Skip to search form Skip to main content. In this paper, we differentiate between two classes of frameworks: classic enterprise architecture frameworks, and federated enterprise architecture frameworks. From each class, a number of reputable frameworks are presented. Conclusions are made concerning what these frameworks learn us for setting up an Extended Enterprise architecture framework. View PDF.
References Publications referenced by this Feaf and semantic model. However, like most things in IT, EA as a discipline suffers from a lack of clarity regarding its core principles and approaches. Skip to search form Skip to main content. A semantic data model can be used to serve many purposes. Database model. Categories : Data semanti Systems analysis Knowledge representation.
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Semantic data model SDM is a high-level semantics-based database description and structuring formalism database model for mldel. Those semantic models can be stored in Gellish Databases, being semantic databases. The semantic Feaf and semantic model model is Nurses in louisville ky method of structuring data in order to represent it in a specific logical way. Usually, singular data or a word does not convey any meaning to humans, but paired with a context this word inherits more meaning. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. This implies that semantic databases can be integrated when they use the same standard relation Feaf and semantic model. It can be considered the first [ citation needed ] Social Semantic Web application, in that it combines RDF technology with 'social web' concerns. An SDM database description can serve as a Feaf and semantic model specification and documentation tool ,odel a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system. Techopedia explains Semantic Data Model The semantic data model is a relatively new approach that is based on semantic principles that result in a data set with inherently specified data structures. Assortative senantic Interpersonal bridge Organizational network analysis Small-world experiment Social aspects of television Social capital Social data revolution Social exchange theory Social identity theory Social network analysis Social web Structural endogamy. NyeBradley J. An example of such is the semantic data model that is standardised as ISO -2which is further developed into the semantic modelling language Gellish
The DRM is a framework whose primary purpose is to enable information sharing and reuse across the United States federal government via the standard description and discovery of common data and the promotion of uniform data management practices.
- The DM2 defines architectural data elements and enables the integration and federation of Architectural Descriptions.
- FOAF an acronym of friend of a friend is a machine-readable ontology describing persons , their activities and their relations to other people and objects.
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The DRM is a framework whose primary purpose is to enable information sharing and reuse across the United States federal government via the standard description and discovery of common data and the promotion of uniform data management practices. The DRM describes artifacts which can be generated from the data architectures of federal government agencies. The DRM provides a flexible and standards-based approach to accomplish its purpose.
The scope of the DRM is broad, as it may be applied within a single agency, within a community of interest , or cross-community of interest.
The DRM provides a standard means by which data may be described, categorized, and shared. The Data Reference Model version 2 released in November is a page document with detailed architectural diagrams and an extensive glossary of terms.
The DRM is not technically a published technical interoperability standard such as web services, it is an excellent starting point for data architects within federal and state agencies. Any federal or state agencies that are involved with exchanging information with other agencies or that are involved in data warehousing efforts should use this document as a guide. From Wikipedia, the free encyclopedia. Categories : Computer data Data management Reference models.
Views Read Edit View history. This is done hierarchically so that types that reference other types are always listed above the types that they are referencing, which makes it easier to read and understand. Related Tags. Semantic data model SDM is a high-level semantics-based database description and structuring formalism database model for databases. Community recognition Complex contagion Consequential strangers Friend of a friend Friendship paradox Six degrees of separation Social media addiction Social invisibility Social network game Social occultation Tribe.
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Techopedia explains Semantic Data Model The semantic data model is a relatively new approach that is based on semantic principles that result in a data set with inherently specified data structures.
Usually, singular data or a word does not convey any meaning to humans, but paired with a context this word inherits more meaning. In a database environment, the context of data is often defined mainly by its structure, such as its properties and relationships with other objects.
So, in a relational approach, the vertical structure of the data is defined by explicit referential constraints, but in semantic modeling this structure is defined in an inherent way, which is to say that a property of the data itself may coincide with a reference to another object.
A semantic data model may be illustrated graphically through an abstraction hierarchy diagram, which shows data types as boxes and their relationships as lines. This is done hierarchically so that types that reference other types are always listed above the types that they are referencing, which makes it easier to read and understand.
Share this:. Related Terms. Related Articles. NoSQL Data Warehousing Driverless Cars: Levels of Autonomy. Related Tutorials. Introduction to Databases. What is the difference between a mobile OS and a computer OS? By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications.
The design of the present SDM is based on our experience in using a preliminary version of it. SDM is designed to enhance the effectiveness and usability of database systems.
An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system.
A semantic data model in software engineering has various meanings:. To interpret the meaning of the facts from the instances it is required that the meaning of the kinds of relations relation types be known. Therefore, semantic data models typically standardize such relation types.
This means that the second kind of semantic data models enable that the instances express facts that include their own meaning. The second kind of semantic data models are usually meant to create semantic databases. The ability to include meaning in semantic databases facilitates building distributed databases that enable applications to interpret the meaning from the content. This implies that semantic databases can be integrated when they use the same standard relation types.
This also implies that in general they have a wider applicability than relational or object-oriented databases. 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, 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 the figure.
The real world, in terms of resources, ideas, events, etc. A semantic data model is an abstraction which defines how the stored symbols relate to the real world. Thus, the model must be a true representation of the real world. According to Klas and Schrefl , the "overall goal of semantic data models is to capture more meaning of data by integrating relational concepts with more powerful abstraction concepts known from the Artificial Intelligence field.
The idea is to provide high level modeling primitives as an integral part of a data model in order to facilitate the representation of real world situations". The need for semantic data models was first recognized by the U. The objective of this program was to increase manufacturing productivity through the systematic application of computer technology.
Although EA has been practiced across a much wider community of IT professionals for a longer period of time, it still suffers from an identity crisis. Is EA the mandatory precursor for model driven development, or is it part of a bigger picture and if so, what is that picture?
It is my contention that the reason Enterprise Architecture is still misunderstood in many quarters and often unsuccessful in practice is precisely because it does exist within the context of a larger picture.
All too often, that larger picture is simply ignored leaving those executing EA projects somewhat perplexed as to find meaningful ways to make their efforts relevant to the organization sponsoring their efforts. Enterprise Architecture represents a practices, tools and techniques which have evolved to help define the nature and state transitions of an organization from an IT perspective.
The business view of the enterprise is of course included within this perspective but EA at its heart is and always has been driven by technology.
The EA perspective is of a virtual entity, perhaps even the cyber-identity of an organization. This view allows those who manage and maintain complex system of systems ecosystems through exploitation of a holistic characterization of all capability elements.
However, like most things in IT, EA as a discipline suffers from a lack of clarity regarding its core principles and approaches. In other words, there is no agreed upon definition for any aspect of Enterprise Architecture right now.
So, Semantics and EA come together on many different levels; first in the need for one to clarify the other, secondly in the ability to build that larger context and big picture view of where EA fits along with all of the other aspects of IT. What then is Enterprise Architecture, really? Is it a framework or set of frameworks, is it product specific skills, is it language specific skills, is purely technical or partially business focused, is it the metamodels and notation language used to characterize design?
The simple answer is that EA has been and will be what it needs to be to those who need it. There is no one approach now because no one approach will handle all of the related duties that architects are saddled with. The more important question has always been, will we find a way in which we can coordinate the various types of EA activities.
This is an exact corollary to the questions that helped launch EA as a discipline of set of disciplines some twenty to thirty years ago. At that time pioneers in EA were looking for a way in which they could combine and coordinate the complexity of their systems environments.
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