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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).
Web data integration (WDI) is the process of aggregating and managing data from different websites into a single, homogeneous workflow. This process includes data access, transformation, mapping, quality assurance and fusion of data. Data that is sourced and structured from websites is referred to as "web data".
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.
Information integration (II) is the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations.It is used in data mining and consolidation of data from unstructured or semi-structured resources.
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including: [1] Data transformation or data mediation between a data source and a destination
It may include access control information regarding its use by a specific federation user. The export schema helps in managing flow of control of data. Federated Schema is an integration of multiple export schemas. It includes information on data distribution that is generated when integrating export schemas. [3]
There is concern that political appointees have access to systems, including the Enterprise Human Resources Integration, without standard safeguard procedures designed to keep information private.
Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. [1] It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). [ 2 ]