Abstract
Efficient processes give companies the edge required to prevail in global competition. Processes can have a high impact on important factors like product and service quality as well as overall economic efficiency. Hence process improvement plays an increasingly important role in many companies. The first step in process improvement and analysis is understanding the process. While a number of process analysis tools are available, these tools can only analyze processes for which log data (e.g. generated by BPM systems) exists. This paper introduces a tool that allows users to collect and structure traces from undocumented processes like workarounds or improvised processes in order to generate log files. The tool supports query specific ad-hoc exchange of ontologies in order to extract information from unstructured documents containing process traces as well as data extraction components for common databases. It thus bridges the gap between process traces in unstructured, heterogenous documents and process analysis software.
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
Cook, J.E., Wolf, A.L.: Discovering models of software processes from event-based data. ACM Trans. Softw. Eng. Methodol. 7(3), 215–249. ISSN:1049-331X (1998)
Schiefer, J., et al.: Event data warehousing for complex event processing. In: Loucopoulos, P., Cavarero, J.L. (eds.) IEEE, RCIS, pp. 203–212 (2010)
van der Aalst, W., van Hee, K.M.: Workow Management: Models, Methods, and Systems. Cooperative Information Systems. MIT Press, Cambridge (2004)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings Data Engineering 95, ICDE ’95. IEEE Computer Society, Washington, DC, USA, pp. 3–14 (1995)
Das, S., Mozer, M.: A unified gradient-descent/clustering architecture for finite state machine induction. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Morgan Kaufmann, NIPS, pp. 19–26 (2003) ISBN:1-55860-322-0
Duan, B., Shen, B.: Software process discovery using link analysis. In: IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 60–63 (2011)
Werf, J.M., et al.: Process discovery using integer linear programming. In: Proceedings of Petri Nets 08, pp. 368–387. Springer, Heidelberg
van Dongen, B.F., et al.: The prom framework: a new era in process mining tool support. In: Proceedings of Petri Nets 05, pp. 444–454. Springer, Miami (2005)
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, 1st edn. Springer, New York (2011). ISBN:3642193447, 9783642193446
van der Aalst, W., Weijters, T., Maruster, L.: Workow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142. ISSN:1041-4347 (2004)
Casati, F., et al.: A generic solution for warehousing business process data. In: Proceedings of Very Large Data Bases 07. Vienna, Austria: VLDB Endowment, pp. 1128–1137 (2007)
van der Aalst, W.M.P., et al.: Process mining manifesto. In: Business Process Management Workshops ’11, pp. 169–194 (2011)
Pospiech, S., et al.: Exploration and analysis of undocumented processes using heterogeneous and unstructured business data. In: Proceedings of IEEE ICSC 2014, pp. 191–198 (2014)
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
Pospiech, S., Mertens, R., Mielke, S., Städler, M., Söhlke, P. (2016). Creating Event Logs from Heterogeneous, Unstructured Business Data. In: Piazolo, F., Felderer, M. (eds) Multidimensional Views on Enterprise Information Systems. Lecture Notes in Information Systems and Organisation, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-27043-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-27043-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27041-8
Online ISBN: 978-3-319-27043-2
eBook Packages: Business and ManagementBusiness and Management (R0)