, chosen Integrated Data Shop (IDS), was designed by Charles Bachman at Full general Electric. Ii famous database models, the network data model and the hierarchical information model, were proposed during this menses of time".^[12] Towards the end of the 1960s, Edgar F. Codd worked out his theories of information arrangement, and proposed the relational model for database management based on offset-social club predicate logic.^[13]

In the 1970s entity relationship modeling emerged as a new type of conceptual data modeling, originally proposed in 1976 by Peter Chen. Entity-relationship models were being used in the first stage of information system design during the requirements analysis to describe data needs or the type of information that is to be stored in a database. This technique can describe whatsoever ontology, i.east., an overview and classification of concepts and their relationships, for a sure expanse of interest.

In the 1970s Thou.1000. Nijssen developed "Natural Language Information Analysis Method" (NIAM) method, and adult this in the 1980s in cooperation with Terry Halpin into Object-Role Modeling (ORM). Even so, it was Terry Halpin'southward 1989 PhD thesis that created the formal foundation on which Object-Role Modeling is based.

Bill Kent, in his 1978 book Data and Reality, ^[fourteen] compared a data model to a map of a territory, emphasizing that in the real globe, "highways are not painted ruby, rivers don't have county lines running down the middle, and you can't see contour lines on a mount". In dissimilarity to other researchers who tried to create models that were mathematically clean and elegant, Kent emphasized the essential messiness of the real globe, and the task of the information modeler to create order out of chaos with out excessively distorting the truth.

In the 1980s, according to January L. Harrington (2000), "the development of the object-oriented paradigm brought nigh a central change in the way nosotros look at information and the procedures that operate on data. Traditionally, information and procedures have been stored separately: the data and their human relationship in a database, the procedures in an awarding program. Object orientation, however, combined an entity's procedure with its information."^[fifteen]

During the early on 1990s, three Dutch mathematicians Guido Bakema, Damage van der Lek, and JanPieter Zwart, connected the development on the work of G.K. Nijssen. They focused more on the communication part of the semantics. In 1997 they formalized the method Fully Advice Oriented Data Modeling FCO-IM.

Types [edit]

Database model [edit]

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

Several such models have been suggested. Common models include:

Flat model: This may non strictly qualify as a data model. The flat (or table) model consists of a unmarried, two-dimensional array of data elements, where all members of a given column are causeless to be similar values, and all members of a row are assumed to be related to one another.
Hierarchical model: The hierarchical model is similar to the network model except that links in the hierarchical model grade a tree structure, while the network model allows capricious graph.
Network model: This model organizes data using two cardinal constructs, chosen records and sets. Records comprise fields, and sets define i-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 databases.
Relational model: is a database model based on first-society predicate logic. Its cadre 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 ability of the relational data model lies in its mathematical foundations and a unproblematic user-level paradigm.
Object-relational model: Similar to a relational database model, only objects, classes and inheritance are directly supported in database schemas and in the query linguistic communication.
Object-role modeling: A method of data modeling that has been defined equally "aspect free", and "fact-based". The result is a verifiably correct organization, from which other common artifacts, such as ERD, UML, and semantic models may be derived. Associations betwixt data objects are described during the database design procedure, such that normalization is an inevitable result of the procedure.
Star schema: The simplest style of data warehouse schema. The star schema consists of a few "fact tables" (perchance just one, justifying the name) referencing any number of "dimension tables". The star schema is considered an of import special example of the snowflake schema.

Concept-oriented model

Data structure diagram [edit]

Instance of a Information Structure Diagram

A information structure diagram (DSD) is a diagram and data model used to draw conceptual data models by providing graphical notations which certificate entities and their relationships, and the constraints that bind them. The basic graphic elements of DSDs are boxes, representing entities, and arrows, representing relationships. Data construction diagrams are nigh useful for documenting complex data entities.

Information structure diagrams are an extension of the entity-human relationship model (ER model). In DSDs, attributes are specified inside the entity boxes rather than outside of them, while relationships are drawn equally 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 betwixt unlike entities, 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.

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

Example of an IDEF1X Entity relationship diagrams used to model IDEF1X itself^[16]

Entity-relationship model [edit]

An entity-relationship model (ERM), sometimes referred to as an entity-human relationship diagram (ERD), could be used to represent an abstract conceptual data model (or semantic data model or physical information model) used in software engineering to represent structured information. There are several notations used for ERMs. Like DSD's, attributes 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 Due east-R model, while robust, can become visually cumbersome when representing entities with several attributes.

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

Geographic data model [edit]

A data model in Geographic information systems is a mathematical construct for representing geographic objects or surfaces as data. For example,

the vector data model represents geography as points, lines, and polygons
the raster data model represent geography as cell matrixes that store numeric values;
and the Triangulated irregular network (TIN) data model represents geography equally sets of contiguous, nonoverlapping triangles.^[17]

Groups relate to process of making a map^[eighteen]
NGMDB data model applications^[xviii]
NGMDB databases linked together^[xviii]
Representing 3D map information^[18]

Generic data model [edit]

Generic data models are generalizations of conventional data models. They ascertain standardized general relation types, together with the kinds of things that may exist related by such a relation type. Generic data models are developed as an approach to solving some shortcomings of conventional data models. For example, different modelers usually produce unlike conventional data models of the aforementioned domain. This can atomic number 82 to difficulty in bringing the models of different people together and is an obstacle for information exchange and data integration. Invariably, notwithstanding, this difference is attributable to different levels of abstraction in the models and differences in the kinds of facts that tin can exist instantiated (the semantic expression capabilities of the models). The modelers need to communicate and agree on sure elements that are to be rendered more concretely, in order to make the differences less significant.

Semantic data model [edit]

A semantic data model in software engineering is a technique to define the meaning of data inside the context of its interrelationships with other data. A semantic information model is an brainchild which defines how the stored symbols relate to the existent globe.^[16] A semantic information model is sometimes chosen a conceptual information model.

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 considering 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. Every bit illustrated in the figure. The real world, in terms of resources, ideas, events, etc., are symbolically defined within concrete information stores. A semantic data model is an abstraction which defines how the stored symbols relate to the existent world. Thus, the model must be a true representation of the existent world.^[xvi]

Topics [edit]

Data compages [edit]

Data architecture is the pattern of information for apply in defining the target state and the subsequent planning needed to hit the target state. It is ordinarily one of several architecture domains that course the pillars of an enterprise compages or solution architecture.

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

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

Information modeling [edit]

The data modeling procedure

Information modeling in software applied science 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 requirements for a database. It is sometimes chosen database modeling because a data model is somewhen implemented in a database.^[xix]

The figure illustrates the manner information models are adult and used today. A conceptual information model is developed based on the data requirements for the application that is beingness developed, maybe in the context of an activity model. The information model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects. This is then used every bit the start indicate for interface or database blueprint.^[eight]

Data properties [edit]

Some important properties of data^[8]

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

definition-related properties^[8]
- relevance: the usefulness of the data in the context of your concern.
- clarity: the availability of a clear and shared definition for the data.
- consistency: the compatibility of the same type of information from different sources.
content-related properties
- timeliness: the availability of information at the time required and how up to date that data is.
- accurateness: how close to the truth the data is.
properties related to both definition and content
- completeness: how much of the required data is available.
- accessibility: where, how, and to whom the information is bachelor or not available (eastward.chiliad. security).
- cost: the cost incurred in obtaining the data, and making it bachelor for use.

Data system [edit]

Another kind of data model describes how to organize data using a database management system or other information management technology. Information technology describes, for example, relational tables and columns or object-oriented classes and attributes. Such a data model is sometimes referred to as the physical data model, but in the original ANSI three schema compages, 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 than conceptual information model described to a higher place. It may differ, withal, to account for constraints like processing capacity and usage patterns.

While information assay is a common term for data modeling, the action actually has more in common with the ideas and methods of synthesis (inferring general concepts from particular instances) than information technology does with assay (identifying component concepts from more general ones). {Presumably we telephone call ourselves systems analysts considering no i can say systems synthesists.} Data modeling strives to bring the data structures of involvement together into a cohesive, inseparable, whole past eliminating unnecessary information redundancies and by relating information structures with relationships.

A different arroyo is to use adaptive systems such equally artificial neural networks that can autonomously create implicit models of data.

Information structure [edit]

A binary tree, a elementary type of branching linked information structure

A data structure is a way of storing data in a figurer and then that it can be used efficiently. It is an organization of mathematical and logical concepts of information. Frequently a carefully called data structure volition let the well-nigh efficient algorithm to be used. The pick of the data structure often begins from the pick of an abstract data type.

A data model describes the construction 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 grammar for a dedicated artificial language for that domain. A information model represents classes of entities (kinds of things) virtually which a visitor wishes to hold information, the attributes of that data, and relationships amongst those entities and (often implicit) relationships amongst those attributes. The model describes the system of the data to some extent irrespective of how data might be represented in a computer organization.

The entities represented past a data model can exist the tangible entities, just models that include such concrete entity classes tend to alter over fourth dimension. Robust data models oft identify abstractions of such entities. For instance, a information model might include an entity class called "Person", representing all the people who interact with an arrangement. Such an abstruse entity class is typically more appropriate than ones called "Vendor" or "Employee", which place specific roles played past those people.

Array

Information model theory [edit]

The term information model can have two meanings:^[xx]

A data model theory, i.east. a formal description of how data may be structured and accessed.
A data model instance, i.e. applying a data model theory to create a practical data model instance for some detail application.

A data model theory has 3 primary components:^[20]

The structural part: a collection of data structures which are used to create databases representing the entities or objects modeled by the database.
The integrity office: a drove of rules governing the constraints placed on these data structures to ensure structural integrity.
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.

For example, in the relational model, the structural function is based on a modified concept of the mathematical relation; the integrity part is expressed in first-guild logic and the manipulation part is expressed using the relational algebra, tuple calculus and domain calculus.

A data model example is created by applying a data model theory. This is typically done to solve some business enterprise requirement. Business requirements are ordinarily captured by a semantic logical data model. This is transformed into a physical data model instance from which is generated a concrete database. For example, a data modeler may use a data modeling tool to create an entity-human relationship model of the corporate information repository of some business organization enterprise. This model is transformed into a relational model, which in turn generates a relational database.

Patterns [edit]

Patterns^[21] are mutual data modeling structures that occur in many data models.

[edit]

Data-flow diagram [edit]

Data-Flow Diagram instance^[22]

A data-flow diagram (DFD) is a graphical representation of the "menses" of data through an information system. Information technology differs from the flowchart equally it shows the data menstruum instead of the control period of the program. A information-period diagram tin can likewise be used for the visualization of data processing (structured blueprint). Data-flow diagrams were invented by Larry Constantine, the original developer of structured design,^[23] based on Martin and Estrin's "data-menstruum graph" model of computation.

It is common practise to draw a context-level information-flow diagram first which shows the interaction betwixt the system and outside entities. The DFD is designed to show how a system is divided into smaller portions and to highlight the menstruation of data between those parts. This context-level data-catamenia diagram is then "exploded" to prove more detail of the organisation being modeled

Information model [edit]

An Information model is non a type of data model, simply more than or less an alternative model. Inside the field of software engineering both a information model and an information model can exist abstract, formal representations of entity types that include their backdrop, relationships and the operations that can be performed on them. The entity types in the model may exist kinds of existent-world objects, such as devices in a network, or they may themselves exist abstract, such as for the entities used in a billing system. Typically, they are used to model a constrained domain that can be described past a airtight fix of entity types, backdrop, relationships and operations.

According to Lee (1999)^[24] an information model is a representation of concepts, relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse. It can provide sharable, stable, and organized structure of information requirements for the domain context.^[24] More in full general the term information model is used for models of individual things, such equally facilities, buildings, procedure plants, etc. In those cases the concept is specialised to Facility Information Model, Building Information Model, Institute Information Model, etc. Such an data model is an integration of a model of the facility with the data and documents nearly the facility.

An information model provides ceremonial to the clarification 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 object models (eastward.g. using UML), entity relationship models or XML schemas.

Object model [edit]

An object model in figurer scientific discipline is a collection of objects or classes through which a programme can examine and dispense 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 object model of the represented service or organisation. For case, the Document Object Model (DOM) [1] is a collection of objects that stand for a page in a web browser, used by script programs to examine and dynamically change the page. There is a Microsoft Excel object model^[25] for controlling Microsoft Excel from another program, and the ASCOM Telescope Commuter^[26] is an object model for decision-making an astronomical telescope.

In computing the term object model has a distinct second meaning of the full general properties of objects in a specific calculator programming language, engineering science, annotation or methodology that uses them. For example, the Coffee object model, the COM object model, or the object model of OMT. Such object models are normally divers using concepts such equally class, message, inheritance, polymorphism, and encapsulation. There is an extensive literature on formalized object models as a subset of the formal semantics of programming languages.

Object-Function Model [edit]

Example of the application of Object-Role Modeling in a "Schema for Geologic Surface", Stephen K. Richard (1999)^[27]

Object-Role Modeling (ORM) is a method for conceptual modeling, and can be used as a tool for information and rules analysis.^[28]

Object-Role Modeling is a fact-oriented method for performing systems assay at the conceptual level. The quality of a database application depends critically on its design. To assistance 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.

The conceptual design may include data, procedure and behavioral perspectives, and the actual DBMS used to implement the design might be based on 1 of many logical data models (relational, hierarchic, network, object-oriented etc.).^[29]

Unified Modeling Language models [edit]

The Unified Modeling Linguistic communication (UML) is a standardized general-purpose modeling linguistic communication in the field of software engineering science. It is a graphical language for visualizing, specifying, constructing, and documenting the artifacts of a software-intensive system. The Unified Modeling Language offers a standard style to write a system's blueprints, including:^[xxx]

Conceptual things such as business processes and system functions
Physical things such equally programming linguistic communication statements, database schemas, and
Reusable software components.

UML offers a mix of functional models, data models, and database models.

References [edit]

^ "datamodel - UML Domain Modeling - Stack Overflow". Stack Overflow. Stack Exchange Inc. Retrieved 4 February 2017.
^ "XQuery and XPath Information Model 3.ane". Globe Wide Web Consortium (W3C). W3C. Retrieved 4 February 2017.
^ "datamodel". npm. npm, Inc. Retrieved 4 Feb 2017.
^ "DataModel (Java EE 6)". Java Documentation. Oracle. Retrieved 4 February 2017.
^ Ostrovskiy, Stan. "iOS: 3 ways to pass data from Model to Controller". Medium. A Medium Corporation. Retrieved 4 February 2017.
^ Paul R. Smith & Richard Sarfaty Publications, LLC 2009
^ Michael R. McCaleb (1999). "A Conceptual Data Model of Datum Systems" Archived 2008-09-21 at the Wayback Automobile. National Institute of Standards and Technology. Baronial 1999.
^ ^a ^b ^c ^d ^e ^f ^g ^h ⁱ ^j ^k Matthew West and Julian Fowler (1999). Developing Loftier Quality Data Models. The European Process Industries STEP Technical Liaison Executive (EPISTLE).
^ American National Standards Plant. 1975. ANSI/X3/SPARC Study Group on Information Base of operations Management Systems; Interim Report. FDT (Message of ACM SIGMOD) 7:two.
^ Young, J. W., and Kent, H. Chiliad. (1958). "Abstract Formulation of Information Processing Bug". In: Journal of Industrial Engineering. Nov-Dec 1958. 9(6), pp. 471-479
^ ^a ^b Janis A. Bubenko jr (2007) "From Information Algebra to Enterprise Modelling and Ontologies - a Historical Perspective on Modelling for Information Systems". In: Conceptual Modelling in Data Systems Applied science. John Krogstie et al. eds. pp i-eighteen
^ Cornelius T. Leondes (2002). Database and Data Communication Network Systems: Techniques and Applications. Page 7
^ "Derivability, Redundancy, and Consistency of Relations Stored in Large Data Banks", E.F. Codd, IBM Inquiry Report, 1969
^ Data and Reality
^ Jan L. Harrington (2000). Object-oriented Database Design Clearly Explained. p.4
^ ^a ^b ^c ^d FIPS Publication 184 Archived 2013-12-03 at the Wayback Motorcar released of IDEF1X by the Computer Systems Laboratory of the National Institute of Standards and Technology (NIST). 21 December 1993 (withdrawn in 2008).
^ Wade, T. and Sommer, S. eds. A to Z GIS
^ ^a ^b ^c ^d David R. Soller1 and Thomas 1000. Berg (2003). The National Geologic Map Database Project: Overview and Progress U.S. Geological Survey Open-File Report 03–471.
^ Whitten, Jeffrey L.; Lonnie D. Bentley, Kevin C. Dittman. (2004). Systems Assay and Pattern Methods. sixth edition. ISBN 0-256-19906-X.
^ ^a ^b Beynon-Davies P. (2004). Database Systems tertiary Edition. Palgrave, Basingstoke, U.k.. ISBN ane-4039-1601-2
^ "The Data Model Resources Book: Universal Patterns for Data Modeling" Len Silverstone & Paul Agnew (2008).
^ John Azzolini (2000). Introduction to Systems Applied science Practices. July 2000.
^ W. Stevens, K. Myers, L. Constantine, "Structured Design", IBM Systems Periodical, 13 (2), 115-139, 1974.
^ ^a ^b Y. Tina Lee (1999). "Information modeling from blueprint to implementation" National Institute of Standards and Technology.
^ Excel Object Model Overview
^ "ASCOM General Requirements". 2011-05-thirteen. Retrieved 2014-09-25 .
^ Stephen M. Richard (1999). Geologic Concept Modeling. U.Southward. Geological Survey Open up-File Report 99-386.
^ Joachim Rossberg and Rickard Redler (2005). Pro Scalable .Net two.0 Awarding Designs.. Page 27
^ Object Role Modeling: An Overview (msdn.microsoft.com). Retrieved 19 September 2008.
^ Grady Booch, Ivar Jacobson & Jim Rumbaugh (2005) OMG Unified Modeling Language Specification.