Abstract
Analytics Process Management (APM) is an emerging branch of Business Process Management that is focused on supporting Business Analysts and others as they apply analytics approaches, algorithms, and outputs in order to discover and/or repeatedly produce business-relevant insights and apply them into on-going business operations. While APM is now occurring in many businesses, it is typically managed in ad hoc ways using a variety of different tools and practices. This paper proposes to use principles from Case Management (or equivalently, Business Artifacts) to provide a foundational structure for APM. In particular, six key classes of Case Types are identified, that can model the vast majority of activities and data being manipulated in APM contexts. These Case Types can simplify support for managing provenance, auditability, repeatability, and explanation of analytics results. The paper also identifies two key adaptations of the classical Case Management paradigm that are needed to support APM. The paper validates the proposed Case Types and adaptations by examining two recent systems built at IBM Research that support Business Analysists in the use of analytics tools.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Callery, M., III, F.H., Hull, R., Linehan, M.H., Sukaviriya, P.N., Vaculín, R., Oppenheim, D.V.: Towards a plug-and-play B2B marketing tool based on time-sensitive information extraction. In: IEEE International Conference on Services Computing, SCC 2014, Anchorage, AK, USA, June 27-July 2, 2014, pp. 821–828 (2014)
Chaudhuri, S., Dayal, U., Narasayya, V.R.: An overview of business intelligence technology. Commun. ACM 54(8), 88–98 (2011)
Heath III., F., Hull, R., Khabiri, E., Riemer, M., Sukaviriya, N., Vaculin, R.: Alexandria: extensible framework for rapid exploration of social media. In: Proceedings of the IEEE Big Data Congress (2015). to appear
Heath III, F.F., Hull, R.: Analytics process management: a new challenge for the BPM community. In: Fournier, F., Mendling, J. (eds.) Business Process Management Workshops. LNBIP, vol. 202, pp. 175–185. Springer, Heidelberg (2014)
Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)
Krishnamurthy, R., Li, Y., Raghavan, S., Reiss, F., Vaithyanathan, S., Zhu, H.: Systemt: a system for declarative information extraction. SIGMOD Rec. 37(4), 7–13 (2008)
Manyika, J., et al.: Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute report, May 2011. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
MathWorks MATLAB official web site, Accessed June 5, 2015. http://www.mathworks.com/products/matlab/
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). CoRR abs/1301.3781
Provost, F., Fawcett, T.: Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly, Sebastopol, CA, USA (2013)
RapidMiner. RapidMiner Studio Manual. www.rapidminer.com/documentation/
RapidMiner Opensource Development Team. RapidMiner - Data Mining, ETL, OLAP, BI. http://sourceforge.net/projects/rapidminer/
Shearer, C.: The CRISP-DM model: The new blueprint for data mining. J. Data Warehouse. 5(4), 13–22 (2000)
IBM SPSS official web site, accessed June 5, 2015. http://www-01.ibm.com/software/analytics/spss/
Truong, H.L., Dustdar, S.: A survey on cloud-based sustainability governance systems. IJWIS 8(3), 278–295 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Heath III, F.F., Hull, R., Oppenheim, D. (2016). Applying Case Management Principles to Support Analytics Process Management. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-42887-1_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42886-4
Online ISBN: 978-3-319-42887-1
eBook Packages: Computer ScienceComputer Science (R0)