CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Performance Analysis of Business Processes using Process Mining

by Omer S. Dawood
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 37
Year of Publication: 2018
Authors: Omer S. Dawood
10.5120/ijca2018916651

Omer S. Dawood . Performance Analysis of Business Processes using Process Mining. International Journal of Computer Applications. 180, 37 ( Apr 2018), 27-30. DOI=10.5120/ijca2018916651

@article{ 10.5120/ijca2018916651,
author = { Omer S. Dawood },
title = { Performance Analysis of Business Processes using Process Mining },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 180 },
number = { 37 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 27-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number37/29332-2018916651/ },
doi = { 10.5120/ijca2018916651 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:02:57.084762+05:30
%A Omer S. Dawood
%T Performance Analysis of Business Processes using Process Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 37
%P 27-30
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper is aimed to investigate the real time Business process Performance and talent to reinforce the performance. An easy framework was developed to indicate the various steps of analysing and enhancing the process performance. Projected during this paper are a BAM style framework for the real-time business performance management associated an implementation of BAM system model to indicate the pertinence of proposed framework. The goals of business activity observance are to produce real time data concerning the standing and results of varied operations, processes, and transactions. The framework consists from three stages and its facilitates the enhancements of business process.

References
  1. Stefanie e at “Business Process Management: 9th International Conference,” Chennai . India, eISSN 1611-3349, April 2011. (references)
  2. Jin Gu Kang and Kwan Hee Han, “A Business Activity Monitoring System Supporting Real-Time Business Performance Management”, [Third 2008 International Conference on Convergence and Hybrid Information Technology korea, Vol 1, 2008].
  3. Jan-Philipp e at, “Extending BPMN for Business Activity Monitoring” [2012 45th Hawaii International Conference on System Sciences, 2012].
  4. Wang Pan and He Wei, “Research on key performance indicator (KPI) of business process”, [2012 Second International Conference on Business Computing and Global Informatization, Shanghai, 2012].
  5. W.Schmidt,Business Intelligence and Performance Management,Springer-Verlag London 2013.
  6. http://www.redbooks.ibm.com/redbooks/pdfs/sg247638.pdf, July 2008
  7. James Crump, business activity monitoring (bam): The New Face of BPM, June 2006.
  8. BusinessActivityMonitoring.http://www.fujitsu.com/downloads/SVC/fc/fs/bam.pdf.Access date:[Second of Nov,2015-6:10 PM]
  9. Peter Rausch • Alaa F. Sheta • Aladdin Ayesh, Business Intelligence and Performance Management, 2013.
  10. Kapil Pant, Matjaz B. Juric,Business Process Driven SOA using BPMN and BPEL,August 2008.
  11. McCoy, D., Schulte, R., Buytendijk, F.,Rayner, N., and Tiedrich, A., “Business Activity Monitoring: The Promise and Reality”, Gartner, Gartner’s Marketing Knowledge and Technology Commentary COM-13-9992, 2001.
  12. Wikipedia, “Business Activity Monitoring”, http://en.wikipedia.org/wiki/Business_activity_monitoring, 2017, access time:[10/3/2017, 7:30 pm].
  13. Govekar, M. and Schulte, R., “BAM Architecture: More Building Blocks Than You Think”, Gartner, AV-15-5070, 2002.
  14. R.P. Bose, W.M.P. van der Aalst, Dealing with concept drifts in process mining, Neural Netw. Learn. Syst. IEEE Trans. 25 (2014) 154–171.
  15. R. Mans, W.M.P. van der Aalst, R. Vanwersch, Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes, Springer International Publishing, 2015, pp. 17–26.
  16. M. Cho, M. Song, S. Yoo, A systematic methodology for outpatient process analysis based on process mining, Int. J. Ind. Eng. 22 (2015) 480–493.
  17. W.M.P. van der Aalst, M.H. Schonenberg, M. Song, Time prediction based on process mining, Inf. Syst. 36 (2011) 450–475.
  18. M. Song, W.M.P. van der Aalst, Towards comprehensive support for organizaional mining, Decis. Support Syst. 46 (2008) 300–317.
  19. F.M. Maggi, A. Mooij, W. van der Aalst, User-guided discovery of declarative process models, IEEE Symposium on Computational Intelligence and Data Mining, 2011. pp. 192–199. http://dx.doi.org/10.1109/CIDM.2011.5949297.
  20. F.M. Maggi, J.C. Bose, W. van der Aalst, Efficient discovery of understandable declarative process models from event logs, Int. Conf. on Advanced Information 89 (2016) 87–97 97 Systems Engineering (CAiSE), 7328, 2012. pp. 270–285. http://dx.doi.org/10. 1007/978-3-642-31095-9_18.
  21. M. Westergaard, C. Stahl, H. Reijers, UnconstrainedMiner: Efficient Discovery of Generalized Declarative Process Models, Eindhoven University of Technology. 2013, URL https://publications.hse.ru/en/preprints/117624631.
  22. J.C. Bose, F.M. Maggi, W. van der Aalst, Enhancing declare maps based on event correlations, Int. Conf. on Business Process Management (BPM), 8094, 2013. pp. 97–112. http://dx.doi.org/10.1007/978-3-642-40176-3_9.
  23. F.M. Maggi, J.C. Bose, W.M. van der Aalst, A knowledge-based integrated approach for discovering and repairing declare maps, Int. Conf. on Advanced Information Systems Engineering (CAiSE), 7908, 2013. pp. 433–448. http://dx. doi.org/10.1007/978-3-642-38709-8_28.
  24. F. Chesani, E. Lamma, P. Mello, M. Montali, F. Riguzzi, S. Storari, Exploiting inductive logic programming techniques for declarative process mining, Trans. Petri Nets and Other Models of Concurrency 2 (2009) 278–295. http://dx.doi. org/10.1007/978-3-642-00899-3_16.
  25. F.M. Maggi, Discovering metric temporal business constraints from event logs, Int. Conf. on Perspectives in Business Informatics Research (BIR) 194, Springer. 2014, pp. 261–275. http://dx.doi.org/10.1007/978-3-319-11370-8_19.
  26. F.M. Maggi, M. Dumas, Discovering data-aware declarative process models from event logs, Int. Conf. on Business Process Management (BPM), 8094, 2013. pp. 1–16. http://dx.doi.org/10.1007/978-3-642-40176-3_8.
Index Terms

Computer Science
Information Sciences

Keywords

Process mining BAM Real Time Performance Monitoring Bizagi.

Powered by PhDFocusTM