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Mining Goal Refinement Patterns: Distilling Know-How from Data

  • Metta Santiputri
  • Novarun Deb
  • Muhammad Asjad Khan
  • Aditya Ghose
  • Hoa Dam
  • Nabendu Chaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10650)

Abstract

Goal models play an important role by providing a hierarchic representation of stakeholder intent, and by providing a representation of lower-level subgoals that must be achieved to enable the achievement of higher-level goals. A goal model can be viewed as a composition of a number of goal refinement patterns that relate parent goals to subgoals. In this paper, we offer a means for mining these patterns from enterprise event logs and a technique to leverage vector representations of words and phrases to compose these patterns to obtain complete goal models. The resulting machinery can be quiote powerful in its ability to mine know-how or constitutive norms. We offer an empirical evaluation using both real-life and synthetic datasets.

Keywords

Goal model mining Goal refinement Know-how 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Metta Santiputri
    • 1
  • Novarun Deb
    • 2
  • Muhammad Asjad Khan
    • 3
  • Aditya Ghose
    • 3
  • Hoa Dam
    • 3
  • Nabendu Chaki
    • 2
  1. 1.Department of InformaticsState Polytechnic of BatamBatamIndonesia
  2. 2.Department of Computer Science and EngineeringUniversity of CalcuttaKolkataIndia
  3. 3.Decision Systems Lab, School of Computing and ITUniversity of WollongongWollongongAustralia

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