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Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another ...
Data should be consistent between different but related data records (e.g. the same individual might have different birthdates in different records or datasets). Where possible and economic, data should be verified against an authoritative source (e.g. business information is referenced against a D&B database to ensure accuracy). [3] [4]
Structured data is semantically well-defined data from a chosen target domain, interpreted with respect to category and context. Information extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display.
Data science is an interdisciplinary academic field [1] that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data.
Example of data collection in the biological sciences: Adélie penguins are identified and weighed each time they cross the automated weighbridge on their way to or from the sea. [ 1 ] Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to ...
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 user, rather than the database itself, typically initiates data curation and maintains metadata. [8] According to the University of Illinois' Graduate School of Library and Information Science, "Data curation is the active and on-going management of data through its lifecycle of interest and usefulness to scholarship, science, and education; curation activities enable data discovery and ...
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.