Skip to main content

Goal-Heuristic Analysis Method for an Adaptive Process Mining

  • Conference paper
  • First Online:
Proceedings of the International Conference on IT Convergence and Security 2011

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 120))

Abstract

Because of the rapid changes in the market environment and the uncertain demands from the customers, the investment in the information system by the corporate is increasing. This also resulted in the adoption of the process management system, which is intended for the adaptation to the speed of such changes, creation of competitiveness, and systematic management of the business process. To process the service demands from the customers that come in a dynamic manner, an analysis on the possible scope of changes on the recognition of the problems will be required, as well as the concept of data mining to redesign the process based on the adaptive decisions. The existing workflow mining technology was designed to extract business process redesign information from simple database fields or create a process model by collecting, identifying, and analyzing log information from the system that it could not be dynamically reconfigured by exploring the process flow suitable for new requests made on business process. In this study, an analytical method will be suggested using a heuristic algorithm based on the goals to create an adaptive process mining model that could provide a continuous service demand scenario that is created dynamically.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

ASD:

Degree of similarities of the activities

AID:

Degree of importance of the activities

ARD:

Degree of correlation of the activities

References

  1. Agrawal R, Gunopulos D, Leymann F (1998) Mining process models from work-flow logs. In 6th International Conference on Extending Database Technology p 469–483

    Google Scholar 

  2. Cook JE, Wolf AL (1998) Discovering Models of Software Processes from Event Based Data. ACM Trans Softw Eng Method 7(3):215–249

    Article  Google Scholar 

  3. van der Aalst WMP, Weijters AJMM, Maruster L (2004) Workflow mining : discovering process models from event logs. IEEE Trans Knowl Data Eng 16(9):1128–1142

    Article  Google Scholar 

  4. van der Aalst WMP, de Medeiros AKA, Weijters AJMM (2005) Genetic process mining. Lect Notes Comput Sci 3536:48–69

    Article  Google Scholar 

  5. de Medeiros AKA, Weijters AJMM, van der Aalst WMP (2006) Genetic process mining : a basic approach and its challenges. Lect Notes Comput Sci 3812:203–215

    Article  Google Scholar 

  6. van der Aalst WMP, Reijers HA, Weijters AJMM, van Dongen BF, Alves de Medeiros AK, Song MS, Verbeek HMW (2007) Business process mining : An industrial application. Inf syst 32(5):713–732

    Article  Google Scholar 

  7. Chung SY, Kwon ST (2008) A process mining using association rule and sequence pattern. J Soc Korea Ind Syst Eng 31(2):104–111 June 2008

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Su-Jin Baek .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media B.V.

About this paper

Cite this paper

Baek, SJ., Ko, JW., Kim, GJ., Han, JS., Song, YJ. (2012). Goal-Heuristic Analysis Method for an Adaptive Process Mining. In: Kim, K., Ahn, S. (eds) Proceedings of the International Conference on IT Convergence and Security 2011. Lecture Notes in Electrical Engineering, vol 120. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2911-7_37

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-2911-7_37

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-2910-0

  • Online ISBN: 978-94-007-2911-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics