Leveraging Regression Algorithms for Predicting Process Performance Using Goal Alignments View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019-01-29

AUTHORS

Karthikeyan Ponnalagu , Aditya Ghose , Hoa Khanh Dam

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. More... »

PAGES

325-331

Book

TITLE

Business Process Management Workshops

ISBN

978-3-030-11640-8
978-3-030-11641-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-11641-5_26

DOI

http://dx.doi.org/10.1007/978-3-030-11641-5_26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111784365


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