Abstract
Business process redesign is increasingly motivated by analytical requirements, and data intensive activities such as image processing, prediction and classification are increasingly incorporated into business processes. Resulting business processes are referred as to data-intensive business processes. Such processes require data of various types and from different sources as well as analytical data transformations to guide and automate business process execution. Challenges associated with data-intensive business processes are identification and justification of opportunities for using advanced analytical processing methods and selection of appropriate technologies for enactment of these processes. This chapter proposes a method for specifying requirements towards data-intensive activities and uses these requirements to select appropriate implementation and enactment technologies. An example of business process redesign is discussed.
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References
Zimmermann, A., Schmidt, R., Jugel, D., Möhring, M.: Adaptive enterprise architecture for digital transformation. Commun. Comput. Inf. Sci. 567, 308–319 (2016)
Salesforce: What Is Digital Transformation? (2018) https://www.salesforce.com/products/platform/what-is-digital-transformation/
Abawajy, J.: Comprehensive analysis of big data variety landscape. Int. J. Parallel Emergent Distrib. Syst. 30(1), 5–14 (2015)
Weske, M.: Business Process Management: Concepts, Languages, Architectures in Business Process Management: Concepts, Languages, Architectures, Springer (2007)
Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Key challenges in cloud computing: enabling the future internet of services. IEEE Internet Comput. 17(4), 18–25 (2013)
Grabis, J., Kampars, J.: Application of microservices for digital transformation of data-intensive business processes. In: ICEIS 2018—Proceedings of the 20th International Conference on Enterprise Information Systems, pp. 736–742 (2018)
Van der Aalst, W.M.P., Ter Hofstede, A.H.M., Weske, M.: Business process management: A survey. In: van der Aalst, W.M.P., Weske, M. (eds.) Business Process Management. BPM 2003. Lecture Notes in Computer Science, vol. 2678. Springer, Berlin, Heidelberg (2003)
De Morais, R.M., Kazan, S., de Pádua, S.I.D., Costa, A.L.: An analysis of BPM lifecycles: from a literature review to a framework proposal. Bus. Process Manag. J. 20(3), 412–432 (2014)
Becker, J., Kugeler, M., Rosemann, M.: Process Management: A Guide for the Design of Business Process. Springer, New York (2011)
Reijers, H.A., Liman Mansar, S.: Best practices in BP redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega 33, 283–306 (2005)
Barros, O.: Business process patterns and frameworks: reusing knowledge in process innovation. Bus. Process Manag. J. 13, 47–69 (2007)
Damij, N., Damij, T., Grad, J., Jelenc, F.: A methodology for BP improvement and IS development. Inf. Softw. Technol. 50(112), 7–1141 (2008)
Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Berlin, Heidelberg (2018)
Zellner, G.: A structured evaluation of business process improvement approaches. Bus. Process Manag. J. 17(2), 203–237 (2011)
Sidorova, A., Isik, O.: BP research: a cross-disciplinary review. Bus. Process Manag. J. 16(4), 566–597 (2010)
Biard, T., Mauff, A.L., Bigand, M., Bourey, J.: Separation of decision modeling from business process modeling using new decision model and notation (DMN) for automating operational decision-making. IFIP Adv. Inf. Commun. Technol. 463, 489–496 (2015)
Mertens, S., Gailly, F., Poels, G.: Towards a decision-aware declarative process modeling language for knowledge-intensive processes. Expert Syst. Appl. 87, 316–334 (2017)
Lapouchnian, A., Yu, E., Sturm, A.: Design dimensions for business process architecture. Lect. Notes Comput. Sci. 9381, 276–284 (2015)
Lapouchnian, A., Babar, Z., Yu, E., Chan, A., Carbajales, S.: Designing process architectures for user engagement with enterprise cognitive systems. Lect. Notes Bus. Inf. Process. 305, 141–155 (2017)
Chou, D.C., Bindu Tripuramallu, H., Chou, A.Y.: BI and ERP integration. Inf. Manag., Comput. Secur. 13(5), 340–349 (2005)
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., Becker, B., Caserta, J.: Kimball’s Data Warehouse Toolkit Classics: 3. Wiley (2014)
Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44(3), 1–70 (2012)
Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. O’Reilly Media, Newton (2013)
Dragoni N., et al.: Microservices: yesterday, today, and tomorrow. In: Mazzara, M., Meyer, B. (eds.) Present and Ulterior Software Engineering. Springer (2017)
Schuster, D., Muthmann, K., Esser, D., Schill, A., Berger, M., Weidling, C., Aliyev, K., Hofmeier, A.: Intellix—End-user trained information extraction for document archiving. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 101–105 (2013)
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Grabis, J. (2021). Transformation and Enactment of Data-Intensive Business Processes Using Advanced Architectural Styles. In: Zimmermann, A., Schmidt, R., Jain, L. (eds) Architecting the Digital Transformation. Intelligent Systems Reference Library, vol 188. Springer, Cham. https://doi.org/10.1007/978-3-030-49640-1_16
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DOI: https://doi.org/10.1007/978-3-030-49640-1_16
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