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  2. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    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]

  3. Dirty data - Wikipedia

    en.wikipedia.org/wiki/Dirty_data

    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data can contain such mistakes as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data, or even data that has been duplicated in the database.

  4. Misleading graph - Wikipedia

    en.wikipedia.org/wiki/Misleading_graph

    Log scales put the data values in terms of a chosen number (the base of the log) to a particular power. The base is often e (2.71828...) or 10. For example, log scales may give a height of 1 for a value of 10 in the data and a height of 6 for a value of 1,000,000 (10 6) in the data. Log scales and variants are commonly used, for instance, for ...

  5. Database repair - Wikipedia

    en.wikipedia.org/wiki/Database_repair

    The problem of database repair is a question about relational databases which has been studied in database theory, and which is a particular kind of data cleansing. The problem asks about how we can "repair" an input relational database in order to make it satisfy integrity constraints .

  6. Data preservation - Wikipedia

    en.wikipedia.org/wiki/Data_preservation

    Data preservation is the act of conserving and maintaining both the safety and integrity of data.Preservation is done through formal activities that are governed by policies, regulations and strategies directed towards protecting and prolonging the existence and authenticity of data and its metadata. [1]

  7. Data validation - Wikipedia

    en.wikipedia.org/wiki/Data_validation

    In this case, the source actor is asked to verify that this data is what they would really want to enter, in the light of a suggestion to the contrary. Here, the check step suggests an alternative (e.g., a check of a mailing address returns a different way of formatting that address or suggests a different address altogether).

  8. Spreadsheet - Wikipedia

    en.wikipedia.org/wiki/Spreadsheet

    Spreadsheet risk is the risk associated with deriving a materially incorrect value from a spreadsheet application that will be utilized in making a related (usually numerically based) decision. Examples include the valuation of an asset , the determination of financial accounts , the calculation of medicinal doses, or the size of a load-bearing ...

  9. Atomicity (database systems) - Wikipedia

    en.wikipedia.org/wiki/Atomicity_(database_systems)

    In database systems, atomicity (/ ˌ æ t ə ˈ m ɪ s ə t i /; from Ancient Greek: ἄτομος, romanized: átomos, lit. 'undividable') is one of the ACID (Atomicity, Consistency, Isolation, Durability) transaction properties. An atomic transaction is an indivisible and irreducible series of database operations such that either all occur ...