Standard

APD tool : Mining anomalous patterns from event logs. / Genga, Laura; Alizadeh, Mahdi; Potena, Domenico; Diamantini, Claudia; Zannone, Nicola.

In: Ceur Workshop Proceedings, Vol. 1920, 2017.

Research output: Scientific - peer-reviewArticle

Harvard

Genga, L, Alizadeh, M, Potena, D, Diamantini, C & Zannone, N 2017, 'APD tool: Mining anomalous patterns from event logs' Ceur Workshop Proceedings, vol 1920.

APA

Genga, L., Alizadeh, M., Potena, D., Diamantini, C., & Zannone, N. (2017). APD tool: Mining anomalous patterns from event logs. Ceur Workshop Proceedings, 1920.

Vancouver

Genga L, Alizadeh M, Potena D, Diamantini C, Zannone N. APD tool: Mining anomalous patterns from event logs. Ceur Workshop Proceedings. 2017;1920.

Author

Genga, Laura; Alizadeh, Mahdi; Potena, Domenico; Diamantini, Claudia; Zannone, Nicola / APD tool : Mining anomalous patterns from event logs.

In: Ceur Workshop Proceedings, Vol. 1920, 2017.

Research output: Scientific - peer-reviewArticle

BibTeX

@article{fba76277b48648fa8e21a877ef99f8ff,
title = "APD tool: Mining anomalous patterns from event logs",
author = "Laura Genga and Mahdi Alizadeh and Domenico Potena and Claudia Diamantini and Nicola Zannone",
year = "2017",
volume = "1920",
journal = "Ceur Workshop Proceedings",
issn = "1613-0073",

}

RIS

TY - JOUR

T1 - APD tool

T2 - Ceur Workshop Proceedings

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 - Article

VL - 1920

JO - Ceur Workshop Proceedings

JF - Ceur Workshop Proceedings

SN - 1613-0073

ER -

ID: 32864675