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In-database processing, sometimes referred to as in-database analytics, refers to the integration of data analytics into data warehousing functionality. Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods.
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).
Data sharing may also be restricted to protect institutions and scientists from use of data for political purposes. Data and methods may be requested from an author years after publication. In order to encourage data sharing [3] and prevent the loss or corruption of data, a number of funding agencies and journals established policies on data ...
Electronic data interchange is a successful implementation of commercial data exchanges that began in the late 1970s and remains in use today. [5] Some controversy comes when discussing regulations regarding information exchange. [6] Initiatives to standardize information sharing protocols include extensible markup language , simple object ...
System heterogeneity: use of different operating system, hardware platforms lead to system heterogeneity; Ontologies, as formal models of representation with explicitly defined concepts and named relationships linking them, are used to address the issue of semantic heterogeneity in data sources.
An OLTP system is an accessible data processing system in today's enterprises. Some examples of OLTP systems include order entry, retail sales, and financial transaction systems. [5] Online transaction processing systems increasingly require support for transactions that span a network and may include more than one company.
The following is provided as an overview of and topical guide to databases: Database – organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies).
Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources.The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing.