DocumentCode :
1796227
Title :
A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis
Author :
Cuzzocrea, Alfredo ; Diamantini, Claudia ; Genga, Laura ; Potena, Domenico ; Storti, Emanuele
Author_Institution :
ICAR, Univ. of Calabria, Rende, Italy
fYear :
2014
fDate :
11-14 Aug. 2014
Firstpage :
459
Lastpage :
464
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.
Keywords :
data mining; pattern recognition; semantic networks; collaboration pattern discovery; collaboration process log storage; common analysis model; composite methodology; data types; heterogeneous data storage; log generation; multidimensional analysis; process discovery approach; process schema discovery; process schema mining; semantic enrichment; semantic-based techniques; Buildings; Collaboration; Context; Data mining; Electronic mail; Semantics; Taxonomy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of
Conference_Location :
Tunis
Type :
conf
DOI :
10.1109/SOCPAR.2014.7008050
Filename :
7008050
Link To Document :
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