Subgraph mining for anomalous pattern discovery in event logs

L. Genga, D. Potena, O. Martino, M. Alizadeh, C. Diamantini, Nicola Zannone

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

5 Citations (Scopus)
3 Downloads (Pure)

Abstract

Conformance checking allows organizations to verify whether their IT system complies with the prescribed behavior by comparing process executions recorded by the IT system against a process model (representing the normative behavior). However, most of the existing techniques are only able to identify low-level deviations, which provide a scarce support to investigate what actually happened when a process execution deviates from the specification. In this work, we introduce an approach to extract recurrent deviations from historical logging data and generate anomalous patterns representing high-level deviations. These patterns provide analysts with a valuable aid for investigating nonconforming behaviors; moreover, they can be exploited to detect high-level deviations during conformance checking. To identify anomalous behaviors from historical logging data, we apply frequent subgraph mining techniques together with an ad-hoc conformance checking technique. Anomalous patterns are then derived by applying frequent items algorithms to determine highly-correlated deviations, among which ordering relations are inferred. The approach has been validated by means of a set of experiments.

Original languageEnglish
Title of host publicationNew Frontiers in Mining Complex Patterns - 5th International Workshop, NFMCP 2016 Held in Conjunction with ECML-PKDD 2016, Revised Selected Papers
PublisherSpringer
Pages181-197
Number of pages17
ISBN (Print)9783319614601
DOIs
Publication statusPublished - 2017
Event5th International Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2016) - Riva del Garda, Italy
Duration: 19 Sept 201619 Sept 2016
Conference number: 5
http://www.di.uniba.it/~loglisci/NFmcp2016/

Publication series

NameLecture Notes in Computer Science
Volume10312
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

Workshop5th International Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2016)
Abbreviated titleNFMCP 2016
Country/TerritoryItaly
CityRiva del Garda
Period19/09/1619/09/16
OtherHeld in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016
Internet address

Fingerprint

Dive into the research topics of 'Subgraph mining for anomalous pattern discovery in event logs'. Together they form a unique fingerprint.

Cite this