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76-683: Data models formally define data objects and relationships among data objects for a domain of interest. Some typical applications of data models include supporting the development of databases and enabling the exchange of data for a particular area of interest. Data models are specified in a data modeling language. EXPRESS is a data modeling language defined in ISO 10303-11, the EXPRESS Language Reference Manual. An EXPRESS data model can be defined in two ways, textually and graphically. For formal verification and as input for tools such as SDAI

152-495: A data processing problem". They wanted to create "a notation that should enable the analyst to organize the problem around any piece of hardware ". 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 CODASYL , an IT industry consortium formed in 1959, who essentially aimed at

228-565: A data structure , especially in the context of programming languages . Data models are often complemented by function models , especially in the context of enterprise models . A data model explicitly determines the structure of data ; conversely, structured data is data organized according to an explicit data model or data structure. Structured data is in contrast to unstructured data and semi-structured data . The term data model can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of

304-517: A carefully chosen data structure will allow the most efficient algorithm to be used. The choice of the data structure often begins from the choice of an abstract data type . 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 grammar for a dedicated artificial language for that domain. A data model represents classes of entities (kinds of things) about which

380-440: A cohesive, inseparable, whole by eliminating unnecessary data redundancies and by relating data structures with relationships . A different approach is to use adaptive systems such as artificial neural networks that can autonomously create implicit models of data. 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

456-535: A company wishes to hold information, the attributes of that information, and relationships among those entities and (often implicit) relationships among those attributes. The model describes the organization of the data to some extent irrespective of how data might be represented in a computer system. 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 abstractions of such entities. For example,

532-561: A data model for XML documents. The main aim of data models is to support the development of information systems 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 are indicated above. However, systems and interfaces often cost more than they should, to build, operate, and maintain. They may also constrain

608-455: A data model is sometimes referred to as the physical data model , 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. While data analysis

684-417: A data model might include an entity class called "Person", representing all the people who interact with an organization. Such an abstract entity class is typically more appropriate than ones called "Vendor" or "Employee", which identify specific roles played by those people. The term data model can have two meanings: A data model theory has three main components: For example, in the relational model ,

760-413: A data modeling language.[3] A data model instance may be one of three kinds according to ANSI in 1975: 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

836-409: A design can be detailed into a logical data model . In later stages, this model may be translated into physical data model . However, it is also possible to implement a conceptual model directly. 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

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912-524: A non-abstract subtype. Entities and defined data types may be further constrained with WHERE rules. WHERE rules are also part of global rules. A WHERE rule is an expression, which must evaluate to TRUE, otherwise a population of an EXPRESS schema, is not valid. Like derived attributes these expression may invoke EXPRESS functions, which may further invoke EXPRESS procedures. The functions and procedures allow formulating complex statements with local variables, parameters and constants - very similar to

988-445: A programming language. The EXPRESS language can describe local and global rules. For example: This example describes that area_unit entity must have square value of length. For this the attribute dimensions.length_exponent must be equal to 2 and all other exponents of basic SI units must be 0. Another example: That is, it means that week value cannot exceed 7. And so, you can describe some rules to your entities. More details on

1064-503: A semantic logical data model . 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 entity–relationship model of the corporate data repository of some business enterprise. This model is transformed into a relational model , which in turn generates a relational database . Patterns are common data modeling structures that occur in many data models. A data-flow diagram (DFD)

1140-578: A series of datatypes, with specific data type symbols of the EXPRESS-G notation: A few general things are to be mentioned for datatypes. Entity attributes allow to add "properties" to entities and to relate one entity with another one in a specific role. The name of the attribute specifies the role. Most datatypes can directly serve as type of an attribute. This includes aggregation as well. There are three different kinds of attributes, explicit, derived and inverse attributes. And all these can be re-declared in

1216-457: A single entity (if not abstract) or for a complex combination of entities in such a sub-supertype graph. For the big graphs the number of possible combinations is likely to grow in astronomic ranges. To restrict the possible combinations special supertype constraints got introduced such as ONEOF and TOTALOVER. Furthermore, an entity can be declared to be abstract to enforce that no instance can be constructed of just this entity but only if it contains

1292-593: A subtype. In addition an explicit attribute can be re-declared as derived in a subtype. No other change of the kind of attributes is possible. Specific attribute symbols of the EXPRESS-G notation: An entity can be defined to be a subtype of one or several other entities ( multiple inheritance is allowed!). A supertype can have any number of subtypes. It is very common practice in STEP to build very complex sub-supertype graphs. Some graphs relate 100 and more entities with each other. An entity instance can be constructed for either

1368-486: A successor of IGES , SET and VDA-FS . The initial plan was that "STEP shall be based on one single, complete, implementation-independent Product Information Model, which shall be the Master Record of the integrated topical and application information models". But because of the complexity, the standard had to be broken up into smaller parts that can be developed, balloted and approved separately. In 1994/95 ISO published

1444-440: 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 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

1520-439: A work item (NWI) for a new standard. The original intent of STEP was to publish one integrated data-model for all life cycle aspects. But due to the complexity, different groups of developers and different speed in the development processes, the splitting into several APs was needed. But this splitting made it difficult to ensure that APs are interoperable in overlapping areas. Main areas of harmonization are: For complex areas it

1596-440: Is a common term for data modeling, the activity actually has more in common with the ideas and methods of synthesis (inferring general concepts from particular instances) than it does with analysis (identifying component concepts from more general ones). { Presumably we call ourselves systems analysts because no one can say systems synthesists . } Data modeling strives to bring the data structures of interest together into

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1672-470: Is a graphical representation of the "flow" of data through an information system . It differs from the flowchart as it shows the data flow instead of the control flow of the program. A data-flow diagram can also be used for the visualization of data processing (structured design). Data-flow diagrams were invented by Larry Constantine , the original developer of structured design, based on Martin and Estrin's "data-flow graph" model of computation. It

1748-514: Is a technique for defining business requirements for a database. It is sometimes called database modeling because a data model is eventually implemented in a database. The figure illustrates the way data models are developed and used today. A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model . The data model will normally consist of entity types, attributes, relationships, integrity rules, and

1824-731: Is added to every AP, using IDEF0 . STEP is primarily defining data models using the EXPRESS modeling language. Application data according to a given data model can be exchanged either by a STEP-File , STEP-XML or via shared database access using SDAI . Every AP defines a top data models to be used for data exchange, called the Application Interpreted Model (AIM) or in the case of a modular AP called Module Interpreted Models (MIM). These interpreted models are constructed by choosing generic objects defined in lower level data models (4x, 5x, 1xx, 5xx) and adding specializations needed for

1900-591: Is an ISO standard for the computer -interpretable representation and exchange of product manufacturing information . It is an ASCII -based format. Its official title is: Automation systems and integration — Product data representation and exchange . It is known informally as " STEP ", which stands for "Standard for the Exchange of Product model data". ISO 10303 can represent 3D objects in Computer-aided design (CAD) and related information. The objective of

1976-571: Is closely related with PLIB (ISO 13584, IEC 61360). The basis for STEP was the Product Data Exchange Specification (PDES) , which was initiated during the mid-1980's and was submitted to ISO in 1988. The Product Data Exchange Specification (PDES) was a data definition effort intended to improve interoperability between manufacturing companies, and thereby improve productivity. The evolution of STEP can be divided into four release phases. The development of STEP started in 1984 as

2052-402: Is common practice to draw a context-level data-flow diagram first which shows the interaction between the system and outside entities. The DFD 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 An Information model is not

2128-416: Is developed and maintained by the ISO technical committee TC 184, Automation systems and integration , sub-committee SC 4, Industrial data . Like other ISO and IEC standards STEP is copyright by ISO and is not freely available. However, the 10303 EXPRESS schemas are freely available, as are the recommended practices for implementers. Other standards developed and maintained by ISO TC 184/SC 4 are: STEP

2204-449: Is enclosed within the EXPRESS schema Family . It contains a supertype entity Person with the two subtypes Male and Female . Since Person is declared to be ABSTRACT only occurrences of either (ONEOF) the subtype Male or Female can exist. Every occurrence of a person has a mandatory name attribute and optionally attributes mother and father . There is a fixed style of reading for attributes of some entity type: EXPRESS offers

2280-420: Is that the structure of a data model can be presented in a more understandable manner. A disadvantage of EXPRESS-G is that complex constraints cannot be formally specified. Figure 1 is an example. The data model presented in figure could be used to specify the requirements of a database for an audio compact disc (CD) collection. A simple EXPRESS data model looks like fig 2, and the code like this: The data model

2356-440: Is the possibility to formally validate a population of datatypes - this is to check for all the structural and algorithmic rules. EXPRESS-G is a standard graphical notation for information models . It is a companion to the EXPRESS language for displaying entity and type definitions, relationships and cardinality. This graphical notation supports a subset of the EXPRESS language. One of the advantages of using EXPRESS-G over EXPRESS

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2432-400: Is to be stored in a database . This technique can describe any ontology , i.e., an overview and classification of concepts and their relationships, for a certain area of interest . In the 1970s G.M. Nijssen developed "Natural Language Information Analysis Method" (NIAM) method, and developed this in the 1980s in cooperation with Terry Halpin into Object–Role Modeling (ORM). However, it

2508-461: Is very similar to the ISO 15926-2 model, whereas AP 221 follows the STEP architecture and ISO 15926-2 has a different architecture. They both use ISO-15926-4 as their common reference data library or dictionary of standard instances. A further development of both standards resulted in Gellish English as general product modeling language that is application domain independent and that is proposed as

2584-480: The constraints that bind them. The basic graphic elements of DSDs are boxes , representing entities, and arrows , representing relationships. Data structure diagrams are most useful for documenting complex data entities. Data structure diagrams are an extension of the entity–relationship model (ER model). In DSDs, attributes are specified inside the entity boxes rather than outside of them, while relationships are drawn as boxes composed of attributes which specify

2660-399: The initial release of STEP as international standards (IS) with the parts 1, 11, 21, 31, 41, 42, 43, 44, 46, 101, AP 201 and AP 203. Today AP 203 Configuration controlled 3D design is still one of the most important parts of STEP and supported by many CAD systems for import and export. In the second phase the capabilities of STEP were widely extended, primarily for the design of products in

2736-869: The international standard is to provide a mechanism that is capable of describing product data throughout the life cycle of a product , independent from any particular system. The nature of this description makes it suitable not only for neutral file exchange, but also as a basis for implementing and sharing product databases and archiving. STEP can be typically used to exchange data between CAD , computer-aided manufacturing , computer-aided engineering , product data management / enterprise data modeling and other CAx systems. STEP addresses product data from mechanical and electrical design, geometric dimensioning and tolerancing , analysis and manufacturing, as well as additional information specific to various industries such as automotive , aerospace , building construction , ship , oil and gas , process plants and others. STEP

2812-441: The objects 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 tables. So the "data model" of a banking application may be defined using the entity–relationship "data model". This article uses

2888-423: The relational model for database management based on first-order predicate logic . In the 1970s entity–relationship modeling emerged as a new type of conceptual data modeling, originally formalized 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 information needs or the type of information that

2964-455: The requirements 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 conceptual view has led to the development of semantic data modeling techniques. That is, techniques to define the meaning of data within the context of its interrelationships with other data. As illustrated in

3040-574: The AIM was also used for the ARM. Over time these ARM models got very detailed till to the point that some implementations preferred to use the ARM instead of the formally required AIM/MIM. Today a few APs have ARM based exchange formats standardized outside of ISO TC184/SC4: There is a bigger overlap between APs because they often need to refer to the same kind of products, product structures, geometry and more. And because APs are developed by different groups of people it

3116-484: The AIM, called MIM. Modules are built on each other, resulting in an (almost) directed graph with the AP and conformance class modules at the very top. The modular APs are: The modular editions of AP 209 and 210 are explicit extensions of AP 242. The STEP APs can be roughly grouped into the three main areas design, manufacturing and life cycle support. Design APs: Manufacturing APs: Life cycle support APs: The AP 221 model

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3192-538: The aerospace, automotive, electrical, electronic, and other industries. This phase ended in the year 2002 with the second major release, including the STEP parts AP 202, AP 209, AP 210, AP 212, AP 214, AP 224, AP 225, AP 227, AP 232. Basic harmonization between the APs especially in the geometric areas was achieved by introducing the Application Interpreted Constructs (AIC, 500 series). A major problem with

3268-473: The biggest standard within ISO. Each part has its own scope and introduction. The APs are the top parts. They cover a particular application and industry domain and hence are most relevant for users of STEP. Every AP defines one or several Conformance Classes, suitable for a particular kind of product or data exchange scenario. To provide a better understanding of the scope, information requirements and usage scenarios an informative application activity model (AAM)

3344-498: The business rather than support it. A major cause is that the quality of the data models implemented in systems and interfaces is poor". The reason for these problems is a lack of standards that will ensure that data models will both meet business needs and be consistent. 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

3420-610: The cardinality. A data model in Geographic information systems is a mathematical construct for representing geographic objects or surfaces as data. For example, 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 models. For example, different modelers usually produce different conventional data models of

3496-414: 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 conceptual data model . Such

3572-450: The constraints that bind entities together. DSDs differ from the ER model in that the ER model focuses on the relationships between different 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

3648-414: The data and their relationship in a database, the procedures in an application program. Object orientation, however, combined an entity's procedure with its data." During the early 1990s, three Dutch mathematicians Guido Bakema, Harm van der Lek, and JanPieter Zwart, continued the development on the work of G.M. Nijssen . They focused more on the communication part of the semantics. In 1997 they formalized

3724-543: 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. The corresponding professional activity is called generally data modeling or, more specifically, database design . Data models are typically specified by a data expert, data specialist, data scientist, data librarian, or a data scholar. A data modeling language and notation are often represented in graphical form as diagrams. A data model can sometimes be referred to as

3800-425: The definitions of those objects. This is then used as the start point for interface or database design . Some important properties of data for which requirements need to be met are: Another kind of data model describes how to organize data using a database management system or other data management technology. It describes, for example, relational tables and columns or object-oriented classes and attributes. Such

3876-548: The description of Electrical Wire Harnesses and introduces an extension of STEP modelisation and implementation methods based on SysML and system engineering with an optimized XML implementation method. This new edition contains also enhancements on 3D Dimensioning and Tolerancing, and Composite Design. New functionalities are also introduced like: STEP is divided into many parts, grouped into In total STEP consists of several hundred parts and every year new parts are added or new revisions of older parts are released. This makes STEP

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3952-500: The development of an ATS was very expensive and inefficient this requirement was dropped and replaced by the requirements to have an informal validation report and recommended practices how to use it. Today the recommended practices are a primary source for those going to implement STEP. The Application Reference Models (ARM) is the mediator between the AAM and the AIM/MIM. Originally its purpose

4028-506: The differences less significant. 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 conceptual data model . The logical data structure of a database management system (DBMS), whether hierarchical , network , or relational , cannot totally satisfy

4104-444: The domain context. More in general the term information model is used for models of individual things, such as facilities, buildings, process plants, etc. In those cases the concept is specialised to Facility Information Model , Building Information Model , Plant Information Model, etc. Such an information model is an integration of a model of the facility with the data and documents about the facility. ISO 10303 ISO 10303

4180-474: 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. According to Lee (1999) 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

4256-498: 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. 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

4332-419: 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. In the 1980s, according to Jan L. Harrington (2000), "the development of the object-oriented 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:

4408-414: The figure. The real world, in terms of resources, ideas, events, etc., are symbolically defined within physical data stores. A semantic data model is an abstraction that defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world. Data architecture is the design of data for use in defining the target state and the subsequent planning needed to hit

4484-470: The given examples can be found in ISO 10303 -41 [REDACTED]  This article incorporates public domain material from the National Institute of Standards and Technology Data model A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities . For instance, a data model may specify that

4560-450: The information system provided the data and information for management purposes. The first generation database system , called Integrated Data Store (IDS), was designed by Charles Bachman at General Electric. Two famous database models, the network data model and the hierarchical data model , were proposed during this period of time". Towards the end of the 1960s, Edgar F. Codd worked out his theories of data arrangement, and proposed

4636-524: The manner of defining cardinality . The choices are between arrow heads, inverted arrow heads ( crow's feet ), or numerical representation of the cardinality. An entity–relationship model (ERM), sometimes referred to as an entity–relationship diagram (ERD), could be used to represent an abstract conceptual data model (or semantic data model or physical data model) used in software engineering to represent structured data. There are several notations used for ERMs. Like DSD's, attributes are specified inside

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4712-419: The method Fully Communication Oriented Information Modeling FCO-IM . A database model is a specification describing how a database is structured and used. Several such models have been suggested. Common models include: A data structure diagram (DSD) is a diagram and data model used to describe conceptual data models by providing graphical notations which document entities and their relationships , and

4788-526: The monolithic APs of the first and second releases is that they are too big, have too much overlap with each other, and are not sufficiently harmonized. These deficits led to the development of the STEP modular architecture (400 and 1000 series). This activity was primarily driven by new APs covering additional life-cycle phases such as early requirement analysis (AP 233) and maintenance and repair (AP 239), and also new industrial areas (AP 221, AP 236). New editions of

4864-583: The particular application domain of the AP. The common generic data models are the basis for interoperability between APs for different kinds of industries and life cycle stages. In APs with several Conformance Classes the top data model is divided into subsets, one for each Conformance Class. The requirements of a conformant STEP application are: Originally every APs was required to have a companion Abstract test suite (ATS) (e.g. ATS 303 for AP 203), providing Test Purposes , Verdict Criteria and Abstract Test Cases together with example STEP-Files. But because

4940-609: The parts separately. In December 2014, ISO published the first edition of a new major Application Protocol, AP 242 Managed model based 3d engineering , that combined and replaced the following previous APs in an upward compatible way: AP 242 was created by merging the following two Application protocols: In addition AP 242 edition 1 contains extensions and significant updates for: Two APs had been modified to be directly based on AP 242, and thus became supersets of it: AP242 edition 2, published in April 2020, extends edition 1 domain by

5016-460: The previous monolithic APs on a modular basis have been developed (AP 203, AP 209, AP 210). The publication of these new editions coincided with the release in 2010 of the new ISO product SMRL , the STEP Module and Resource Library, that contains all STEP resource parts and application modules on a single CD. The SMRL will be revised frequently and is available at a much lower cost than purchasing all

5092-495: 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

5168-411: 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 information algebra . In the 1960s data modeling gained more significance with the initiation of the management information system (MIS) concept. According to Leondes (2002), "during that time,

5244-433: The structural part is based on a modified concept of the mathematical relation ; the integrity part is expressed in first-order logic and the manipulation part is expressed using the relational algebra , tuple calculus and domain calculus . 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

5320-423: 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. Data modeling in software engineering is the process of creating a data model by applying formal data model descriptions using data modeling techniques. Data modeling

5396-478: The target state. It is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture . 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. Essential to realizing

5472-460: The term in both senses. Managing large quantities of structured and unstructured data is a primary function of information systems . 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 word processing documents, email messages , pictures, digital audio, and video: XDM , for example, provides

5548-523: The textual representation within an ASCII file is the most important one. The graphical representation on the other hand is often more suitable for human use such as explanation and tutorials. The graphical representation, called EXPRESS-G, is not able to represent all details that can be formulated in the textual form. EXPRESS is similar to programming languages such as Pascal . Within a SCHEMA various datatypes can be defined together with structural constraints and algorithmic rules. A main feature of EXPRESS

5624-498: Was Terry Halpin's 1989 PhD thesis that created the formal foundation on which Object–Role Modeling is based. Bill Kent, in his 1978 book Data and Reality, 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 mathematically clean and elegant, Kent emphasized

5700-403: Was always an issue to ensure interoperability between APs on a higher level. The Application Interpreted Constructs (AIC) solved this problem for common specializations of generic concepts, primarily in the geometric area. To address the problem of harmonizing the ARM models and their mapping to the AIM the STEP modules were introduced. They contain a piece of the ARM, the mapping and a piece of

5776-406: Was only to document high level application objects and the basic relations between them. IDEF1X diagrams documented the AP of early APs in an informal way. The ARM objects, their attributes and relations are mapped to the AIM so that it is possible to implement an AP. As APs got more and more complex formal methods were needed to document the ARM and so EXPRESS which was originally only developed for

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