A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis.

Alfredo Cuzzocrea, Claudia Diamantini, Laura Genga, Domenico Potena, Emanuele Storti

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

3 Citations (Scopus)

Abstract

Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches turn to be poorly effective, due to the fact that: (i) logs of collaboration processes are usually stored in heterogeneous data storages which also expose different data types; (ii) it is not easy and direct to derive a common analysis model from such logs. As a consequence, classical methodologies usually fail. In order to fulfill this gap, in this paper we describe a composite methodology that combines semantics-based techniques and multidimensional analysis paradigms to support effective and efficient collaboration process discovery from log data.
Original languageEnglish
Title of host publication2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR)
PublisherIEEE Press
Pages459-464
ISBN (Electronic)978-1-4799-5934-1
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR)
- Ramada Plaza hotel, Tunis, Tunisia
Duration: 11 Aug 201414 Aug 2014
http://www.mirlabs.org/socpar14/

Conference

Conference 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR)
Country/TerritoryTunisia
CityTunis
Period11/08/1414/08/14
Internet address

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