metadata

 

Metadata is data about data. An item of metadata may describe an individual datum, or content item, or a collection of data including multiple content items.

Metadata (sometimes written 'meta data') is used to facilitate the understanding, use and management of data. The metadata required for effective data management varies with the type of data and context of use. In a library, where the data is the content of the titles stocked, metadata about a title would typically include a description of the content, the author, the publication date and the physical location. In the context of a camera, where the data is the photographic image, metadata would typically include the date the photograph was taken and details of the camera settings. On a portable music player such as an Apple iPod, the album names, song titles and album art embedded in the music files are used to generate the artist and song listings, and are metadata. In the context of an information system, where the data is the content of the computer files, metadata about an individual data item would typically include the name of the field and its length. Metadata about a collection of data items, a computer file, might typically include the name of the file, the type of file and the name of the data administrator.

What is metadata?

Any item of data is a description of something. Metadata is a type of data where the something being described is data. Or, as it is often put, metadata is data about data. If we consider a particular place in the real world, this may be described by many items of data, for example:

• 1 “E83BJ”
• 2 “17”
• 3 “Sunny”

To make sense of and use this data, it is necessary to have access to some form of description of the sort of data it is, or, in other words, have access to its metadata. So, for example, the metadata for the above three items of data might include:

• 1.1 “Post Code” – This is a description of the data item “E83BJ”
• 1.2 “The unique identifier of a postal district" - This is another description of “E83BJ”
• 1.3 “27th June 2006” – This is another description of “E83BJ”
• 2 “Average temperature in degrees Celsius” – This is a description of “17”
• 3 "Yesterday’s weather”. – This is a description of “sunny”

An item of metadata is itself data and therefore may have its own metadata. This might (not particularly usefully) be referred to as meta-metadata. So, for example, “Post Code” might have the following metadata:

• 1.1.1 “data item name”
• 1.1.2 “8 characters, starting with A – Z”

“27th June 2006” might have the following metadata:

• 1.3.1 “Date last changed”

The hierarchy of data, metadata, meta-metadata etc. can go on forever. Fortunately we have sufficient background knowledge so that we can usually make sense of and use an item of data with access to very little, if any, formally defined metadata. So, for example, with the “Post Code” metadata “8 characters, starting with A – Z” , it would be possible using background knowledge to know that this is a description of the format of a post code, without having access to any defined metadata for “8 characters, starting with A – Z”.

Levels

As indicated, there are hierarchies of data and metadata. However, any particular item of data may be on different levels of a hierarchy depending on the context. For example, when considering the geography of London, “E83BJ” would be data and “Post Code” would be metadata. But, when considering the data management of an automated system that manages geographical data, “Post Code” might be data and then “data item name” and “8 characters, starting with A – Z” would be metadata.

In any particular context, metadata must be at a higher level of abstraction than the data it is describing. So, in relation to “E83BJ”, the item of data “is in London” is a further description of the place in the real world which has the post code “E83BJ” and is at the same level of abstraction. Therefore, although it is providing information about “E83BJ” (It is telling us that this is the post code of a place in London) this would not normally be considered metadata, as it is describing “E83BJ” qua place in the real world and not qua data.

Definitions

The term was introduced intuitively, without a formal definition. Because of that, today there are various definitions. The most common one is the literal translation:

• Metadata is data about data.

Example: "12345" is data, and with no additional context is meaningless. When "12345" is given a meaningful name (metadata) of "ZIP code", one can understand (at least in the United States, and further placing "ZIP code" within the context of a postal address) that "12345" refers to the General Electric plant in Schenectady, New York.

As for most people the difference between data and information is merely a philosophical one of no relevance in practical use, other definitions are:

• Metadata is information about data.
• Metadata is information about information.

There are more sophisticated definitions, such as:

• "Metadata is structured, encoded data that describe characteristics of information-bearing entities to aid in the identification, discovery, assessment, and management of the described entities."^[1]
• "[Metadata is a set of] optional structured descriptions that are publicly available to explicitly assist in locating objects."^[2]

These are used more rarely because they tend to concentrate on one purpose of metadata — to find "objects", "entities" or "resources" — and ignore others, such as using metadata to optimize compression algorithms, or to perform additional computations using the data.

The metadata concept has been extended into the world of systems to include any "data about data": the names of tables, columns, programs, and the like. Different views of this "system metadata" are detailed below, but beyond that is the recognition that metadata can describe all aspects of systems: data, activities, people and organizations involved, locations of data and processes, access methods, limitations, timing and events, as well as motivation and rules.

Fundamentally, then, metadata is "the data that describe the structure and workings of an organization's use of information, and which describe the systems it uses to manage that information". To do a model of metadata is to do an "Enterprise model" of the information technology industry itself.^[3]

Hierarchies of metadata

When structured into a hierarchical arrangement, metadata is more properly called an ontology or schema. Both terms describe "what exists" for some purpose or to enable some action. For instance, the arrangement of subject headings in a library catalog serves not only as a guide to finding books on a particular subject in the stacks, but also as a guide to what subjects "exist" in the library's own ontology and how more specialized topics are related to or derived from the more general subject headings.

Metadata is frequently stored in a central location and used to help organizations standardize their data. This information is typically stored in a metadata registry.

Difference between data and metadata

Usually it is not possible to distinguish between (raw) data and metadata because:

• Something can be data and metadata at the same time. The headline of an article is both its title (metadata) and part of its text (data).
• Data and metadata can change their roles. A poem, as such, would be regarded as data, but if there were a song that used it as lyrics, the whole poem could be attached to an audio file of the song as metadata. Thus, the labeling depends on the point of view.

These considerations apply no matter which of the above definitions is considered.

Use

Metadata has many different applications; this section lists some of the most common.

Metadata is used to speed up and enrich searching for resources. In general, search queries using metadata can save users from performing more complex filter operations manually. It is now common for web browsers (with the notable exception of Mozilla Firefox), P2P applications and media management software to automatically download and locally cache metadata, to improve the speed at which files can be accessed and searched ^{[citation needed]}.

Metadata may also be associated to files manually. This is often the case with documents which are scanned into a document storage repository such as FileNet or Documentum. Once the documents have been converted into an electronic format a user brings the image up in a viewer application, manually reads the document and keys values into an online application to be stored in a metadata repository.

Metadata provide additional information to users of the data it describes. This information may be descriptive ("These pictures were taken by children in the school's third grade class.") or algorithmic ("Checksum=139F").

Metadata helps to bridge the semantic gap. By telling a computer how data items are related and how these relations can be evaluated automatically, it becomes possible to process even more complex filter and search operations. For example, if a search engine understands that "Van Gogh" was a "Dutch painter", it can answer a search query on "Dutch painters" with a link to a web page about Vincent Van Gogh, although the exact words "Dutch painters" never occur on that page. This approach, called knowledge representation, is of special interest to the semantic web and artificial intelligence.

Certain metadata is designed to optimize lossy compression algorithms. For example, if a video has metadata that allows a computer to tell foreground from background, the latter can be compressed more aggressively to achieve a higher compression rate.

Some metadata is intended to enable variable content presentation. For example, if a picture has metadata that indicates the most important region — the one where there is a person — an image viewer on a small screen, such as on a mobile phone's, can narrow the picture to that region and thus show the user the most interesting details. A similar kind of metadata is intended to allow blind people to access diagrams and pictures, by converting them for special output devices or reading their description using text-to-speech software.

Other descriptive metadata can be used to automate workflows. For example, if a "smart" software tool knows content and structure of data, it can convert it automatically and pass it to another "smart" tool as input. As a result, users save the many copy-and-paste operations required when analyzing data with "dumb" tools.

Metadata is becoming an increasingly important part of electronic discovery. [1] Application and file system metadata derived from electronic documents and files can be important evidence. Recent changes to the Federal Rules of Civil Procedure make metadata routinely discoverable as part of civil litigation. Parties to litigation are required to maintain and produce metadata as part of discovery, and spoliation of metadata can lead to sanctions.

Metadata has become important on the World Wide Web because of the need to find useful information from the mass of information available. Manually-created metadata adds value because it ensures consistency. If a web page about a certain topic contains a word or phrase, then all web pages about that topic should contain that same word or phrase. Metadata also ensures variety, so that if a topic goes by two names each will be used. For example, an article about "sport utility vehicles" would also be tagged "4 wheel drives", "4WDs" and "four wheel drives", as this is how SUVs are known in some countries.

Examples of metadata for an audio CD include the MusicBrainz project and AMG's All Music Guide. Similarly, MP3 files have metadata tags in a format called ID3.

Types of metadata

Metadata can be classified by:

• Content. Metadata can either describe the resource itself (for example, name and size of a file) or the content of the resource (for example, "This video shows a boy playing football").
• Mutability. With respect to the whole resource, metadata can be either immutable (for example, the "Title" of a video does not change as the video itself is being played) or mutable (the "Scene description" does change).
• Logical function. There are three layers of logical function: at the bottom the subsymbolic layer that contains the raw data itself, then the symbolic layer with metadata describing the raw data, and on the top the logical layer containing metadata that allows logical reasoning using the symbolic layer.

Important issues

To successfully develop and use metadata, several important issues should be treated with care:

Metadata risks

Microsoft Office files include metadata beyond their printable content, such as the original author's name, the creation date of the document, and the amount of time spent editing it. Unintentional disclosure can be awkward or even raise malpractice concerns. Some of Microsoft Office document's metadata can be seen by clicking File then Properties from the program's menu. Other metadata is not visible except through external analysis of a file, such as is done in forensics. The author of the Microsoft Word-based Melissa computer virus in 1999 was caught due to Word metadata that uniquely identified the computer used to create the original infected document.

Metadata lifecycle

Even in the early phases of planning and designing it is necessary to keep track of all metadata created. It is not economical to start attaching metadata only after the production process has been completed. For example, if metadata created by a digital camera at recording time is not stored immediately, it may have to be restored afterwards manually with great effort. Therefore, it is necessary for different groups of resource producers to cooperate using compatible methods and standards.

• Manipulation. Metadata must adapt if the resource it describes changes. It should be merged when two resources are merged. These operations are seldom performed by today's software; for example, image editing programs usually do not keep track of the Exif metadata created by digital cameras.
• Destruction. It can be useful to keep metadata even after the resource it describes has been destroyed, for example in change histories within a text document or to archive file deletions due to digital rights management. None of today's metadata standards considers this phase.

Storage

Metadata can be stored either internally, in the same file as the data, or externally, in a separate file. Both ways have advantages and disadvantages:

• Internal storage allows transferring metadata together with the data it describes; thus, metadata is always at hand and can be manipulated easily. This method creates high redundancy and does not allow holding metadata together.
• External storage allows bundling metadata, for example in a database, for more efficient searching. There is no redundancy and metadata can be transferred simultaneously when using streaming. However, as most formats use URIs for that purpose, the method of how the metadata is linked to its data should be treated with care. What if a resource does not have an URI (resources on a local hard disk or web pages that are created on-the-fly using a content management system)? What if metadata can only be evaluated if there is a connection to the Web, especially when using RDF? How to realize that a resource is replaced by another with the same name but different content?

Moreover, there is the question of data format: storing metadata in a human-readable format such as XML can be useful because users can understand and edit it without specialized tools. On the other hand, these formats are not optimized for storage capacity; it may be useful to store metadata in a binary, non-human-readable format instead to speed up transfer and save memory.

Criticisms

Although the majority of computer scientists see metadata as a chance for better interoperability, some critics argue:

• Metadata is too expensive and time-consuming. The argument is that companies will not produce metadata without need because it costs extra money, and private users also will not produce complex metadata because its creation is very time-consuming.
• Metadata is too complicated. Private users will not create metadata because existing formats, especially MPEG-7, are too complicated. As long as there are no automatic tools for creating metadata, it will not be created.
• Metadata is subjective and depends on context. Most probably, two persons will attach different metadata to the same resource due to their different points of view. Moreover, metadata can be misinterpreted due to its dependency on context. For example searching for "post-modern art" may miss a certain item because the expression was not in use at the time when that work of art was created, or searching for "pictures taken at 1:00" may produce confusing results due to local time differences.
• There is no end to metadata. For example, when annotating a match of soccer with metadata, one can describe all the players and their actions in time and stop there. One can also describe the advertisements in the background and the clothes the players wear. One can also describe each fan on the tribune and the clothes they wear. All of this metadata can be interesting to one party or another — such as the spectators, sponsors or a counterterrorist unit of the police — and even for a simple resource the amount of possible metadata can be gigantic.
• Metadata is useless. Many of today's search engines allow finding text very efficiently. Other techniques for finding pictures, videos and music (namely query-by-example) will become more and more powerful in the future. Thus, there is no real need for metadata.

The opposers of metadata sometimes use the term metacrap to refer to the unsolved problems of metadata in some scenarios.

Types

In general, there are two distinct classes of metadata: structural or control metadata and guide metadata.^[4] Structural metadata is used to describe the structure of computer systems such as tables, columns and indexes. Guide metadata is used to help humans find specific items and is usually expressed as a set of keywords in a natural language.

Metatadata can be divided into 3 distinct categories:

• Descriptive
• Administrative
• Structural

Relational database metadata

Each relational database system has its own mechanisms for storing metadata. Examples of relational-database metadata include:

• Tables of all tables in database, their names, sizes and number of rows in each table.
• Tables of columns in each database, what tables they are used in, and the type of data stored in each column.

In database terminology, this set of metadata is referred to as the catalog. The SQL standard specifies a uniform means to access the catalog, called the INFORMATION_SCHEMA, but not all databases implement it, even if they implement other aspects of the SQL standard. For an example of database-specific metadata access methods, see Oracle metadata.

Data warehouse metadata

Data warehouse metadata systems are sometimes separated into two sections:

1. back room metadata that are used for Extract, transform, load functions to get OLTP data into a data warehouse
2. front room metadata that are used to label screens and create reports

Kimball^[5] lists the following types of metadata in a data warehouse (See also [2]):

• source system metadata
  • source specifications, such as repositories, and source schemas
  • source descriptive information, such as ownership descriptions, update frequencies, legal limitations, and access methods
  • process information, such as job schedules and extraction code
• data staging metadata
  • data acquisition information, such as data transmission scheduling and results, and file usage
  • dimension table management, such as definitions of dimensions, and surrogate key assignments
  • transformation and aggregation, such as data enhancement and mapping, DBMS load scripts, and aggregate definitions
  • audit, job logs and documentation, such as data lineage records, data transform logs
• DBMS metadata, such as:
  • DBMS system table contents
  • processing hints

Michael Bracket defines metadata (what he calls "Data resource data") as "any data about the organization’s data resource".^[6] Adrienne Tannenbaum defines metadata as "the detailed description of instance data. The format and characteristics of populated instance data: instances and values, dependent on the role of the metadata recipient".^[7] These definitions are characteristic of the "data about data" definition.

Business Intelligence metadata

Business Intelligence is the process of analyzing large amounts of corporate data, usually stored in large databases such as the Data Warehouse, tracking business performance, detecting patterns and trends, and helping enterprise business users make better decisions. Business Intelligence metadata describes how data is queried, filtered, analyzed, and displayed in Business Intelligence software tools, such as Reporting tools, OLAP tools, Data Mining tools.

Examples:

• OLAP metadata: The descriptions and structures of Dimensions, Cubes, Measures (Metrics), Hierarchies, Levels, Drill Paths
• Reporting metadata: The descriptions and structures of Reports, Charts, Queries, DataSets, Filters, Variables, Expressions
• Data Mining metadata: The descriptions and structures of DataSets, Algorithms, Queries

Business Intelligence metadata can be used to understand how corporate financial reports reported to Wall Street are calculated, how the revenue, expense and profit are aggregated from individual sales transactions stored in the data warehouse. A good understanding of Business Intelligence metadata is required to solve complex problems such as compliance with corporate governance standards, such as Sarbanes Oxley (SOX) or Basel II.

General IT metadata

In contrast, David Marco, another metadata theorist, defines metadata as "all physical data and knowledge from inside and outside an organization, including information about the physical data, technical and business processes, rules and constraints of the data, and structures of the data used by a corporation."^[8] Others have included web services, systems and interfaces. In fact, the entire Zachman framework (see Enterprise Architecture) can be represented as metadata.^[9]

Notice that such definitions expand metadata's scope considerably, to encompass most or all of the data required by the Management Information Systems capability. In this sense, the concept of metadata has significant overlaps with the ITIL concept of a Configuration Management Database (CMDB), and also with disciplines such as Enterprise Architecture and IT portfolio management.

This broader definition of metadata has precedent. Third generation corporate repository products (such as those eventually merged into the CA Advantage line) not only store information about data definitions (COBOL copybooks, DBMS schema), but also about the programs accessing those data structures, and the Job Control Language and batch job infrastructure dependencies as well. These products (some of which are still in production) can provide a very complete picture of a mainframe computing environment, supporting exactly the kinds of impact analysis required for ITIL-based processes such as Incident and Change Management. The ITIL Back Catalogue includes the Data Management volume which recognizes the role of these metadata products on the mainframe, posing the CMDB as the distributed computing equivalent. CMDB vendors however have generally not expanded their scope to include data definitions, and metadata solutions are also available in the distributed world. Determining the appropriate role and scope for each is thus a challenge for large IT organizations requiring the services of both.

Since metadata is pervasive, centralized attempts at tracking it need to focus on the most highly leveraged assets. Enterprise Assets may only constitute a small percentage of the entire IT portfolio.

Some practitioners have successfully managed IT metadata using the Dublin Core metamodel.^[10]

IT metadata management products

First generation data dictionary/metadata repository tools would be those only supporting a specific DBMS, such as IDMS's IDD (integrated data dictionary), the IMS Data Dictionary, and Adabas's Predict.

Second generation would be ASG's DATAMANAGER product which could support many different file and DBMS types.

Third generation repository products became briefly popular in the early 1990s along with the rise of widespread use of RDBMS engines such as IBM's DB2.

Fourth generation products link the repository with more Extract, transform, load tools and can be connected with architectural modeling tools. Examples include Adaptive Metadata Manager from Adaptive, Rochade from ASG,InfoLibrarian Metadata Integration Framework and Troux Technologies Metis Server product.

File system metadata

Nearly all file systems keep metadata about files out-of-band. Some systems keep metadata in directory entries; others in specialized structure like inodes or even in the name of a file. Metadata can range from simple timestamps, mode bits, and other special-purpose information used by the implementation itself, to icons and free-text comments, to arbitrary attribute-value pairs.

With more complex and open-ended metadata, it becomes useful to search for files based on the metadata contents. The Unix find utility was an early example, although inefficient when scanning hundreds of thousands of files on a modern computer system. Apple Computer's current version of its Mac OS X operating system (Tiger) supports cataloguing and searching for file metadata through a feature known as Spotlight. Microsoft worked in the development of similar functionality with the Instant Search system in Windows Vista, as well as being present in SharePoint Server. Linux implements file metadata using extended file attributes.

Image metadata

Examples of image files containing metadata include Exchangeable Image File Format (EXIF) and Tagged Image File Format (TIFF).

Having metadata about images embedded in TIFF or EXIF files is one way of acquiring additional data about an image. Image metadata are attained through tags. Tagging pictures with subjects, related emotions, and other descriptive phrases helps Internet users find pictures easily rather than having to search through entire image collections. A prime example of an image tagging service is Flickr, where users upload images and then describe the contents. Other patrons of the site can then search for those tags. Flickr uses a folksonomy: a free-text keyword system in which the community defines the vocabulary through use rather than through a controlled vocabulary.

Digital photography is increasingly making use of metadata tags. Photographers shooting Camera RAW file formats can use applications such as Adobe Bridge or Apple Computer's Aperture to work with camera metadata for post-processing. Users can also tag photos for organization purposes using Adobe's Extensible Metadata Platform (XMP) language, for example.

Program metadata

Metadata is casually used to describe the controlling data used in software architectures that are more abstract or configurable. Most executable file formats include what may be termed "metadata" that specifies certain, usually configurable, behavioral runtime characteristics. However, it is difficult if not impossible to precisely distinguish program "metadata" from general aspects of stored-program computing architecture; if the machine reads it and acts upon it, it is a computational instruction, and the prefix "meta" has little significance.

In Java, the class file format contains metadata used by the Java compiler and the Java virtual machine to dynamically link classes and to support reflection. The J2SE 5.0 version of Java included a metadata facility to allow additional annotations that are used by development tools.

In MS-DOS, the COM file format does not include metadata, while the EXE file and Windows PE formats do. These metadata can include the company that published the program, the date the program was created, the version number and more.

In the Microsoft .NET executable format, extra metadata is included to allow reflection at runtime.

Existing software metadata

Object Management Group (OMG) has defined metadata format for representing entire existing applications for the purposes of software mining, software modernization and software assurance. This specification, called the OMG Knowledge Discovery Metamodel (KDM) is the OMG's foundation for "modeling in reverse". KDM is a common language-independednt intermediate representation that provides an integrated view of an entire enterpise application, including its behavior (program flow), data, and structure. One of the applications of KDM is Business Rules Mining.

Knowledge Discovery Metamodel includes a fine grained low-level representation (called "micro KDM"), suitable for performing static analysis of programs.

Document metadata

Most programs that create documents, including Microsoft SharePoint, Microsoft Word and other Microsoft Office products, save metadata with the document files. These metadata can contain the name of the person who created the file (obtained from the operating system), the name of the person who last edited the file, how many times the file has been printed, and even how many revisions have been made on the file. Other saved material, such as deleted text (saved in case of an undelete command), document comments and the like, is also commonly referred to as "metadata", and the inadvertent inclusion of this material in distributed files has sometimes led to undesirable disclosures.

Document Metadata is particularly important in legal environments where litigation can request this sensitive information (metadata) which can include many elements of private detrimental data. This data has been linked to multiple lawsuits that have got corporations into legal complications.

Many legal firms today use "Metadata Management Software", also known as "Metadata Removal Tools". This software can be used to clean documents before they are sent outside of their firm. This process, known as metadata management, protects lawfirms from potentially unsafe leaking of sensitive data through Electronic Discovery.

For a list of executable formats, see object file.

Metamodels

Metadata on Models are called Metamodels. In Model Driven Engineering, a Model has to conform to a given Metamodel. According to the MDA guide, a metamodel is a model and each model conforms to a given metamodel. Meta-modeling allows strict and agile automatic processing of models and metamodels.

The Object Management Group (OMG) defines 4 layers of meta-modeling. Each level of modeling is defined, validated by the next layer:

• M0: instance object, data row, record -> "John Smith"
• M1: model, schema -> "Customer" UML Class or database Table
• M2: metamodel -> Unified Modeling Language (UML), Common Warehouse Metamodel (CWM), Knowledge Discovery Metamodel (KDM)
• M3: meta-metamodel -> Meta-Object Facility (MOF)

Strange metadata

Since metadata are also data, it is possible to have metadata of metadata–"meta-metadata." Machine-generated meta-metadata, such as the reversed index created by a free-text search engine, is generally not considered metadata, though.

Metadata that are embedded with content is called embedded metadata. A data repository typically stores the metadata detached from the data.

Digital library metadata

There are three categories of metadata that are frequently used to describe objects in a digital library [3][4]:

1. descriptive - Information describing the intellectual content of the object, such as MARC cataloguing records, finding aids or similar schemes. It is typically used for bibliographic purposes and for search and retrieval.
2. structural - Information that ties each object to others to make up logical units (e.g., information that relates individual images of pages from a book to the others that make up the book).
3. administrative - Information used to manage the object or control access to it. This may include information on how it was scanned, its storage format, copyright and licensing information, and information necessary for the long-term preservation of the digital objects.

Geospatial metadata

Metadata that describe geographic objects (such as datasets, maps, features, or simply documents with a geospatial component) have a history going back to at least 1994 (refer MIT Library page on FGDC Metadata). This class of metadata is described more fully on the Geospatial metadata page.

See also

• Agricultural Metadata Element Set
• APEv2 tag
• Common Warehouse Metamodel
• Data Dictionary
• Domain Specific Language (DSL)
• Domain-specific modelling (DSM)
• Dublin Core
• Exchangeable image file format (Exif)
• Folksonomy
• Generic Modeling Environment (GME)
• Geospatial metadata
• GNUBrain
• ID3
• IPTC (image meta-data)
• ISO/IEC 11179
• Kendra initiative
• Learning object metadata
• Magic number (programming)
• Material eXchange Format (MXF)
• Meta tag
• Metadata discovery
• Metadata registry
• Metadata facility for Java
• Meta-Object Facility (MOF)
• Meta:Page metadata
• Model Driven Engineering
• MDE
• Metadata publishing
• Meta noise
• Metatable
• Microcontent
• Microformats
• Model-based testing (MBT)
• Meta-modeling
• MPEG-7 ISO Standard for Multimedia Metadata
• PBCore
• REBOL
• Resource Description Framework (RDF)
• RIFE
• Semantic Web
• Tagged Image File Format (TIFF)
• Topic Maps
• Transformation
• Vocabulary-based transformation
• XML
• XML Metadata Interchange (XMI)
• Extensible Metadata Platform (XMP)

References

• Michael H. Brackett, Data Resource Quality, Addison-Wesley, 2000, ISBN 0-201-71306-3

External links

• "Managing Metadata" - A white paper by Randall Farrar on protection methods against metadata exposure.
• "Metacrap" - An opinion by Cory Doctorow on the limitations of metadata on the Internet
• A review of Mac OS X v. 10.4's new metadata implementations
• New Approaches Required for e-Discovery and Metadata
• Meta Meta Data Data - Article by Ralph Kimball
• Metadata article at LISWiki, a Library and information science wiki
• Marine Metadata Interoperability Project - A collaborative attempt to address marine science metadata needs
• Guidelines for adding IPTC Metadata to images (captions and keywords) - from ControlledVocabulary.com
• Guidance and techniques for tagging and keywording images - Article by Third Light Ltd
• "Enterprise Metadata SME" - Articles, Best Practices, and Publications on Enterprise Metadata
• VOD Metadata (Aka. ADI metadata) - North American Video On Demand (VOD) metadata standards home page (CableLabs).
• "Effective reporting of tacit (soft) information" - Article by Dr. Cyril Brookes
• Australian Government Recordkeeping Requirements
• "GNU libextractor" - software library and tool for extracting metadata from documents
• MDweb : Web tool for management and search of metadata
• Metadata Goes Mainstream - about online photo galleries and the lessons we can learn
• Extract IPTC Metadata using C# - Tips for extracting IPTC Metadata from jpeg and tiff image formats.

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