Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining

Sander J.J. Leemans; Shiva Shabaninejad; Kanika Goel; Hassan Khosravi; Shazia Sadiq; Moe Thandar Wynn

doi:10.1007/978-3-030-62522-1_7

Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining

Sander J.J. Leemans^*
, Shiva Shabaninejad
, Kanika Goel
, Hassan Khosravi
, Shazia Sadiq
, Moe Thandar Wynn

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Process mining aims to obtain insights from event logs to improve business processes. In complex environments with large variances in process behaviour, analysing and making sense of such complex processes becomes challenging. Insights in such processes can be obtained by identifying sub-groups of traces (cohorts) and studying their differences. In this paper, we introduce a new framework that elicits features from trace attributes, measures the stochastic distance between cohorts defined by sets of these features, and presents this landscape of sets of features and their influence on process behaviour to users. Our framework differs from existing work in that it can take many aspects of behaviour into account, including the ordering of activities in traces (control flow), the relative frequency of traces (stochastic perspective), and cost. The framework has been instantiated and implemented, has been evaluated for feasibility on multiple publicly available real-life event logs, and evaluated on real-life case studies in two Australian universities.

Original language	English
Title of host publication	Conceptual Modeling - 39th International Conference, ER 2020, Proceedings
Editors	Gillian Dobbie, Ulrich Frank, Gerti Kappel, Stephen W. Liddle, Heinrich C. Mayr
Publisher	Springer
Pages	92-102
Number of pages	11
ISBN (Print)	9783030625214
DOIs	https://doi.org/10.1007/978-3-030-62522-1_7
Publication status	Published - 2020
Externally published	Yes
Event	39th International Conference on Conceptual Modeling, ER 2020 - Virtual, Vienna, Austria Duration: 3 Nov 2020 → 6 Nov 2020

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12400 LNCS
ISSN	0302-9743

Conference

Conference	39th International Conference on Conceptual Modeling, ER 2020
Abbreviated title	ER 2020
Country/Territory	Austria
City	Vienna
Period	3/11/20 → 6/11/20

Keywords

Drill-down recommendation
Filter recommendation
Process mining
Stochastic comparative process mining

Access to Document

10.1007/978-3-030-62522-1_7

Cite this

Leemans, S. J. J., Shabaninejad, S., Goel, K., Khosravi, H., Sadiq, S., & Wynn, M. T. (2020). Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining. In G. Dobbie, U. Frank, G. Kappel, S. W. Liddle, & H. C. Mayr (Eds.), Conceptual Modeling - 39th International Conference, ER 2020, Proceedings (pp. 92-102). Springer. https://doi.org/10.1007/978-3-030-62522-1_7

Leemans, Sander J.J. ; Shabaninejad, Shiva ; Goel, Kanika et al. / Identifying Cohorts : Recommending Drill-Downs Based on Differences in Behaviour for Process Mining. Conceptual Modeling - 39th International Conference, ER 2020, Proceedings. editor / Gillian Dobbie ; Ulrich Frank ; Gerti Kappel ; Stephen W. Liddle ; Heinrich C. Mayr. Springer, 2020. pp. 92-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12400 LNCS).

@inproceedings{5e2e4bb53d3c45298e8dcd1d14db5dc3,

title = "Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining",

abstract = "Process mining aims to obtain insights from event logs to improve business processes. In complex environments with large variances in process behaviour, analysing and making sense of such complex processes becomes challenging. Insights in such processes can be obtained by identifying sub-groups of traces (cohorts) and studying their differences. In this paper, we introduce a new framework that elicits features from trace attributes, measures the stochastic distance between cohorts defined by sets of these features, and presents this landscape of sets of features and their influence on process behaviour to users. Our framework differs from existing work in that it can take many aspects of behaviour into account, including the ordering of activities in traces (control flow), the relative frequency of traces (stochastic perspective), and cost. The framework has been instantiated and implemented, has been evaluated for feasibility on multiple publicly available real-life event logs, and evaluated on real-life case studies in two Australian universities.",

keywords = "Drill-down recommendation, Filter recommendation, Process mining, Stochastic comparative process mining",

author = "Leemans, \{Sander J.J.\} and Shiva Shabaninejad and Kanika Goel and Hassan Khosravi and Shazia Sadiq and Wynn, \{Moe Thandar\}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 39th International Conference on Conceptual Modeling, ER 2020, ER 2020 ; Conference date: 03-11-2020 Through 06-11-2020",

year = "2020",

doi = "10.1007/978-3-030-62522-1\_7",

language = "English",

isbn = "9783030625214",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "92--102",

editor = "Gillian Dobbie and Ulrich Frank and Gerti Kappel and Liddle, \{Stephen W.\} and Mayr, \{Heinrich C.\}",

booktitle = "Conceptual Modeling - 39th International Conference, ER 2020, Proceedings",

address = "United States",

}

Leemans, SJJ, Shabaninejad, S, Goel, K, Khosravi, H, Sadiq, S & Wynn, MT 2020, Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining. in G Dobbie, U Frank, G Kappel, SW Liddle & HC Mayr (eds), Conceptual Modeling - 39th International Conference, ER 2020, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12400 LNCS, pp. 92-102, 39th International Conference on Conceptual Modeling, ER 2020, Vienna, Austria, 3/11/20. https://doi.org/10.1007/978-3-030-62522-1_7

Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining. / Leemans, Sander J.J.; Shabaninejad, Shiva; Goel, Kanika et al.
Conceptual Modeling - 39th International Conference, ER 2020, Proceedings. ed. / Gillian Dobbie; Ulrich Frank; Gerti Kappel; Stephen W. Liddle; Heinrich C. Mayr. Springer, 2020. p. 92-102 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12400 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Identifying Cohorts

T2 - 39th International Conference on Conceptual Modeling, ER 2020

AU - Leemans, Sander J.J.

AU - Shabaninejad, Shiva

AU - Goel, Kanika

AU - Khosravi, Hassan

AU - Sadiq, Shazia

AU - Wynn, Moe Thandar

PY - 2020

Y1 - 2020

N2 - Process mining aims to obtain insights from event logs to improve business processes. In complex environments with large variances in process behaviour, analysing and making sense of such complex processes becomes challenging. Insights in such processes can be obtained by identifying sub-groups of traces (cohorts) and studying their differences. In this paper, we introduce a new framework that elicits features from trace attributes, measures the stochastic distance between cohorts defined by sets of these features, and presents this landscape of sets of features and their influence on process behaviour to users. Our framework differs from existing work in that it can take many aspects of behaviour into account, including the ordering of activities in traces (control flow), the relative frequency of traces (stochastic perspective), and cost. The framework has been instantiated and implemented, has been evaluated for feasibility on multiple publicly available real-life event logs, and evaluated on real-life case studies in two Australian universities.

AB - Process mining aims to obtain insights from event logs to improve business processes. In complex environments with large variances in process behaviour, analysing and making sense of such complex processes becomes challenging. Insights in such processes can be obtained by identifying sub-groups of traces (cohorts) and studying their differences. In this paper, we introduce a new framework that elicits features from trace attributes, measures the stochastic distance between cohorts defined by sets of these features, and presents this landscape of sets of features and their influence on process behaviour to users. Our framework differs from existing work in that it can take many aspects of behaviour into account, including the ordering of activities in traces (control flow), the relative frequency of traces (stochastic perspective), and cost. The framework has been instantiated and implemented, has been evaluated for feasibility on multiple publicly available real-life event logs, and evaluated on real-life case studies in two Australian universities.

KW - Drill-down recommendation

KW - Filter recommendation

KW - Process mining

KW - Stochastic comparative process mining

U2 - 10.1007/978-3-030-62522-1_7

DO - 10.1007/978-3-030-62522-1_7

M3 - Conference article in proceeding

AN - SCOPUS:85095726686

SN - 9783030625214

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 92

EP - 102

BT - Conceptual Modeling - 39th International Conference, ER 2020, Proceedings

A2 - Dobbie, Gillian

A2 - Frank, Ulrich

A2 - Kappel, Gerti

A2 - Liddle, Stephen W.

A2 - Mayr, Heinrich C.

PB - Springer

Y2 - 3 November 2020 through 6 November 2020

ER -

Leemans SJJ, Shabaninejad S, Goel K, Khosravi H, Sadiq S, Wynn MT. Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining. In Dobbie G, Frank U, Kappel G, Liddle SW, Mayr HC, editors, Conceptual Modeling - 39th International Conference, ER 2020, Proceedings. Springer. 2020. p. 92-102. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12400 LNCS). doi: 10.1007/978-3-030-62522-1_7

Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining

Abstract

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Keywords

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