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Data integration refers to the process of combining, sharing, or synchronizing data from multiple sources to provide users with a unified view. [1] There are a wide range of possible applications for data integration, from commercial (such as when a business merges multiple databases) to scientific (combining research data from different bioinformatics repositories).
The W3C introduced R2RML as a standard for mapping data in a relational database to data expressed in terms of the Resource Description Framework (RDF). In the future, tools based on semantic web languages such as RDF, the Web Ontology Language (OWL) and standardized metadata registry will make data mapping a more automatic process.
Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software .
[11] Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence ...
Data integration allows companies to access their enterprise data and functions, fragmented across disparate systems, in order to create a combined, accurate, and consistent view of their core information as well as process assets and leverage them across the enterprise to drive business decisions and operations.
Enterprise information integration (EII) is the ability to support a unified view of data and information for an entire organization.In a data virtualization application of EII, a process of information integration, using data abstraction to provide a unified interface (known as uniform data access) for viewing all the data within an organization, and a single set of structures and naming ...
In many circumstances, these sources use inconsistent terms and definitions to describe the data content itself – making it hard to compare data, hard to automate business processes, hard to feed complex applications and hard to exchange data. This frequently results in a difficult process of data mapping and cross-referencing.
Specifically, ontologies play the following roles: Content Explication [1] The ontology enables accurate interpretation of data from multiple sources through the explicit definition of terms and relationships in the ontology. Query Model [1] In some systems like SIMS, [6] the query is formulated using the ontology as a global query schema ...