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The ISO/IEC 11179 metadata registry (MDR) standard is an international ISO/IEC standard for representing metadata for an organization in a metadata registry. It documents the standardization and registration of metadata to make data understandable and shareable. [1]
Organizations exchange data between computer systems precisely using enterprise application integration technologies. Completed transactions are often transferred to separate data warehouse and business rules systems with structures designed to support data for analysis. A de facto standard model for data integration platforms is the Common Warehouse Metamodel (CWM). Data integration is often also solved as a problem of data, rather than metadata, with the use of so-called master data. ISO/IEC 11179 claims that it is a standard for metadata-driven exchange of data in an heterogeneous environment, based on exact definitions of data.
The ISO/IEC 11179 model is a result of two principles of semantic theory, combined with basic principles of data modelling.
The first principle from semantic theory is the thesaurus type relation between wider and more narrow (or specific) concepts, e.g. the wide concept "income" has a relation to the more narrow concept "net income".
The second principle from semantic theory is the relation between a concept and its representation, e.g., "buy" and "purchase" are the same concept although different terms are used.
A basic principle of data modelling is the combination of an object class and a characteristic. For example, "Person - hair color".
When applied to data modelling, ISO/IEC 11179 combines a wide "concept" with an "object class" to form a more specific "data element concept". For example, the high-level concept "income" is combined with the object class "person" to form the data element concept "net income of person". Note that "net income" is more specific than "income".
The different possible representations of a data element concept are then described with the use of one or more data elements. Differences in representation may be a result of the use of synonyms or different value domains in different data sets in a data holding. A value domain is the permitted range of values for a characteristic of an object class. An example of a value domain for "sex of person" is "M = Male, F = Female, U = Unknown". The letters M, F and U are then the permitted values of sex of person in a particular data set.
The data element concept "monthly net income of person" may thus have one data element called "monthly net income of individual by 100 dollar groupings" and one called "monthly net income of person range 0-1000 dollars", etc., depending on the heterogeneity of representation that exists within the data holdings covered by one ISO/IEC 11179 registry. Note that these two examples have different terms for the object class (person/individual) and different value sets (a 0-1000 dollar range as opposed to 100 dollar groupings).
The result of this is a catalogue of sorts, in which related data element concepts are grouped by a high-level concept and an object class, and data elements grouped by a shared data element concept. Strictly speaking, this is not a hierarchy, even if it resembles one.
ISO/IEC 11179 proper does not describe data as it is actually stored. It does not refer to the description of physical files, tables and columns. The ISO/IEC 11179 constructs are "semantic" as opposed to "physical" or "technical".
The standard has two main purposes: definition and exchange. The core object is the data element concept, since it defines a concept and, ideally, describes data independent of its representation in any one system, table, column or organisation.
The standard consists of seven parts:
Part 1 explains the purpose of each part. Part 3 specifies the metamodel that defines the registry. Part 7 is released per December 2019 and provides an extension to part 3 for registration of metadata about data sets. The other parts specify various aspects of the use of the registry.
The data element is foundational concept in an ISO/IEC 11179 metadata registry. The purpose of the registry is to maintain a semantically precise structure of data elements.
Each Data element in an ISO/IEC 11179 metadata registry:
Data elements that store "Codes" or enumerated values must also specify the semantics of each of the code values with precise definitions.
Software AG's COTS Metadata Registry (MDR) product supports the ISO 11179 standard and continues to be sold and used for this purpose in both commercial and government applications (see Vendor Tools section below).
While commercial adoption is increasing, the spread of ISO/IEC 11179 has been more successful in the public sector. However, the reason for this is unclear. ISO membership is open to organizations through their national bodies. Countries with public sector repositories across various industries include Australia, Canada, Germany, United States and the United Kingdom.
The United Nations and the US Government refer to and use the 11179 standards. 11179 is strongly recommended on the U.S. government's XML website. [2] and is promoted by The Open Group as a foundation of the Universal Data Element Framework. [3] The Open Group is a vendor-neutral and technology-neutral consortium working to enable access to integrated information within and between enterprises based on open standards and global interoperability.
Although the ISO/IEC 11179 metadata registry is 6-part standard comprising several hundreds of pages, the primary model is presented in Part-3 and depicted in UML diagrams to facilitate understanding, supported by normative text. The eXtended Metadata Registry initiative, XMDR led by the US, explored the use of ontologies as the basis for MDR content in order to provide richer semantic framework than could be achieved by lexical and syntax naming conventions alone. The XMDR experimented with a prototype using OWL, RDF and SPARQL to prove the concept. The initiative resulted in Edition 3 of ISO/IEC 11179. The first part published is ISO/IEC 11179-3:2013. The primary extension in Edition 3 is the Concept Region, expanding the use of concepts to more components within the standard, and supporting registration of a Concept system for use within the registry. The standard also supports the use of externally defined concept systems. Edition 3 versions of Parts 1, 5, and 6 were published in 2015. Part 2, Classifications, is subsumed by the Concept Region in Part 3, but is being updated to a Technical Report (TR) to provide guidance on the development of Classification Schemes. Part 4 describes principles for forming data definitions; an Edition 3 has not been proposed.
The following metadata registries state that they follow ISO/IEC 11179 guidelines although there have been no formal third party tests developed to test for metadata registry compliance.
No independent agencies certify ISO/IEC 11179 compliance.
In metadata, the term data element is an atomic unit of data that has precise meaning or precise semantics. A data element has:
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A representation term is a word, or a combination of words, that semantically represent the data type of a data element. A representation term is commonly referred to as a class word by those familiar with data dictionaries. ISO/IEC 11179-5:2005 defines representation term as a designation of an instance of a representation class As used in ISO/IEC 11179, the representation term is that part of a data element name that provides a semantic pointer to the underlying data type. A Representation class is a class of representations. This representation class provides a way to classify or group data elements.
A data element name is a name given to a data element in, for example, a data dictionary or metadata registry. In a formal data dictionary, there is often a requirement that no two data elements may have the same name, to allow the data element name to become an identifier, though some data dictionaries may provide ways to qualify the name in some way, for example by the application system or other context in which it occurs.
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Semantic translation is the process of using semantic information to aid in the translation of data in one representation or data model to another representation or data model. Semantic translation takes advantage of semantics that associate meaning with individual data elements in one dictionary to create an equivalent meaning in a second system.
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