TY - GEN
T1 - Probability Based Heuristic for Predictive Business Process Monitoring
AU - Böhmer, Kristof
AU - Rinderle-Ma, Stefanie
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Predictive business process monitoring concerns the unfolding of ongoing process instance executions. Recent work in this area frequently applies “blackbox” like methods which, despite delivering high quality prediction results, fail to implement a transparent and understandable prediction generation process, likely, limiting the trust users put into the results. This work tackles this limitation by basing prediction and the related prediction models on well known probability based histogram like approaches. Those enable to quickly grasp, and potentially visualise the prediction results, various alternative futures, and the overall prediction process. Furthermore, the proposed heuristic prediction approach outperforms state-of-the-art approaches with respect to prediction accuracy. This conclusion is drawn based on a publicly available prototypical implementation, real life logs from multiple sources and domains, along with a comparison with multiple alternative approaches.
AB - Predictive business process monitoring concerns the unfolding of ongoing process instance executions. Recent work in this area frequently applies “blackbox” like methods which, despite delivering high quality prediction results, fail to implement a transparent and understandable prediction generation process, likely, limiting the trust users put into the results. This work tackles this limitation by basing prediction and the related prediction models on well known probability based histogram like approaches. Those enable to quickly grasp, and potentially visualise the prediction results, various alternative futures, and the overall prediction process. Furthermore, the proposed heuristic prediction approach outperforms state-of-the-art approaches with respect to prediction accuracy. This conclusion is drawn based on a publicly available prototypical implementation, real life logs from multiple sources and domains, along with a comparison with multiple alternative approaches.
KW - Business process
KW - Predictive monitoring
KW - Probability
UR - http://www.scopus.com/inward/record.url?scp=85055793971&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-02610-3_5
DO - 10.1007/978-3-030-02610-3_5
M3 - Conference contribution
AN - SCOPUS:85055793971
SN - 9783030026097
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 96
BT - On the Move to Meaningful Internet Systems. OTM 2018 Conferences - Confederated International Conferences
A2 - Proper, Henderik A.
A2 - Meersman, Robert
A2 - Ardagna, Claudio Agostino
A2 - Panetto, Hervé
A2 - Debruyne, Christophe
A2 - Roman, Dumitru
PB - Springer Verlag
T2 - Confederated International Conferences: Cooperative Information Systems, CoopIS 2018, Ontologies, Databases, and Applications of Semantics, ODBASE 2018, and Cloud and Trusted Computing, C and TC, held as part of OTM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -