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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 ...
Traditionally, data transformation has been a bulk or batch process, [6] whereby developers write code or implement transformation rules in a data integration tool, and then execute that code or those rules on large volumes of data. [7] This process can follow the linear set of steps as described in the data transformation process above.
The reciprocal transformation, some power transformations such as the Yeo–Johnson transformation, and certain other transformations such as applying the inverse hyperbolic sine, can be meaningfully applied to data that include both positive and negative values [10] (the power transformation is invertible over all real numbers if λ is an odd ...
The term "digital curation" was first used in the e-science and biological science fields as a means of differentiating the additional suite of activities ordinarily employed by library and museum curators to add value to their collections and enable its reuse [12] [13] [14] from the smaller subtask of simply preserving the data, a significantly more concise archival task. [12]
The range of data values or data quality in an operational system may exceed the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis can identify the data conditions that must be managed by transform rules specifications, leading to an amendment of validation ...
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data.
The import and export of data is the automated or semi-automated input and output of data sets between different software applications.It involves "translating" from the format used in one application into that used by another, where such translation is accomplished automatically via machine processes, such as transcoding, data transformation, and others.
Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. . The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data or produce summary data and statistics at different aggregation levels for various applications
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