A latitudinal study on the use of sequential and concurrency patterns in deviance mining

Laura Genga, Domenico Potena, Andrea Chiorrini, Claudia Diamantini, Nicola Zannone

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

Deviance mining is an emerging area in the field of Process Mining, with the aim of explaining the differences between normal and deviant process executions. Deviance mining approaches typically extract representative subprocesses characterizing normal/deviant behaviors from an event log and use these subprocesses as features for classification. Existing approaches mainly differ for the employed feature extraction technique and, in particular, for the representation of the patterns extracted, ranging from patterns consisting of sequence of activities to patterns explicitly representing concurrency. In this work, we perform a latitudinal study on the use of sequential and concurrency patterns in deviance mining. Comparisons between sequential and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
EditorsA. Appice, M. Ceci, C. Loglisci, G. Manco, E. Masciari, Z. Ras
Place of PublicationCham
PublisherSpringer
Pages103-119
Number of pages17
ISBN (Electronic)978-3-030-36617-9
ISBN (Print)978-3-030-36616-2
DOIs
Publication statusPublished - 1 Jan 2020

Publication series

NameStudies in Computational Intelligence
Volume880
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Fingerprint

Feature extraction
Experiments

Cite this

Genga, L., Potena, D., Chiorrini, A., Diamantini, C., & Zannone, N. (2020). A latitudinal study on the use of sequential and concurrency patterns in deviance mining. In A. Appice, M. Ceci, C. Loglisci, G. Manco, E. Masciari, & Z. Ras (Eds.), Studies in Computational Intelligence (pp. 103-119). (Studies in Computational Intelligence; Vol. 880). Cham: Springer. https://doi.org/10.1007/978-3-030-36617-9_7
Genga, Laura ; Potena, Domenico ; Chiorrini, Andrea ; Diamantini, Claudia ; Zannone, Nicola. / A latitudinal study on the use of sequential and concurrency patterns in deviance mining. Studies in Computational Intelligence. editor / A. Appice ; M. Ceci ; C. Loglisci ; G. Manco ; E. Masciari ; Z. Ras. Cham : Springer, 2020. pp. 103-119 (Studies in Computational Intelligence).
@inbook{787d5187755d4daa9495068e5a17321a,
title = "A latitudinal study on the use of sequential and concurrency patterns in deviance mining",
abstract = "Deviance mining is an emerging area in the field of Process Mining, with the aim of explaining the differences between normal and deviant process executions. Deviance mining approaches typically extract representative subprocesses characterizing normal/deviant behaviors from an event log and use these subprocesses as features for classification. Existing approaches mainly differ for the employed feature extraction technique and, in particular, for the representation of the patterns extracted, ranging from patterns consisting of sequence of activities to patterns explicitly representing concurrency. In this work, we perform a latitudinal study on the use of sequential and concurrency patterns in deviance mining. Comparisons between sequential and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.",
author = "Laura Genga and Domenico Potena and Andrea Chiorrini and Claudia Diamantini and Nicola Zannone",
year = "2020",
month = "1",
day = "1",
doi = "10.1007/978-3-030-36617-9_7",
language = "English",
isbn = "978-3-030-36616-2",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "103--119",
editor = "A. Appice and M. Ceci and C. Loglisci and G. Manco and E. Masciari and Z. Ras",
booktitle = "Studies in Computational Intelligence",
address = "Germany",

}

Genga, L, Potena, D, Chiorrini, A, Diamantini, C & Zannone, N 2020, A latitudinal study on the use of sequential and concurrency patterns in deviance mining. in A Appice, M Ceci, C Loglisci, G Manco, E Masciari & Z Ras (eds), Studies in Computational Intelligence. Studies in Computational Intelligence, vol. 880, Springer, Cham, pp. 103-119. https://doi.org/10.1007/978-3-030-36617-9_7

A latitudinal study on the use of sequential and concurrency patterns in deviance mining. / Genga, Laura; Potena, Domenico; Chiorrini, Andrea; Diamantini, Claudia; Zannone, Nicola.

Studies in Computational Intelligence. ed. / A. Appice; M. Ceci; C. Loglisci; G. Manco; E. Masciari; Z. Ras. Cham : Springer, 2020. p. 103-119 (Studies in Computational Intelligence; Vol. 880).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

TY - CHAP

T1 - A latitudinal study on the use of sequential and concurrency patterns in deviance mining

AU - Genga, Laura

AU - Potena, Domenico

AU - Chiorrini, Andrea

AU - Diamantini, Claudia

AU - Zannone, Nicola

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Deviance mining is an emerging area in the field of Process Mining, with the aim of explaining the differences between normal and deviant process executions. Deviance mining approaches typically extract representative subprocesses characterizing normal/deviant behaviors from an event log and use these subprocesses as features for classification. Existing approaches mainly differ for the employed feature extraction technique and, in particular, for the representation of the patterns extracted, ranging from patterns consisting of sequence of activities to patterns explicitly representing concurrency. In this work, we perform a latitudinal study on the use of sequential and concurrency patterns in deviance mining. Comparisons between sequential and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.

AB - Deviance mining is an emerging area in the field of Process Mining, with the aim of explaining the differences between normal and deviant process executions. Deviance mining approaches typically extract representative subprocesses characterizing normal/deviant behaviors from an event log and use these subprocesses as features for classification. Existing approaches mainly differ for the employed feature extraction technique and, in particular, for the representation of the patterns extracted, ranging from patterns consisting of sequence of activities to patterns explicitly representing concurrency. In this work, we perform a latitudinal study on the use of sequential and concurrency patterns in deviance mining. Comparisons between sequential and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.

UR - http://www.scopus.com/inward/record.url?scp=85078177505&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-36617-9_7

DO - 10.1007/978-3-030-36617-9_7

M3 - Chapter

AN - SCOPUS:85078177505

SN - 978-3-030-36616-2

T3 - Studies in Computational Intelligence

SP - 103

EP - 119

BT - Studies in Computational Intelligence

A2 - Appice, A.

A2 - Ceci, M.

A2 - Loglisci, C.

A2 - Manco, G.

A2 - Masciari, E.

A2 - Ras, Z.

PB - Springer

CY - Cham

ER -

Genga L, Potena D, Chiorrini A, Diamantini C, Zannone N. A latitudinal study on the use of sequential and concurrency patterns in deviance mining. In Appice A, Ceci M, Loglisci C, Manco G, Masciari E, Ras Z, editors, Studies in Computational Intelligence. Cham: Springer. 2020. p. 103-119. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-36617-9_7