Bulk sets for executing database queries

    公开(公告)号:US10437839B2

    公开(公告)日:2019-10-08

    申请号:US15140893

    申请日:2016-04-28

    Abstract: A computer-implemented method includes determining a plurality of bulk sets for querying database records. The method also includes assigning a plurality of keysets to the plurality of bulk sets, with each keyset comprising a unique set of dimension attribute values from the database records. The method also includes calculating a predicted load score of each bulk set. The method also includes performing a transfer of a keyset from a first bulk set to a second bulk set when the transfer reduces a difference between predicted load scores of the first bulk set and the second bulk set. The method also includes, after the transfer, executing bulk queries using the plurality of bulk sets.

    Bulk Sets for Executing Database Queries
    4.
    发明申请

    公开(公告)号:US20170316003A1

    公开(公告)日:2017-11-02

    申请号:US15140893

    申请日:2016-04-28

    CPC classification number: G06F16/24578 G06F16/2453 G06F16/2455

    Abstract: A computer-implemented method includes determining a plurality of bulk sets for querying database records. The method also includes assigning a plurality of keysets to the plurality of bulk sets, with each keyset comprising a unique set of dimension attribute values from the database records. The method also includes calculating a predicted load score of each bulk set. The method also includes performing a transfer of a keyset from a first bulk set to a second bulk set when the transfer reduces a difference between predicted load scores of the first bulk set and the second bulk set. The method also includes, after the transfer, executing bulk queries using the plurality of bulk sets.

    Calculating normalized metrics
    5.
    发明授权

    公开(公告)号:US10152302B2

    公开(公告)日:2018-12-11

    申请号:US15404599

    申请日:2017-01-12

    Abstract: Examples relate to calculating normalize metrics. The examples disclosed herein calculate respective normalized first metric values for each of a plurality of first metric values that are on a time scale and respective normalized second metric values for each of the plurality of raw second metric values that are on the time scale, where the plurality of first metric values are associated with a first metric, and the plurality of second metric values are associated with a second metric. An extremum of the normalized first metric value and the normalized second metric value at each time of the time scale is averaged to calculate a plurality of extremum baseline values. Examples herein calculate a plurality of sleeve values of the plurality of extremum baseline values based on a standard deviation of the plurality of extremum baseline values.

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