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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".
Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California.It provides applications focused on sales, customer service, marketing automation, e-commerce, analytics, artificial intelligence, and application development.
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 ]
Many online communities for A.I. developers exist where tutorials, examples, and forums aim at helping both beginners and experts build intelligent systems. However, few communities have succeeded in making a certain standard, or a code of conduct popular to allow the large collection of miscellaneous systems to be integrated with ease.
A heterogeneous database system is an automated (or semi-automated) system for the integration of heterogeneous, disparate database management systems to present a user with a single, unified query interface. Heterogeneous database systems (HDBs) are computational models and software implementations that provide heterogeneous database integration.
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