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

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dc.citation.epage22
dc.citation.issue3
dc.citation.journalTitleEcontechmod
dc.citation.spage13
dc.citation.volume7
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorBatyuk, A.
dc.contributor.authorVoityshyn, V.
dc.coverage.placenameLublin
dc.date.accessioned2019-06-18T12:03:47Z
dc.date.available2019-06-18T12:03:47Z
dc.date.created2018-06-18
dc.date.issued2018-06-18
dc.description.abstractHaving 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.
dc.format.extent13-22
dc.format.pages10
dc.identifier.citationBatyuk A. Software architecture design of the information technology for real-time business process monitoring / A. Batyuk, V. Voityshyn // Econtechmod. — Lublin, 2018. — Vol 7. — No 3. — P. 13–22.
dc.identifier.citationenBatyuk A. Software architecture design of the information technology for real-time business process monitoring / A. Batyuk, V. Voityshyn // Econtechmod. — Lublin, 2018. — Vol 7. — No 3. — P. 13–22.
dc.identifier.issn2084-5715
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45133
dc.language.isoen
dc.relation.ispartofEcontechmod, 3 (7), 2018
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dc.relation.references2. 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.
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dc.relation.references6. 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.
dc.relation.references7. 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.
dc.relation.references8. 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
dc.relation.references9. 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.
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dc.relation.references22. 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.
dc.relation.references23. 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.
dc.relation.references24. Business activity monitoring. (2018, Mar 7). Retrieved from https://en. wikipedia.org/wiki/Business_activity_monitoring
dc.relation.references25. IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams, IEEE Std 1849–2016,2016.
dc.relation.references26. Lambda Architecture. 2017. Retrieved fromhttp://lambda-architecture. net/
dc.relation.references27. Scalable Vector Graphics (SVG) 1.1 (Second Edition). (2011, Aug 16). Retrieved fromhttps://www. w3. org/TR/2011/REC-SVG11-20110816/
dc.relation.references28. Time Series Database (TSDB) Explained. (2018). Retrieved from https://www. influxdata. com/timeseries-database/
dc.relation.references29. Web sockets. (2018, Sep). Retrieved from https://html.spec. whatwg. org/multipage/web-sockets. html
dc.relation.referencesen1. Van der Aalst, W. M. P. 2016. Process mining: data science in action, 2nd ed. Berlin: Springer.
dc.relation.referencesen2. 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.
dc.relation.referencesen3. 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.
dc.relation.referencesen4. Bass, L., Clements, P., and Kazman, R. 2012. Software Architecture in Practice, 3rd ed. Addison- Wesley Professional, 120 p.
dc.relation.referencesen5. 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
dc.relation.referencesen6. 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.
dc.relation.referencesen7. 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.
dc.relation.referencesen8. 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
dc.relation.referencesen9. 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.
dc.relation.referencesen10. 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.
dc.relation.referencesen11. 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
dc.relation.referencesen12. 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
dc.relation.referencesen13. Günther C. W. and van der AalstW. 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.
dc.relation.referencesen14. 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 (BPM2012), Tallinn, Estonia, 940, 40–44.
dc.relation.referencesen15. 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. CAiSE2011. Lecture Notes in Computer Science, 6741,391-405.
dc.relation.referencesen16. Kerremans, M. 2018. Market Guide for Process Mining. Gartner, Inc. Retrieved from https://www. gartner. com/doc/3870291/market-guide-processmining
dc.relation.referencesen17. de Leoni, M. and Mannhardt, F. 2015. Road Traffic Fine Management Process. Retrieved fromhttps://data.4tu. nl/repository/uuid:270fd440-1057-4fb9-89a9-b699b47990f5
dc.relation.referencesen18. 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.
dc.relation.referencesen19. Pathirage, M. 2017. kappa-architecture. com. Retrieved from http://milinda. pathirage.org/kappa-architecture. com/
dc.relation.referencesen20. Rozinat, A. 2010. ProM Tips – Which Mining Algorithm Should You Use. Retrieved fromhttps://fluxicon. com/blog/2010/10/prom-tipsmining- algorithm/
dc.relation.referencesen21. 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 (BPM2017). BPM Demo Track and BPM Dissertation Award, Barcelona, Spain, 1920.
dc.relation.referencesen22. 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.
dc.relation.referencesen23. 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.
dc.relation.referencesen24. Business activity monitoring. (2018, Mar 7). Retrieved from https://en. wikipedia.org/wiki/Business_activity_monitoring
dc.relation.referencesen25. IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams, IEEE Std 1849–2016,2016.
dc.relation.referencesen26. Lambda Architecture. 2017. Retrieved fromhttp://lambda-architecture. net/
dc.relation.referencesen27. Scalable Vector Graphics (SVG) 1.1 (Second Edition). (2011, Aug 16). Retrieved fromhttps://www. w3. org/TR/2011/REC-SVG11-20110816/
dc.relation.referencesen28. Time Series Database (TSDB) Explained. (2018). Retrieved from https://www. influxdata. com/timeseries-database/
dc.relation.referencesen29. Web sockets. (2018, Sep). Retrieved from https://html.spec. whatwg. org/multipage/web-sockets. html
dc.relation.urihttps://resources
dc.relation.urihttps://arxiv
dc.relation.urihttps://www
dc.relation.urihttps://data.4tu
dc.relation.urihttp://milinda
dc.relation.urihttps://fluxicon
dc.relation.urihttps://en
dc.relation.urihttp://lambda-architecture
dc.relation.urihttps://html.spec
dc.rights.holder© Copyright by Lviv Polytechnic National University 2018
dc.rights.holder© Copyright by Polish Academy of Sciences 2018
dc.rights.holder© Copyright by University of Engineering and Economics in Rzeszów 2018
dc.rights.holder© Copyright by University of Life Sciences in Lublin 2018
dc.subjectinformation technology
dc.subjectprocess mining
dc.subjectstreaming process discovery
dc.subjectlambda architecture
dc.subjectHeuristic Miner
dc.titleSoftware architecture design of the information technology for real-time business process monitoring
dc.typeArticle

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Econtechmod. – 2018. – Vol. 7, No. 3