%0 Journal Article %1 Tsai:2010:TPM:1851992.1851996 %A Tsai, Chieh-Yuan %A Jen, Henyi %A Chen, Yi-Ching %C Hingham, MA, USA %D 2010 %I Kluwer Academic Publishers %J Applied Intelligence %K discovery mining process %N 1 %P 54--66 %R 10.1007/s10489-010-0240-5 %T Time-interval Process Model Discovery and Validation--a Genetic Process Mining Approach %U http://dx.doi.org/10.1007/s10489-010-0240-5 %V 33 %X A process management technique, called process mining, received much attention recently. Process mining can extract organizational or social structures from event logs recorded in an information system. However, when constructing process models, most process mining searches consider only the topology information among events, but do not include the time information. To overcome the drawbacks, a time-interval genetic process mining framework is proposed. First, time-intervals between events are derived for all event sequences. A discretization procedure is then developed to transform time-interval data from continues type to categorical type. Second, the genetic process mining method which is based on global search strategy is applied to generate time-interval process models. Finally, a precision measure is defined to evaluate the quality of the generated models. With the measure, managers can select the best process model among a set of candidate models without human involvement.

@article{Tsai:2010:TPM:1851992.1851996, abstract = {A process management technique, called process mining, received much attention recently. Process mining can extract organizational or social structures from event logs recorded in an information system. However, when constructing process models, most process mining searches consider only the topology information among events, but do not include the time information. To overcome the drawbacks, a time-interval genetic process mining framework is proposed. First, time-intervals between events are derived for all event sequences. A discretization procedure is then developed to transform time-interval data from continues type to categorical type. Second, the genetic process mining method which is based on global search strategy is applied to generate time-interval process models. Finally, a precision measure is defined to evaluate the quality of the generated models. With the measure, managers can select the best process model among a set of candidate models without human involvement.}, acmid = {1851996}, added-at = {2014-05-13T21:57:24.000+0200}, address = {Hingham, MA, USA}, author = {Tsai, Chieh-Yuan and Jen, Henyi and Chen, Yi-Ching}, biburl = {https://www.bibsonomy.org/bibtex/2fa4d8a1d14d0f13af2feec5a2b10be81/stefah}, description = {Time-interval process model discovery and validation--a genetic process mining approach}, doi = {10.1007/s10489-010-0240-5}, interhash = {bfb68a4a42b14ae51b0dbc10f5d04dae}, intrahash = {fa4d8a1d14d0f13af2feec5a2b10be81}, issn = {0924-669X}, issue_date = {August 2010}, journal = {Applied Intelligence}, keywords = {discovery mining process}, month = aug, number = 1, numpages = {13}, pages = {54--66}, publisher = {Kluwer Academic Publishers}, timestamp = {2014-05-13T21:57:24.000+0200}, title = {Time-interval Process Model Discovery and Validation--a Genetic Process Mining Approach}, url = {http://dx.doi.org/10.1007/s10489-010-0240-5}, volume = 33, year = 2010 }