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Divide-and-conquer strategies for process mining
dc.contributor.author | Carmona Vargas, Josep |
dc.contributor.author | Cortadella, Jordi |
dc.contributor.author | Kishinevsky, Mike |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2016-06-02T07:43:25Z |
dc.date.available | 2016-06-02T07:43:25Z |
dc.date.issued | 2009-03 |
dc.identifier.citation | Carmona, J., Cortadella, J., Kishinevsky, M. "Divide-and-conquer strategies for process mining". 2009. |
dc.identifier.uri | http://hdl.handle.net/2117/87625 |
dc.description | A previous version of this report was titled "A recursive approach for Process Mining" |
dc.description.abstract | The main goal of Process Mining is to extract process models from logs of a system. Among the possible models to represent a process, Petri nets is an ideal candidate due to its graphical representation, clear semantics and expressive p ower. The theory of regions can be used to transform a log into a Petri net, but unfortunately the transformation requires algorithms with high complexity. This paper provides techniques to overcome this limitation. Either by using decomposition techniques, or by clustering events in the log and working on projections, the proposed techniques can be used to alleviat e the complexity and make the theory of regions practical for real-life problems. |
dc.format.extent | 20 p. |
dc.language.iso | eng |
dc.relation.ispartofseries | LSI-08-35-R |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.other | Regions |
dc.subject.other | Petri nets |
dc.subject.other | Synthesis |
dc.subject.other | Process mining |
dc.title | Divide-and-conquer strategies for process mining |
dc.type | External research report |
dc.contributor.group | Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals |
dc.rights.access | Open Access |
local.identifier.drac | 18545499 |
dc.description.version | Postprint (published version) |
local.citation.author | Carmona, J.; Cortadella, J.; Kishinevsky, M. |
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