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BPM for the Masses: Empowering Participants of Cognitive Business Processes

  • Aleksander SlominskiEmail author
  • Vinod Muthusamy
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)

Abstract

Authoring, developing, monitoring, and analyzing business processes has required both domain and IT expertise since Business Process Management tools and practices have focused on enterprise applications and not end users. There are trends, however, that can greatly lower the bar for users to author and analyze their own processes. One emerging trend is the attention on blockchains as a shared ledger for parties collaborating on a process. Transaction logs recorded in a standard schema and stored in the open significantly reduces the effort to monitor and apply advanced process analytics. A second trend is the rapid maturity of machine learning algorithms, in particular deep learning models, and their increasing use in enterprise applications. These cognitive technologies can be used to generate views and processes customized for an end user so they can modify them and incorporate best practices learned from other users’ processes.

Keywords

BPM Cognitive computing Blockchain Privacy Machine learning 

References

  1. 1.
    Aalst, W.M.P.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013, 37 pages (2013). Article ID 507984Google Scholar
  2. 2.
    Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: Proceedings of the IEEE Symposium on Security and Privacy (2008)Google Scholar
  3. 3.
    Chen, D., Zhao, H.: Data security and privacy protection issues in cloud computing. In: International Conference on Computer Science and Electronics Engineering (2012)Google Scholar
  4. 4.
    Muthusamy, V., Slominski, A., Ishakian, V., Khalaf, R., Reason, J., Rozsnyai, S.: Lessons learned using a process mining approach to analyze events from distributed applications. In: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems (2016)Google Scholar
  5. 5.
    Evermann, J.-R., Fettke, P.: Predicting process behaviour using deep learning. Decision Support Systems (2017)Google Scholar
  6. 6.
    Ehrig, M., Koschmider, A., Oberweis, A.: Measuring similarity between semantic business process models. In: Proceedings of the Fourth Asia-Pacific Conference on Conceptual Modelling, vol. 67 (2007)Google Scholar
  7. 7.
    Kelly III, J.E.: Computing, Cognition and the Future of Knowing: How Humans and Machines Are Forging a New Age of Understanding. IBM (2015). https://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf
  8. 8.
    Tarafdar, M., Beath, C., Ross, J.: Enterprise cognitive computing applications: opportunities and challenges. IT Prof. 19(4), 21–27 (2017)CrossRefGoogle Scholar
  9. 9.
    Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf
  10. 10.
  11. 11.
    Ethereum Project. https://www.ethereum.org/

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

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