Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts

Yorick Spenrath, Marwan Hassani

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Bottleneck prediction is an important sub-task of process mining that aims at optimizing the discovered process models by avoiding such congestions. This paper discusses an ongoing work on incorporating recurrent concept drift in bottleneck prediction when applied to a real-world scenario. In the field of process mining, we develop a method of predicting whether and which bottlenecks will likely appear based on data known before a case starts. We next introduce GRAEC, a carefully-designed weighting mechanism to deal with concept drifts. The weighting decays over time and is extendable to adapt to seasonality in data. The methods are then applied to a simulation, and an invoicing process in the field of installation services in real-world settings. The results show an improvement to prediction accuracy compared to retraining a model on the most recent data.

LanguageEnglish
Title of host publicationProceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference
Subtitle of host publicationLisbon, Portugal, March 26, 2019
EditorsPaolo Papotti
PublisherCEUR-WS.org
Number of pages8
StatePublished - 1 Jan 2019
Event2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019 - Lisbon, Portugal
Duration: 26 Mar 201926 Mar 2019
http://ceur-ws.org/Vol-2322/

Publication series

NameCEUR Workshop Proceedings
Volume2322
ISSN (Print)1613-0073

Conference

Conference2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019
CountryPortugal
CityLisbon
Period26/03/1926/03/19
Internet address

Fingerprint

Industry

Keywords

  • Adaptation methods
  • Complex event processing
  • Data mining
  • Data streams
  • Knowledge discovery
  • Process mining
  • Recurrent concepts

Cite this

Spenrath, Y., & Hassani, M. (2019). Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. In P. Papotti (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019 (CEUR Workshop Proceedings; Vol. 2322). CEUR-WS.org.
Spenrath, Yorick ; Hassani, Marwan. / Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019. editor / Paolo Papotti. CEUR-WS.org, 2019. (CEUR Workshop Proceedings).
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title = "Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts",
abstract = "Bottleneck prediction is an important sub-task of process mining that aims at optimizing the discovered process models by avoiding such congestions. This paper discusses an ongoing work on incorporating recurrent concept drift in bottleneck prediction when applied to a real-world scenario. In the field of process mining, we develop a method of predicting whether and which bottlenecks will likely appear based on data known before a case starts. We next introduce GRAEC, a carefully-designed weighting mechanism to deal with concept drifts. The weighting decays over time and is extendable to adapt to seasonality in data. The methods are then applied to a simulation, and an invoicing process in the field of installation services in real-world settings. The results show an improvement to prediction accuracy compared to retraining a model on the most recent data.",
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Spenrath, Y & Hassani, M 2019, Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. in P Papotti (ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019. CEUR Workshop Proceedings, vol. 2322, CEUR-WS.org, 2019 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2019, Lisbon, Portugal, 26/03/19.

Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. / Spenrath, Yorick; Hassani, Marwan.

Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019. ed. / Paolo Papotti. CEUR-WS.org, 2019. (CEUR Workshop Proceedings; Vol. 2322).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - Bottleneck prediction is an important sub-task of process mining that aims at optimizing the discovered process models by avoiding such congestions. This paper discusses an ongoing work on incorporating recurrent concept drift in bottleneck prediction when applied to a real-world scenario. In the field of process mining, we develop a method of predicting whether and which bottlenecks will likely appear based on data known before a case starts. We next introduce GRAEC, a carefully-designed weighting mechanism to deal with concept drifts. The weighting decays over time and is extendable to adapt to seasonality in data. The methods are then applied to a simulation, and an invoicing process in the field of installation services in real-world settings. The results show an improvement to prediction accuracy compared to retraining a model on the most recent data.

AB - Bottleneck prediction is an important sub-task of process mining that aims at optimizing the discovered process models by avoiding such congestions. This paper discusses an ongoing work on incorporating recurrent concept drift in bottleneck prediction when applied to a real-world scenario. In the field of process mining, we develop a method of predicting whether and which bottlenecks will likely appear based on data known before a case starts. We next introduce GRAEC, a carefully-designed weighting mechanism to deal with concept drifts. The weighting decays over time and is extendable to adapt to seasonality in data. The methods are then applied to a simulation, and an invoicing process in the field of installation services in real-world settings. The results show an improvement to prediction accuracy compared to retraining a model on the most recent data.

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Spenrath Y, Hassani M. Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. In Papotti P, editor, Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019. CEUR-WS.org. 2019. (CEUR Workshop Proceedings).