TY - GEN
T1 - LoGo
T2 - 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020
AU - Böhmer, Kristof
AU - Rinderle-Ma, Stefanie
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Predicting process behavior in terms of the next activity to be executed and/or its timestamp can be crucial, e.g., to avoid impeding compliance violations or performance problems. Basically, two prediction techniques are conceivable, i.e., global and local techniques. Global techniques consider all process behavior at once, but might suffer from noise. Local techniques consider a certain subset of the behavior, but might loose the “big picture”. A combination of both techniques is promising to balance out each others drawbacks, but exists so far only in an implicit and unsystematic way. We propose LoGo as a systematic combined approach based on a novel global technique and an extended local one. LoGo is evaluated based on real life execution logs from multiple domains, outperforming nine comparison approaches. Overall, LoGo results in explainable prediction models and high prediction quality.
AB - Predicting process behavior in terms of the next activity to be executed and/or its timestamp can be crucial, e.g., to avoid impeding compliance violations or performance problems. Basically, two prediction techniques are conceivable, i.e., global and local techniques. Global techniques consider all process behavior at once, but might suffer from noise. Local techniques consider a certain subset of the behavior, but might loose the “big picture”. A combination of both techniques is promising to balance out each others drawbacks, but exists so far only in an implicit and unsystematic way. We propose LoGo as a systematic combined approach based on a novel global technique and an extended local one. LoGo is evaluated based on real life execution logs from multiple domains, outperforming nine comparison approaches. Overall, LoGo results in explainable prediction models and high prediction quality.
KW - Explainable prediction models
KW - Global prediction
KW - Local prediction
KW - Predictive process monitoring
KW - Sequential rule mining
UR - http://www.scopus.com/inward/record.url?scp=85086227393&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49435-3_18
DO - 10.1007/978-3-030-49435-3_18
M3 - Conference contribution
AN - SCOPUS:85086227393
SN - 9783030494346
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 283
EP - 298
BT - Advanced Information Systems Engineering - 32nd International Conference, CAiSE 2020, Proceedings
A2 - Dustdar, Schahram
A2 - Yu, Eric
A2 - Pant, Vik
A2 - Salinesi, Camille
A2 - Rieu, Dominique
PB - Springer
Y2 - 8 June 2020 through 12 June 2020
ER -