DOI

  • Laura Genga
  • Domenico Potena
  • Orazio Martino
  • Mahdi Alizadeh
  • Claudia Diamantini
  • Nicola Zannone

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
Place of PublicationCham
PublisherSpringer Verlag
Pages181-197
Number of pages17
Volume10312 LNCS
ISBN (Electronic)978-3-319-61461-8
ISBN (Print)978-3-319-61460-1
DOIs
StatePublished - 2017
Externally publishedYes
Event5th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2016 was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016 - Riva del Garda, Italy
Duration: 19 Sep 201619 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10312 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference5th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2016 was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016
CountryItaly
CityRiva del Garda
Period19/09/1619/09/16

ID: 32863885