%0 Journal Article %1 grigori04bpi %A Grigori, Daniela %A Casati, Fabio %A Castellanos, Malu %A Dayal, Umeshwar %A Sayal, Mehmet %A Shan, Ming-Chien %D 2004 %J Computers in Industry %K research.bizInt.bpm cites.pclass research.mining cites.procm %N 3 %P 321--343 %R 10.1016/j.compind.2003.10.007 %T Business Process Intelligence %U http://dx.doi.org/10.1016/j.compind.2003.10.007 %V 53 %X Business Process Management Systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. BPMSs have the ability of logging information about the business processes they support. Proper analysis of BPMS execution logs can yield important knowledge and help organizations improve the quality of their business processes and services to their business partners. This paper presents a set of integrated tools that supports business and IT users in managing process execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization. We refer to this set of tools as the Business Process Intelligence (BPI) tool suite. Experimental results presented in this paper are very encouraging. We plan to investigate further enhancements on the BPI tools suite, including automated exception prevention, and refinement of process data preparation stage, as well as integrating other data mining techniques.

@article{grigori04bpi, abstract = { Business Process Management Systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. BPMSs have the ability of logging information about the business processes they support. Proper analysis of BPMS execution logs can yield important knowledge and help organizations improve the quality of their business processes and services to their business partners. This paper presents a set of integrated tools that supports business and IT users in managing process execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization. We refer to this set of tools as the Business Process Intelligence (BPI) tool suite. Experimental results presented in this paper are very encouraging. We plan to investigate further enhancements on the BPI tools suite, including automated exception prevention, and refinement of process data preparation stage, as well as integrating other data mining techniques.}, added-at = {2009-06-25T16:49:35.000+0200}, author = {Grigori, Daniela and Casati, Fabio and Castellanos, Malu and Dayal, Umeshwar and Sayal, Mehmet and Shan, Ming-Chien}, biburl = {https://www.bibsonomy.org/bibtex/26c4a92e5565321272618cead4fdc9402/msn}, doi = {10.1016/j.compind.2003.10.007}, file = {grigori04bpi.pdf:cites\\procminer\\grigori04bpi.pdf:PDF}, interhash = {1d68a3314a218171d65e7be97ba8f92d}, intrahash = {6c4a92e5565321272618cead4fdc9402}, journal = {Computers in Industry}, keywords = {research.bizInt.bpm cites.pclass research.mining cites.procm}, month = {April}, number = 3, pages = {321--343}, timestamp = {2009-06-25T16:49:35.000+0200}, title = {Business Process Intelligence}, url = {http://dx.doi.org/10.1016/j.compind.2003.10.007}, volume = 53, year = 2004 }