Open Access Open Access  Restricted Access Subscription or Fee Access

Knowledge Discovery Process through Process Mining

M.S. Saravanan, R.J. Rama Sree

Abstract


The main goal of process mining is to extract semantic knowledge from process execution traces for the purposes of process understanding, innovation and improvement. In this paper we describe a leading process model for a knowledge discovery process and metadata knowledge discovery process. Normally the knowledge can be developed or generated with various process models. Hence the new proposed process model can help to discover new decision or process method or control system for the industry experts or process controllers. Therefore, this paper deals about that how to create an event log and create a mined process model. The process mining is an emerging technology used to generate event logs from input data. Hence we demonstrated the event log for an automobile repair business process. So, the data stored in the database can be effectively analyze and discover the knowledge for various activities of automobile repair process. This paper demonstrates a metadata of an automobile repair process using process mining tools such as ProM framework by ProM mining framework Incorporation. The metadata such as Extensible Markup Language (XML) or Mining Extensible Markup Language (MXML) script is mostly accepted by process mining tools to generate a new mined process model for the better process management or discovery of knowledge for any type of applications. Hence, this paper also deals about the leading process mining algorithm and metadata standards based on XML and MXML.

Keywords


Knowledge Discovery, Process Model, Process Mining, Event Log, Meta Data.

Full Text:

PDF

References


Jensen, C., Scacchi, W. Simulating an Automated Approach to Discovery and Modeling of Open Source Software Development Processes. In Proceedings of ProSim'03 Workshop on Software Process Simulation and Modeling, (Portland, OR May 2003).

van der Aalst, W., van Dongen, B., Herbst, J., Maruster, L., Schimm, G., and Weijters, A., Workflow Mining: A survey of Issues and Approaches. IEEE Journal of the Data and Knowledge Engineering, 2003, 47(2), pp. 237-267.

W. Van der Aalst and A. Weijters, "Process mining: a research agenda," Computers in Industry, 2004, vol. 53, pp. 231-244

Zidrina Pabarskaite, Aistis Raudys, A process of knowledge discovery from web log data: Systematization and critical review, published in Journal of Intelligent Information Systems, 2007, Volume 28, Issue 1, pp. 3-4.

Elena Ikonomovska, João Gama and Sašo Džeroski, " Learning model trees from evolving data streams, "Journal of Data Mining and Knowledge Discovery, 2010, Vol. 21, pp. 63-69.

Aalst, W. M. P. van der, B. F. van Dongen, C. W. Günther, R. S. Mans, A. K. Alves deMedeiros, A. Rozinat, V. Rubin, M. Song, A. J. M. M. Weijters and H. M. W. Verbeek, "ProM 4.0: Comprehensive support for real process analysis," Proceedings of the 28th International Conference on Applications and Theory of Petri Nets 2007, Siedcle, Poland, 2007, pp. 484-494.

Aalst, W. M. P. van der, A. K. A. de Medeiros and A. J. M. M. Weijters, "Genetic process mining: an experimental evaluation," Data Mining and Knowledge Discovery, Vol. 14, Issue 2, 2007, pp. 245-304.

Aalst, W. M. P. van der, H. A. Reijers and M. Song, "Discovering social networks from event logs," Computer Supported Cooperative Work, 2005, Vol. 14, Issue 6, pp. 549-593.

Agrawal, R., D. Gunopulos and F. Leymann, "Mining process models from workflow logs, "Proceedings of the 6th International Conference on Extending Database Technology, 1998, pp. 469-483.

van der Aalst, W., Weijters, A., and Maruster, L., Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering, 2004, Vol. 16, Issue 9, pp. 1128–1142.

Klein, M. and A. Bernstein, "Towards high-precision service retrieval," IEEE Internet Computing, 8, 1, (2004), 30-36.

Tan, P.-N., M. Steinbach and V. Kumar, Introduction to data mining, Addison Wesley, 2006, pp. 1-4.

Mans, R. S., M. H. Schonenberg, M. Song, W. M. P. van der Aalst and P. J. M. Bakker, "Process mining in healthcare - A case study," Proceedings of the HEALTHINF 2008, Funchal, Madeira, Portugal, 2008, pp. 118-125.

Yang, W.-S. and S.-Y. Hwang, "A process-mining framework for the detection of healthcare fraud and buse," Expert Systems with Applications, 31, (2006), 56-68.

van der Aalst, W., Reijers, H., Weijters, A., van Dongen, B., de Medeiros, A. A., Song, M., and Verbeek, H., Business process mining : an industrial application. Journal of the Information Systems, 2007, 32(5).


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.