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Leveraging Regression Algorithms for Predicting Process Performance Using Goal Alignments

Journal Article


Abstract


  • Industry-scale context-aware processes typically manifest a large number of variants during their execution. Being able to predict the performance of a partially executed process instance (in terms of cost, time or customer satisfaction) can be particularly useful. Such predictions can help in permitting interventions to improve matters for instances that appear likely to perform poorly. This paper proposes an approach for leveraging the process context, process state, and process goals to obtain such predictions.

Publication Date


  • 2019

Citation


  • Ponnalagu, K., Ghose, A. & Dam, H. Khanh. (2019). Leveraging Regression Algorithms for Predicting Process Performance Using Goal Alignments. Lecture Notes in Business Information Processing, 342 325-331. Business Process Management Workshops BPM 2018 International Workshops Sydney, NSW, Australia, September 9–14, 2018 Revised Papers

Scopus Eid


  • 2-s2.0-85061382620

Number Of Pages


  • 6

Start Page


  • 325

End Page


  • 331

Volume


  • 342

Place Of Publication


  • Germany

Abstract


  • Industry-scale context-aware processes typically manifest a large number of variants during their execution. Being able to predict the performance of a partially executed process instance (in terms of cost, time or customer satisfaction) can be particularly useful. Such predictions can help in permitting interventions to improve matters for instances that appear likely to perform poorly. This paper proposes an approach for leveraging the process context, process state, and process goals to obtain such predictions.

Publication Date


  • 2019

Citation


  • Ponnalagu, K., Ghose, A. & Dam, H. Khanh. (2019). Leveraging Regression Algorithms for Predicting Process Performance Using Goal Alignments. Lecture Notes in Business Information Processing, 342 325-331. Business Process Management Workshops BPM 2018 International Workshops Sydney, NSW, Australia, September 9–14, 2018 Revised Papers

Scopus Eid


  • 2-s2.0-85061382620

Number Of Pages


  • 6

Start Page


  • 325

End Page


  • 331

Volume


  • 342

Place Of Publication


  • Germany