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Data analytics lifecycle automation

  • US 9,098,617 B1
  • Filed: 09/27/2012
  • Issued: 08/04/2015
  • Est. Priority Date: 09/27/2012
  • Status: Active Grant
First Claim
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1. A method comprising:

  • defining an initial data analytic plan for analyzing a given data set associated with a given data problem;

    conditioning at least a portion of original data in the given data set to generate conditioned data;

    selecting at least one model to analyze at least one of the original data and the conditioned data;

    executing the at least one selected model on at least one of a portion of the original data and a portion of the conditioned data to confirm an adequacy of the at least one selected model;

    communicating results of the model execution to at least one entity, the results comprising a refined data analytic plan for analyzing the given data set; and

    provisioning, via generating and deploying, one or more computing resources to implement the refined data analytic plan;

    wherein the defining, conditioning, selecting, executing, communicating and provisioning steps correspond to respective phases of a data analytics lifecycle;

    wherein the method further comprises;

    providing, to a user during multiple ones of the respective phases prior to the phase corresponding to the provisioning, an inventory of one or more computing resources to implement the at least one selected model; and

    changing, by the user during one or more of the respective phases prior to the phase corresponding to the provisioning, the at least one selected model based on the provided inventory;

    wherein the step of conditioning at least a portion of original data in the given data set to generate conditioned data comprises creating a separate analytics environment used to condition the portion of the original data, the separate analytics environment is created to have a capacity that is a selectable multiple of a capacity associated with the original data in the given data set, and one of one or more selectable multiples of the capacity are selected by the user between any two of the respective phases to dynamically provision and adjust the capacity of the separate analytics environment;

    wherein, in response to the user returning to at least one previous phase of the data analytics lifecycle and altering the previous phase, one or more subsequent phases of the data analytics lifecycle are automatically updated based on the user-altered previous phase; and

    wherein the defining, conditioning, selecting, executing, communicating, provisioning, providing, and changing steps are performed on one or more processing elements associated with a computing system and automate the data analytics lifecycle.

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