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  2. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [ 2 ] and transforming it into one cohesive data set; a simple example is the ...

  3. Chartjunk - Wikipedia

    en.wikipedia.org/wiki/Chartjunk

    An example of a chart containing gratuitous chartjunk. This chart uses a large area and much "ink" (many symbols and lines) to show only five hard-to-read numbers, 1, 2, 4, 8, and 16. Chartjunk consists of all visual elements in charts and graphs that are not necessary to comprehend the information represented on the graph, or that distract the ...

  4. Isolation (database systems) - Wikipedia

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

    Isolation is typically enforced at the database level. However, various client-side systems can also be used. It can be controlled in application frameworks or runtime containers such as J2EE Entity Beans [2] On older systems, it may be implemented systemically (by the application developers), for example through the use of temporary tables.

  5. Create, read, update and delete - Wikipedia

    en.wikipedia.org/.../Create,_read,_update_and_delete

    For example, in address book software, the basic storage unit is an individual contact entry. As a bare minimum, the software must allow the user to: [6] Create, or add new entries; Read, retrieve, search, or view existing entries; Update, or edit existing entries; Delete, deactivate, or remove existing entries

  6. Reverse-delete algorithm - Wikipedia

    en.wikipedia.org/wiki/Reverse-delete_algorithm

    The reverse-delete algorithm is an algorithm in graph theory used to obtain a minimum spanning tree from a given connected, edge-weighted graph. It first appeared in Kruskal (1956) , but it should not be confused with Kruskal's algorithm which appears in the same paper.

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  8. 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.

  9. AOL

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    The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web.