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A metadata engine collects, stores and analyzes information about data and metadata (data about data) in use within a knowledge domain. [1] It virtualizes the view of data for an application by separating the data (physical) path from the metadata (logical) path so that data management can be performed independently of, where the data physically resides.
A good example of metadata is the cataloging system found in libraries, which records for example the author, title, subject, and location on the shelf of a resource. Another is software system knowledge extraction of software objects such as data flows, control flows, call maps, architectures, business rules, business terms, and database schemas.
This type of "translating" from one format to another is often called "metadata mapping" or "field mapping," and is related to "data mapping", and "semantic mapping". Crosswalks also have several technical capabilities. They help databases using different metadata schemes to share information. They help metadata harvesters create union catalogs.
Descriptive metadata is typically used for discovery and identification, as information to search and locate an object, such as title, authors, subjects, keywords, and publisher. Structural metadata describes how the components of an object are organized. An example of structural metadata would be how pages are ordered to form chapters of a book.
Fig.1 STANDARD, OPEN and CLOSED CONCEPTS Fig.2 Example of STANDARD, OPEN and CLOSED CONCEPTS First of all, a concept is a simple version of a Unified Modeling Language (UML) class. The class definition [ 1 ] is adopted to define a concept, namely: a set of objects that share the same attributes, operations, relations, and semantics.
SPARQL (pronounced "sparkle", a recursive acronym [2] for SPARQL Protocol and RDF Query Language) is an RDF query language—that is, a semantic query language for databases—able to retrieve and manipulate data stored in Resource Description Framework (RDF) format.
API documentation can be enriched with metadata information like Java annotations. This metadata can be used by the compiler, tools, and by the run-time environment to implement custom behaviors or custom handling. [57] It is possible to generate API documentation in a data-driven manner.
A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. [1] A key concept of the system is the graph (or edge or relationship).