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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [ 2 ] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to ...
Dirty data, also known as rogue data, [1] are inaccurate, incomplete or inconsistent data, especially in a computer system or database. [2]Dirty data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.
[21] [22] The need for data cleaning will arise from problems in the way that the datum are entered and stored. [21] Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. [23]
Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming it into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational ...
Data erasure (sometimes referred to as data clearing, data wiping, or data destruction) is a software-based method of data sanitization that aims to completely destroy all electronic data residing on a hard disk drive or other digital media by overwriting data onto all sectors of the device in an irreversible process. By overwriting the data on ...
As an example, ZFS creator Jeff Bonwick stated that the fast database at Greenplum, which is a database software company specializing in large-scale data warehousing and analytics, faces silent corruption every 15 minutes. [12]
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...