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  2. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    The term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. [22] [23] Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

  3. Limits of computation - Wikipedia

    en.wikipedia.org/wiki/Limits_of_computation

    Several methods have been proposed for producing computing devices or data storage devices that approach physical and practical limits: A cold degenerate star could conceivably be used as a giant data storage device, by carefully perturbing it to various excited states, in the same manner as an atom or quantum well used for these purposes. Such ...

  4. Critical data studies - Wikipedia

    en.wikipedia.org/wiki/Critical_data_studies

    First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. [1] Big data is important to critical data studies because it is the type of data used within this field.

  5. Data-intensive computing - Wikipedia

    en.wikipedia.org/wiki/Data-intensive_computing

    Data-intensive computing is intended to address this need. Parallel processing approaches can be generally classified as either compute-intensive, or data-intensive. [6] [7] [8] Compute-intensive is used to describe application programs that are compute-bound. Such applications devote most of their execution time to computational requirements ...

  6. Data-centric computing - Wikipedia

    en.wikipedia.org/wiki/Data-centric_computing

    Organizations are struggling to cope with exponential data growth while seeking better approaches to extracting insights from that data using services including Big Data analytics and machine learning. However, existing architectures aren't built to address service requirements at petabyte scale and beyond without significant performance limits ...

  7. Online analytical processing - Wikipedia

    en.wikipedia.org/wiki/Online_analytical_processing

    For example, for some vendors, a HOLAP database will use relational tables to hold the larger quantities of detailed data and use specialized storage for at least some aspects of the smaller quantities of more-aggregate or less-detailed data. HOLAP addresses the shortcomings of MOLAP and ROLAP by combining the capabilities of both approaches ...

  8. Very large database - Wikipedia

    en.wikipedia.org/wiki/Very_large_database

    VLDB is not the same as big data, but the storage aspect of big data may involve a VLDB database. [2] That said some of the storage solutions supporting big data were designed from the start to support large volumes of data, so database administrators may not encounter VLDB issues that older versions of traditional RDBMS's might encounter. [29]

  9. CAP theorem - Wikipedia

    en.wikipedia.org/wiki/CAP_theorem

    In database theory, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that any distributed data store can provide only two of the following three guarantees: [1] [2] [3]