ISO/IEC 11179

ISO/IEC 11179 (formally known as the ISO/IEC 11179 Metadata Registry (MDR) standard) is an international standard for representing metadata for an organization in a metadata registry.

Intended purpose

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.

Structure of an ISO/IEC 11179 metadata registry

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.

Structure of the ISO/IEC 11179 standard

The standard consists of six parts:

  • ISO/IEC 11179-1:2015 Framework (referred to as ISO/IEC 11179-1)
  • ISO/IEC 11179-2:2005 Classification
  • ISO/IEC 11179-3:2013 Registry metamodel and basic attributes
  • ISO/IEC 11179-4:2004 Formulation of data definitions
  • ISO/IEC 11179-5:2015 Naming and identification principles
  • ISO/IEC 11179-6:2015 Registration

Part 1 explains the purpose of each part. Part 3 specifies the metamodel that defines the registry. The other parts specify various aspects of the use of the registry.

An additional part, Part 7: Datasets is currently under development.

Overview of 11179 Data Element

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:

  • should be registered according to the Registration guidelines (11179-6)
  • will be uniquely identified within the register (11179-5)
  • should be named according to Naming and Identification Principles (11179-5) See data element name
  • should be defined by the Formulation of Data Definitions rules (11179-4) See data element definition and
  • may be classified in a Classification Scheme (11179-2) See classification scheme

Data elements that store "Codes" or enumerated values must also specify the semantics of each of the code values with precise definitions.

Adoption of 11179 standards

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, it is unclear the reason for this. 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.[1] and is promoted by The Open Group as a foundation of the Universal Data Element Framework.[2] 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.

Extensions to the ISO/IEC 11179 standard

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.

Examples of ISO/IEC 11179 metadata registries

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.

Metadata registry vendor tools that claim ISO/IEC 11179 compliance

No independent agencies certify ISO/IEC 11179 compliance.

See also



  1. ^ US Government's XML web site
  2. ^ Universal Data Element Framework



The AgMES (Agricultural Metadata Element set) initiative was developed by the Food and Agriculture Organization (FAO) of the United Nations and aims to encompass issues of semantic standards in the domain of agriculture with respect to description, resource discovery, interoperability and data exchange for different types of information resources.

There are numerous other metadata schemas for different types of information resources. The following list contains a list of a few examples:

Document-like Information Objects (DLIOs): Dublin Core, Agricultural Metadata Element Set (AgMES)

Events: VCalendar

Geographic and Regional Information: Geographic information—Metadata ISO/IEC 11179 Standards

Persons: Friend-of-a-friend (FOAF), vCard

Plant Production and Protection: Darwin Core (1.0 and 2.0) (DwC)AgMES as a namespace is designed to include agriculture specific extensions for terms and refinements from established standard metadata namespaces like Dublin Core, AGLS etc. Thus to be used for Document-like Information Objects, for example like publications, articles, books, web sites, papers, etc., it will have to be used in conjunction with the standard namespaces mentioned before. The AgMES initiative strives to achieve improved interoperability between information resources in agricultural domain by enabling means for exchange of information.

Describing a DLIO with AgMES means exposing its major characteristics and contents in a standard way that can be reused easily in any information system. The more institutions and organizations in the agricultural domain that use AgMES to describe their DLIOs, the easier it will be to interchange data in between information systems like digital libraries and other repositories of agricultural information.

Aristotle Metadata Registry

The Aristotle Metadata Registry (or Aristotle-MDR) is an open-source Metadata Registry framework based on the ISO/IEC 11179 standard for Metadata Registries. It is influenced by the AIHW Meteor Metadata Registry and the Canadian Institute of Health Information Indicator Bank. Aristotle-MDR is designed to describe data holdings databases and associated structural metadata. The Aristotle Metadata Registry was publicly launched at the 2015 IASSIST Conference in Toronto. In 2016, the founders of the Aristotle Metadata Registry were hired by Data61 (a division of CSIRO) to continue development of the platform.

In June 2018, the CSIRO and the Australian Institute of Health and Welfare entered an agreement to replace the current METeOR metadata registry with a system build on the Aristotle Metadata Registry

Comparison and contrast of classification schemes in linguistics and metadata

A classification scheme is the product of arranging things into kinds of things (classes) or into groups of classes.

In the abstract, the resulting structures are a crucial aspect of metadata, often represented as a hierarchical structure and accompanied by descriptive information of the classes or groups. Such a classification scheme is intended to be used for an arrangement or division of individual objects into the classes or groups, and the classes or groups are based on characteristics which the objects (members) have in common.

In linguistics, subordinate concepts are described as hyponyms of their respective superordinates; typically, a hyponym is 'a kind of' its superordinate.The ISO/IEC 11179 metadata registry standard uses classification schemes as a way to classify administered items, such as data elements, in a metadata registry.

Some quality criteria for classification schemes are:

Whether different kinds are grouped together. In other words, whether it is a grouping system or a pure classification system. In case of grouping, a subset (subgroup) does not have (inherit) all the characteristics of the superset, which makes that the knowledge and requirements about the superset are not applicable for the members of the subset.

Whether the classes have overlaps.

Whether subordinates (may) have multiple superordinates. Some classification schemes allow that a kind of thing has more than one superordinate others don't. Multiple supertypes for one subtype implies that the subordinate has the combined characteristics of all its superordinates. This is called multiple inheritance (of characteristics from multiple superordinates to their subordinates).

Whether the criteria for belonging to a class or group are well defined.

Whether the kinds of relations between the concepts are made explicit and well defined.

Whether subtype-supertype relations are distinguished from composition relations (part-whole relations) and from object-role relations.

Data Reference Model

The Data Reference Model (DRM) is one of the five reference models of the Federal Enterprise Architecture.

Data element

In metadata, the term data element is an atomic unit of data that has precise meaning or precise semantics. A data element has:

An identification such as a data element name

A clear data element definition

One or more representation terms

Optional enumerated values Code (metadata)

A list of synonyms to data elements in other metadata registries Synonym ringData elements usage can be discovered by inspection of software applications or application data files through a process of manual or automated Application Discovery and Understanding. Once data elements are discovered they can be registered in a metadata registry.

In telecommunication, the term data element has the following components:

A named unit of data that, in some contexts, is considered indivisible and in other contexts may consist of data items.

A named identifier of each of the entities and their attributes that are represented in a database.

A basic unit of information built on standard structures having a unique meaning and distinct units or values.

In electronic record-keeping, a combination of characters or bytes referring to one separate item of information, such as name, address, or age.In the areas of databases and data systems more generally a data element is a concept forming part of a data model. As an element of data representation, a collection of data elements forms a data structure.

Data element definition

In metadata, a data element definition is a human readable phrase or sentence associated with a data element within a data dictionary that describes the meaning or semantics of a data element.

Data element definitions are critical for external users of any data system. Good definitions can dramatically ease the process of mapping one set of data into another set of data. This is a core feature of distributed computing and intelligent agent development.

There are several guidelines that should be followed when creating high-quality data element definitions.

Data element name

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.

In a database driven data dictionary, the fully qualified data element name may become the primary key, or an alternate key, of a Data Elements table of the data dictionary.

The data element name typically conforms to ISO/IEC 11179 metadata registry naming conventions and has at least three parts:

Object, Property and Representation term.Many standards require the use of Upper camel case to differentiate the components of a data element name. This is the standard used by ebXML, GJXDM and NIEM.


The Global Justice XML Data Model (GJXDM or Global JXDM) is a data reference model for the exchange of information within the justice and public safety communities. The Global JXDM is a product of the Global Justice Information Sharing Initiative's (Global) Infrastructure and Standards Working Group (ISWG), and was developed by the Global ISWG's XML Structure Task Force (XSTF).

The Global JXDM is a comprehensive product that includes a data model, a data dictionary, and an XML schema that together is known as the Global JXDM. Global JXDM is independent of vendors, operating systems, storage media, and applications and is quickly becoming key technology for assisting how criminal and judicial organizations exchange information. The Global JXDM is sponsored by the United States Department of Justice (DOJ), Office of Justice Programs (OJP), with development supported by the Global XML Structure Task Force (GXSTF), which works closely with researchers at the Georgia Tech Research Institute (GTRI). New releases are issued by the GXSTF, which reviews and evaluates each version of the Global JXDM. The GXSTF solicits feedback from technical experts and practitioners in both industry and government and authorizes Global JXDM changes based on this feedback. All approved additions, deletions, and modifications are applied to future releases, with a cumulative change log published along with each release. When a reasonable number of updates are approved by the GXSTF, a new version is released.

The Global JXDM is an XML standard designed specifically for criminal justice information exchanges, providing law enforcement, public safety agencies, prosecutors, public defenders, and the judicial branch with a tool to effectively share data and information in a timely manner. The Global JXDM removes the burden from agencies to independently create exchange standards, and because of its extensibility, there is more flexibility to deal with unique agency requirements and changes. Through the use of a common vocabulary that is understood system-to-system, the Global JXDM enables access from multiple sources and reuse in multiple applications.


An identifier is a name that identifies (that is, labels the identity of) either a unique object or a unique class of objects, where the "object" or class may be an idea, physical [countable] object (or class thereof), or physical [noncountable] substance (or class thereof). The abbreviation ID often refers to identity, identification (the process of identifying), or an identifier (that is, an instance of identification). An identifier may be a word, number, letter, symbol, or any combination of those.

The words, numbers, letters, or symbols may follow an encoding system (wherein letters, digits, words, or symbols stand for (represent) ideas or longer names) or they may simply be arbitrary. When an identifier follows an encoding system, it is often referred to as a code or ID code. For instance the ISO/IEC 11179 metadata registry standard defines a code as system of valid symbols that substitute for longer values in contrast to identifiers without symbolic meaning. Identifiers that do not follow any encoding scheme are often said to be arbitrary IDs; they are arbitrarily assigned and have no greater meaning. (Sometimes identifiers are called "codes" even when they are actually arbitrary, whether because the speaker believes that they have deeper meaning or simply because they are speaking casually and imprecisely.)

The unique identifier (UID) is an identifier that refers to only one instance—only one particular object in the universe. A part number is an identifier, but it is not a unique identifier—for that, a serial number is needed, to identify each instance of the part design. Thus the identifier "Model T" identifies the class (model) of automobiles that Ford's Model T comprises; whereas the unique identifier "Model T Serial Number 159,862" identifies one specific member of that class—that is, one particular Model T car, owned by one specific person.

The concepts of name and identifier are denotatively equal, and the terms are thus denotatively synonymous; but they are not always connotatively synonymous, because code names and ID numbers are often connotatively distinguished from names in the sense of traditional natural language naming. For example, both "Jamie Zawinski" and "Netscape employee number 20" are identifiers for the same specific human being; but normal English-language connotation may consider "Jamie Zawinski" a "name" and not an "identifier", whereas it considers "Netscape employee number 20" an "identifier" but not a "name". This is an emic indistinction rather than an etic one.

Indicator (metadata)

In metadata an indicator is a Boolean value that may contain only the values true or false. The definition of an Indicator must include the meaning of a true value and should also include the meaning if the value is false.

If a data element may take another value to represent e.g. unknown or not applicable, then a Code should be used instead of an Indicator, and the meanings of all possible values should be clearly defined.

The suffix Indicator is used in ISO/IEC 11179 metadata registry standard as a representation term.


METeOR (Metadata Online Registry), Australia’s repository for national metadata standards for health, housing and community services statistics and information. METeOR is a Metadata registry based on the 2003 version of the ISO/IEC 11179 Information technology - Metadata registries standard. METeOR was developed by the Australian Institute of Health and Welfare to store, manage and disseminate metadata in the Australian health, community services and housing assistance sectors.In August 2018, the AIHW entered an agreement with the CSIRO for the supply of a new platform for the METeOR registry.

Measure (data warehouse)

In a data warehouse, a measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made.

Metadata registry

A metadata registry is a central location in an organization where metadata definitions are stored and maintained in a controlled method.

A metadata repository is the database where metadata is stored. The registry also adds relationships with related metadata types.

Metadata standard

A metadata standard is a requirement which is intended to establish a common understanding of the meaning or semantics of the data, to ensure correct and proper use and interpretation of the data by its owners and users. To achieve this common understanding, a number of characteristics, or attributes of the data have to be defined, also known as metadata.

National Information Exchange Model

The National Information Exchange Model (NIEM) ( neem) is an XML-based information exchange framework from the United States. NIEM represents a collaborative partnership of agencies and organizations across all levels of government (federal, state, tribal, and local) and with private industry. The purpose of this partnership is to effectively and efficiently share critical information at key decision points throughout the whole of the justice, public safety, emergency and disaster management, intelligence, and homeland security enterprise. NIEM is designed to develop, disseminate, and support enterprise-wide information exchange standards and processes that will enable jurisdictions to automate information sharing.

NIEM is an outgrowth of the United States Department of Justice's Global Justice XML Data Model (GJXDM) project. NIEM is now being expanded to include other federal and state agencies such as the Office of the Director of National Intelligence, United States Department of Defense, Federal Bureau of Investigation, Texas, Florida, New York, Pennsylvania, and others.

Oracle Enterprise Metadata Manager

The Oracle Enterprise Metadata Manager (EMM) is a product of the Oracle Corporation that provides an ISO/IEC 11179 metadata registry.

Representation class

A representation term is a word, or a combination of words, used as part of a data element name. Representation class is sometimes used as a synonym for representation term.

In ISO/IEC 11179, a representation class provides a way to classify or group data elements. A representation class is effectively a specialized classification scheme. Hence, there is currently some discussion in ISO over the merits of keeping representation class as a separate entity in 11179, versus collapsing it into the general classification scheme facility[1]. A clear distinction between the two mechanisms, however, is that 11179 allows a data element to be classified by only one representation class, whereas there is no such restriction on other classification schemes.

ISO/IEC 11179 does not specify that representation terms should be drawn from the values of representation class, though it would make sense to do so, nor does it provide any mechanism to ensure any sort of consistency (whatever that might mean) between the representation terms used to name a data element, and the representation class used to classify it.

The term representation class has been used in metadata registry standards for many years. Today it has a combination of meanings and now goes well beyond how a data element is represented in a computer system. In practice this term is used to shed light on the semantics or meaning of the data element.

Representation term

A representation term is a word, or a combination of words, that semantically represent the data type (value domain) 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 Representation Term may be thought of as an attribute of a data element in a metadata registry that classifies the data element according to the type of data stored in the data element.[1]Representation terms are typically "approved" by the organization or standards body using them. For example, the UN publishes its approved list as part of the UN/CEFACT Core Components Technical Specification. The Universal Data Element Framework uses a subset of CCTS representation terms and assigns numeric codes to those used.


The Extended Metadata Registry (XMDR) is a project proposing and testing a set of extensions to the ISO/IEC 11179 metadata registry specifications that deal with the development of improved standards and technology for storing and retrieving the semantics of data elements, terminologies, and concept structures in metadata registries.

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