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Reseach Article

Machine Learning Approach for Process Modeling

by P. V. Kumaraguru, S. P. Rajagopalan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 19
Year of Publication: 2012
Authors: P. V. Kumaraguru, S. P. Rajagopalan
10.5120/9221-3778

P. V. Kumaraguru, S. P. Rajagopalan . Machine Learning Approach for Process Modeling. International Journal of Computer Applications. 57, 19 ( November 2012), 12-15. DOI=10.5120/9221-3778

@article{ 10.5120/9221-3778,
author = { P. V. Kumaraguru, S. P. Rajagopalan },
title = { Machine Learning Approach for Process Modeling },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 19 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number19/9221-3778/ },
doi = { 10.5120/9221-3778 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:53.394315+05:30
%A P. V. Kumaraguru
%A S. P. Rajagopalan
%T Machine Learning Approach for Process Modeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 19
%P 12-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

All the modern business industries are in the urge to find the ways and means to handle the digital data which are generated automatically for every business transaction. These transactional data are recorded in the temporal database as event logs along with a unique case and event ID for future reference. Every process will leave its foot prints in the event logs, the size of these logs has grown unimaginable huge and triggered many challenges for the researchers. Providing only hardware solutions for storage is not wise enough, instead extracting process models and enhancing it through machine learning techniques is the real challenge of the day. This paper has made an attempt to find the suitable notations for modeling the process and suggests variousnotations for process model which intern leads for process optimization.

References
  1. Machine learning – John Anderson.
  2. Process modeling from insurance event log – International Journal of Computer Applications.
  3. Wil M. P. Van der Aalst, 2010,Process Mining, Springer.
  4. W. M. P. Van der Aalst 1998 The applications of Petri Nets to wotk flow management, The journal of Circuits, Systems and computers.
  5. W. M. P. Van der Aalst and K. M. VanHee. Workflow Management: Models, Methods, and system. MIT press, Cambridge, MA, 2002.
  6. S. Kumaran and K. Raja, Modeling and simulation of Projects with Petri Nets. American journal of applied Sciences, 2008.
  7. A. J. M. MWeijters and W. M. P Van der Aalst, Process Mining : 2001 Discovering workflow Models from Event based Data, 13th Belgium- Netherlands Conference on Artificial intelligence(BNAIC 2001).
  8. W. M. P van der Aalst A. J. M. M. Weijters, and L. Maruster. 2004, Work flow Mining: Discovering process models from Event Logs. IEEE Transactions on Knowledge and Data Engineering.
Index Terms

Computer Science
Information Sciences

Keywords

Business intelligence Temporal Database Data explosion Event logs process model machine learning stochastic