http://www.cnr.it/ontology/cnr/individuo/prodotto/ID133650
Discovering Multi-perspective Process Models: The Case of Loosely-Structured Processes (Contributo in volume (capitolo o saggio))
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- Label
- Discovering Multi-perspective Process Models: The Case of Loosely-Structured Processes (Contributo in volume (capitolo o saggio)) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/978-3-642-00670-8_10 (literal)
- Alternative label
Folino Francesco Paolo, Greco Gianluigi, Guzzo Antonella, Pontieri Luigi (2009)
Discovering Multi-perspective Process Models: The Case of Loosely-Structured Processes
Springer-Verlag, Berlin (Germania) in ENTERPRISE INFORMATION SYSTEMS-B (Enterprise Information Systems, 10th International Conference, ICEIS 2008, Barcelona, Spain, June 12-16, 2008, Revised Selected Papers), 2009
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- Folino Francesco Paolo, Greco Gianluigi, Guzzo Antonella, Pontieri Luigi (literal)
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- Berlin Heidelberg (literal)
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- http://www.springerlink.com/content/r0373628157k1582/fulltext.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- ENTERPRISE INFORMATION SYSTEMS-B (Enterprise Information Systems, 10th International Conference, ICEIS 2008, Barcelona, Spain, June 12-16, 2008, Revised Selected Papers) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
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- Istituto di calcolo e reti ad alte prestazioni;
Dip. Mat - Università della Calabria;
Dip. DEIS - Università della Calabria;
Istituto di calcolo e reti ad alte prestazioni (literal)
- Titolo
- Discovering Multi-perspective Process Models: The Case of Loosely-Structured Processes (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
- Enterprise Information Systems, 10th Intl Conference, ICEIS 2008, Revised Selected Papers (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-642-00669-2 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Filipe, J and Cordeiro, J (literal)
- Abstract
- Process Mining techniques exploit the information stored in the execution log of a process to extract some high-level process model, useful for
analysis or design tasks. Most of these techniques focus on \"structural\" aspects
of the process, in that they only consider what elementary activities were executed and in which ordering. Hence, any other \"non-structural\" data, usually
kept in real log systems (e.g., activity executors, parameter values), are disregarded, yet being a potential source of knowledge. In this paper, we overcome
this limitation by proposing a novel approach to the discovery of process models,
where the behavior of a process is characterized from both structural and nonstructural viewpoints. Basically, we recognize different executions' classes via a
structural clustering approach, and model them with a collection of specific work-
flows. Relevant correlations between these classes and non-structural properties
are captured by a rule-based classification model, which can be used for both
explanation and prediction. In order to empower the versatility of our approach,
we also combine it with a pre-processing method, which allows to restructure the
log events according to different analysis perspectives, and to study them at the
right abstraction level. Interestingly, such an approach reduces the risk of obtaining knotty, \"spaghetti-like\", process models when analyzing the logs of looselystructured processes consisting of low-level operations that are performed in a
more autonomous way than in traditional BPM platforms. Preliminary results on
real-life application scenario confirm the validity of the approach (literal)
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