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Tytuł artykułu

Software architecture design of the information technology for real-time business process monitoring

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Języki publikacji
EN
Abstrakty
EN
Having precise understanding of how business processes are performed in real-life is an important input for decision makers and consequently is a strong competitive advantage for an organization. In the constantly changing modern business environment it is crucial to provide that information as soon as possible, preferably in the real-time mode. In practice, such kind of tasks are usually resolved by means of Business Intelligence solutions implemented either from scratch or based upon customizable packages. Despite of the wide range currently available types of data visualizations, modern BI solutions still lacks features to represent data obtained from process-aware systems, for example control flow charts. Current paper is devoted to the information technology for real-time business process monitoring. The represented solution is an extendable software which is based on the lambda architecture and a streaming process discovery technique.
Twórcy
autor
  • Automated Control Systems Department Lviv Polytechnic National University Lviv, Ukraine
autor
  • Automated Control Systems Department Lviv Polytechnic National University Lviv, Ukraine
Bibliografia
  • 1. Van der Aalst W. M. P. 2016. Process mining: data science in action, 2nd ed. Berlin: Springer.
  • 2. Van der Aalst W. M. P., Weijters T., Maruster L. 2004. Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142.
  • 3. Van der Aalst W. M. P. et al. 2012. Process Mining Manifesto. In: F. Daniel, K. Barkaoui, S. Dustdar (Ed.), Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing, vol. 99. 169-194. Berlin-Heidelberg: Springer.
  • 4. Bass L., Clements P., and Kazman R. 2012. Software Architecture in Practice, 3rd ed. Addison-Wesley Professional, 120 p.
  • 5. Barbacci M., Klein M. H., Longstaff T. H., Weinstock C. B. 1995. Quality Attributes (Report No. CMU/SEI-95-TR-021). SEI at Carnegie Mellon University, Pittsburgh, Pennsylvania. Retrieved from https://resources.sei.cmu.edu/asset_files/TechnicalReport/1995_005_001_16427.pdf
  • 6. Batyuk A. and Voityshyn V. 2017. Business Processes Monitoring by Means of Real-Time Visual Dashboards. In A. Peleschyshyn, O. Markovets (Eds.), The 6th International Academic Conference on Information, Communication, Society. Lviv, Ukraine: Lviv Polytechnic Publishing House.2017. 204-205.
  • 7. Batyuk A. and Voityshyn V. 2018. Real-Time Process Monitoring Platform: Technical Implementation. In A. Peleschyshyn, O. Markovets (Eds.), The 7th International Academic Conference on Information, Communication, Society 2018 Lviv, Ukraine: Lviv Polytechnic Publishing House. 275-276.
  • 8. Batyuk A. and Voityshyn V. 2018. Software Architecture Design of the Real-Time Processes Monitoring Platform. In O. Vynokurova, D. Peleshko (Eds.), 2018 IEEE Second International Conference on Data Stream Mining & Processing DSMP’2018. Lviv, Ukraine: Lviv Polytechnic Publishing House. 98-101
  • 9. Batyuk A. and Voityshyn V. 2018. Streaming Process Discovery for Lambda Architecture-based Process Monitoring Platform. In T. Shestakevych (Eds.), 2018 IEEE 13th International Scientific and Technical Conference on Computer Science and Information Technologies. CSIT’2018 Lviv, Ukraine: Vezha and Co. 298-301.
  • 10. Burattin A. 2015. Process Mining for Stream Data Sources. Process Mining Techniques in Business Environments. Lecture Notes in Business Information Processing. Cham: Springer. 177-204.
  • 11. Burattin A., Sperduti A., and van der Aalst W. M. P. 2012. Heuristics Miners for Streaming Event Data. CoRR (Computing Research Repository). Retrieved from https://arxiv.org/abs/1212.6383
  • 12. Fan W. and and Bifet A. 2012. Mining Big Data: Current Status, and Forecast to the Future. SIGKDD Explorations, 14(2), 1-5. DOI: 10.1145/2481244.2481246
  • 13. Günther C. W. and van der Aalst W. M. P. 2007. Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics. In: Alonso G., Dadam P., Rosemann M. (Eds.), Business Process Management. BPM 2007. Lecture Notes in Computer Science, 4714 (pp. 328-343). Berlin, Heidelberg: Springer.
  • 14. Günther Ch. W. and Rozinat A. 2012. Disco: Discover Your Processes. Proceedings of the Demonstration Track of the 10th International Conference on Business Process Management (BPM 2012), Tallinn, Estonia, 940, 40-44.
  • 15. Jagadeesh Chandra Bose R. P., and van der Aalst, Wil, M. P., Žliobaitė, I., Pechenizkiy, M. 2011. Handling Concept Drift in Process Mining. Advanced Information Systems Engineering. CAiSE 2011. Lecture Notes in Computer Science, 6741, 391-405.
  • 16. Kerremans M. 2018. Market Guide for Process Mining. Gartner, Inc. Retrieved from https://www.gartner.com/doc/3870291/market-guide-process-mining
  • 17. de Leoni M. and Mannhardt F. 2015. Road Traffic Fine Management Process. Retrieved from https://data.4tu.nl/repository/uuid:270fd440-1057-4fb9-89a9-b699b47990f5
  • 18. Mulesa O., Geche F., Batyuk A., and Buchok V. 2018. Development of Combined Information Technology for Time Series Prediction. In: N. Shakhovska, V. Stepashko (Eds.), Advances in Intelligent Systems and Computing II. CSIT’2017. Advances in Intelligent Systems and Computing, 689 Cham: Springer. 361-373.
  • 19. Pathirage M. 2017. kappa-architecture.com. Retrieved from http://milinda.pathirage org/kappa-architecture.com/
  • 20. Rozinat A. 2010. ProM Tips - Which Mining Algorithm Should You Use. Retrieved from https://fluxicon.com/blog/2010/10/prom-tips-mining-algorithm/
  • 21. Veit F., Geyer-Klingeberg J., Madrzak J., Haug M., and Thomson J. 2017. The Proactive Insights Engine: Process Mining meets Machine Learning and Artificial Intelligence. The 15th International Conference on Business Process Management (BPM 2017). BPM Demo Track and BPM Dissertation Award, Barcelona,Spain, 1920.
  • 22. Weijters A. J. M. M. and Ribeiro J. T. S. 2011. Flexible Heuristics Miner (FHM). 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, 310-317.
  • 23. Weijters A. J. M. M., van der Aalst W. M. P., and Alves De Medeiros A. K. 2006. Process Mining with the Heuristics Miner Algorithm. Eindhoven: BETA Working Paper Series, WP 166.
  • 24. Business activity monitoring. (2018, Mar 7). Retrieved from https://en.wikipedia.org/wiki/Business_activity_monitoring
  • 25. IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams, IEEE Std 1849-2016, 2016.Lambda Architecture. 2017. Retrieved from http://lambda-architecture.net/
  • 26. Scalable Vector Graphics (SVG) 1.1 (Second Edition). (2011, Aug 16). Retrieved from https://www.w3.org/TR/2011/REC-SVG11-20110816/
  • 27. Time Series Database (TSDB) Explained. (2018). Retrieved from https://www.influxdata.com/time-series-database/
  • 28. Web sockets. (2018, Sep). Retrieved from https://html.spec.whatwg.org/multipage/web-sockets.html
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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