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Mining goal refinement patterns: Distilling know-how from data

Conference Paper


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.

UOW Authors


  •   Santiputri, Metta (external author)
  •   Deb, Novarun (external author)
  •   Khan, Muhammad (external author)
  •   Ghose, Aditya
  •   Dam, Hoa
  •   Chaki, Nabendu (external author)

Publication Date


  • 2017

Citation


  • Santiputri, M., Deb, N., Khan, M., Ghose, A., Dam, H. & Chaki, N. (2017). Mining goal refinement patterns: Distilling know-how from data. Lecture Notes in Computer Science (pp. 69-76). Switzerland: Springer.

Scopus Eid


  • 2-s2.0-85033474074

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/947

Start Page


  • 69

End Page


  • 76

Place Of Publication


  • Switzerland

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.

UOW Authors


  •   Santiputri, Metta (external author)
  •   Deb, Novarun (external author)
  •   Khan, Muhammad (external author)
  •   Ghose, Aditya
  •   Dam, Hoa
  •   Chaki, Nabendu (external author)

Publication Date


  • 2017

Citation


  • Santiputri, M., Deb, N., Khan, M., Ghose, A., Dam, H. & Chaki, N. (2017). Mining goal refinement patterns: Distilling know-how from data. Lecture Notes in Computer Science (pp. 69-76). Switzerland: Springer.

Scopus Eid


  • 2-s2.0-85033474074

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/947

Start Page


  • 69

End Page


  • 76

Place Of Publication


  • Switzerland