Machine Learning-based Model For Identifying Executions Of A Business Process

  • Published: Apr 20, 2017
  • Earliest Priority: Dec 30 2012
  • Family: 1
  • Cited Works: 0
  • Cited by: 0
  • Cites: 0
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Abstract

Described herein are systems, methods, and computer programs that may be utilized to identify a sequence corresponding to an execution of a Business Processes (BP) using a machine learning-based model of the BP generated based on sequences corresponding to previous executions of the BP by a plurality of organizations. In one embodiment, a sequence parser module receives one or more streams of steps performed during interactions with an instance of a software system, which belongs to a certain organization, and selects, from among the one or more streams, candidate sequences of steps. A feature generator module generate, for each sequence from among the candidate sequences, a plurality of feature values. And a predictor module utilizes the model to calculate, based on an input comprising the plurality of feature values generated for the sequence, a value indicative of whether the sequence corresponds to an execution of the BP.


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  • Publication: Apr 20, 2017
  • Application: Dec 28, 2016
    US US 201615391876 A
  • Priority: Dec 28, 2016
    US US 201615391876 A
  • Priority: Aug 11, 2016
    US US 201662373479 P
  • Priority: Mar 11, 2016
    US US 201615067225 A
  • Priority: Dec 27, 2013
    US US 201314141514 A
  • Priority: Dec 22, 2013
    US US 201361919773 P
  • Priority: Apr 21, 2013
    US US 201361814305 P
  • Priority: Dec 30, 2012
    US US 201261747313 P
  • Priority: May 8, 2011
    US US 201113103078 A

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