Standard

APD tool : Mining Anomalous Patterns from Event Logs. / Genga, Laura; Alizadeh, Mahdi; Potena, Domenico; Diamantini, Claudia; Zannone, Nicola.

Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017. ed. / Robert Clarisó; Henrik Leopold ; Jan Mendling; Wil van der Aalst; Akhil Kumar; Brian Pentland; Mathias Weske. Aachen : CEUR, 2017. p. 1-5 (CEUR Workshop Proceedings; Vol. 1920).

Research output: Scientific - peer-reviewConference contribution

Harvard

Genga, L, Alizadeh, M, Potena, D, Diamantini, C & Zannone, N 2017, APD tool: Mining Anomalous Patterns from Event Logs. in R Clarisó, H Leopold , J Mendling, W van der Aalst, A Kumar, B Pentland & M Weske (eds), Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017. CEUR Workshop Proceedings, vol. 1920, CEUR, Aachen, pp. 1-5.

APA

Genga, L., Alizadeh, M., Potena, D., Diamantini, C., & Zannone, N. (2017). APD tool: Mining Anomalous Patterns from Event Logs. In R. Clarisó, H. Leopold , J. Mendling, W. van der Aalst, A. Kumar, B. Pentland, & M. Weske (Eds.), Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017 (pp. 1-5). (CEUR Workshop Proceedings; Vol. 1920). Aachen: CEUR.

Vancouver

Genga L, Alizadeh M, Potena D, Diamantini C, Zannone N. APD tool: Mining Anomalous Patterns from Event Logs. In Clarisó R, Leopold H, Mendling J, van der Aalst W, Kumar A, Pentland B, Weske M, editors, Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017. Aachen: CEUR. 2017. p. 1-5. (CEUR Workshop Proceedings).

Author

Genga, Laura ; Alizadeh, Mahdi ; Potena, Domenico ; Diamantini, Claudia ; Zannone, Nicola. / APD tool : Mining Anomalous Patterns from Event Logs. Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017. editor / Robert Clarisó ; Henrik Leopold ; Jan Mendling ; Wil van der Aalst ; Akhil Kumar ; Brian Pentland ; Mathias Weske. Aachen : CEUR, 2017. pp. 1-5 (CEUR Workshop Proceedings).

BibTeX

@inbook{fba76277b48648fa8e21a877ef99f8ff,
title = "APD tool: Mining Anomalous Patterns from Event Logs",
abstract = "A main challenge of today's organizations is the monitoring of their processes to check whether these processes comply with process models specifying the prescribed behavior. Deviations from the prescribed behavior can represent either legitimate work practices not described by the models, which highlight the need of improving it to better reflect the reality, or malicious behaviors representing, for instance, security breaches and frauds. In this paper, we present a tool designed to extract anomalous patterns representing recurrent deviations, together with their correlations, from historical logging data. The tool is targeted to researchers and practitioners in business process and security domains, with background in process mining.",
author = "Laura Genga and Mahdi Alizadeh and Domenico Potena and Claudia Diamantini and Nicola Zannone",
year = "2017",
series = "CEUR Workshop Proceedings",
publisher = "CEUR",
pages = "1--5",
editor = "Robert Clarisó and {Leopold }, Henrik and Jan Mendling and {van der Aalst}, Wil and Akhil Kumar and Brian Pentland and Mathias Weske",
booktitle = "Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017",

}

RIS

TY - CHAP

T1 - APD tool

T2 - Mining Anomalous Patterns from Event Logs

AU - Genga,Laura

AU - Alizadeh,Mahdi

AU - Potena,Domenico

AU - Diamantini,Claudia

AU - Zannone,Nicola

PY - 2017

Y1 - 2017

N2 - A main challenge of today's organizations is the monitoring of their processes to check whether these processes comply with process models specifying the prescribed behavior. Deviations from the prescribed behavior can represent either legitimate work practices not described by the models, which highlight the need of improving it to better reflect the reality, or malicious behaviors representing, for instance, security breaches and frauds. In this paper, we present a tool designed to extract anomalous patterns representing recurrent deviations, together with their correlations, from historical logging data. The tool is targeted to researchers and practitioners in business process and security domains, with background in process mining.

AB - A main challenge of today's organizations is the monitoring of their processes to check whether these processes comply with process models specifying the prescribed behavior. Deviations from the prescribed behavior can represent either legitimate work practices not described by the models, which highlight the need of improving it to better reflect the reality, or malicious behaviors representing, for instance, security breaches and frauds. In this paper, we present a tool designed to extract anomalous patterns representing recurrent deviations, together with their correlations, from historical logging data. The tool is targeted to researchers and practitioners in business process and security domains, with background in process mining.

UR - http://www.scopus.com/inward/record.url?scp=85030092436&partnerID=8YFLogxK

M3 - Conference contribution

T3 - CEUR Workshop Proceedings

SP - 1

EP - 5

BT - Proceedings of the Demo Track and Dissertation Award of the 15th International Conference on Business Process Modeling, BPM 2017

PB - CEUR

ER -

ID: 39996097