Abstract
Today’s enterprise applications span multiple systems and organizations, integrating legacy and newly developed software components to deliver value to business operations. Often business processes rely on human activities that may not be predicted in advance, and information exchange is heavily based on e-mails or attachments where the content is unstructured and needs discovery. Visibility of such end-to-end operations is required to manage compliance and business performance. Hence, it becomes necessary to develop techniques for tracking and correlating the relevant aspects of business operations as needed without the cost and overhead of a fully fledged data and process reengineering. Our business provenance solution provides a generic data model and middleware infrastructure to collect and correlate information about how data was produced, what resources were involved and which tasks were executed. Business provenance gives the flexibility to selectively capture information required to address a specific compliance or performance goal. Additionally, a powerful correlation mechanism yields a representation of the end-to-end operation that puts each business artifact into the right context, for example, to detect situations of compliance violations and find their root causes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Greengard, S.: Compliance Software’s Bonus Benefits. Business Finance Magazine (February 2004)
Gartner, Simplifying Compliance: Best Practices and Technology, French Caldwell, 6/6/05 (Business Process Management Summit 2005)
Freire, J., Koop, D., Santos, E., Silva, C.T.: Provenance for Computational Tasks: A Survey. IEEE Computing is Science & Engineering 10(3), 11–21 (2008)
Simmhan, Y.L., Plale, B., Gannon, D.: A Survey of Data Provenance in E-Science. SIGMOD Record 34(3), 31–36 (2005)
Bose, R., Frew, J.: Lineage Retrieval for Scientific Data Processing: A Survey. ACM Computing Surveys 37(1), 1–28 (2005)
Miers, D., Harmon, P., Hall, C.: The 2007 BPM Suites Report, http://www.bptrends.com/reports_toc_01.cfm
Hammer, M.: Reengineering Work: Don’t automate, obliterate, Harvard Business Review., 104–112 (July/August 1990)
Harvey, M.: Essential Business Process Modelling ISBN 0-596-00843-0
Aalst, W., van der Weijters, A., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)
Moreau, L., Freire, J., Futrelle, J., McGrath, R., Myers, J., Paulson, P.: The Open Provenance Model Technical Report, Provenance in Scientific Computing (2007), http://eprints.ecs.soton.ac.uk/14979/
Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-Science. ACM SIGMOD Record 34(3) (September 2005)
Lord, P., Alper, P., Wroe, C., Stevens, R., Goble, C., Zhao, J., Hull, D., Greenwood, M.: The Semantic Web: Service discovery and provenance in my-Grid. In: W3C Workshop on Semantic Web for Life Sciences (2004)
Second Provenance Challenge Workshop (SPC) in High-Performance Distributed Computing (2007), http://twiki.ipaw.info/bin/view/Challenge
Jonas, J.: Threat and Fraud Intelligence, Las Vegas Style, Security & Privacy, vol. 4(6), pp. 28–34. IEEE, Los Alamitos (2006), http://jeffjonas.typepad.com/IEEE.Identity.Resolution.pdf
Foster, I., Vöckler, J., Wilde, M., Zhao, Y.: Chimera: A Virtual Data System For Representing, Querying, and Automating Data Derivation. In: Conference on Scientific and Statistical Database Management (2002)
Scheer, A.-W., Nüttgens, M.: ARIS Architecture and Reference Models for Business Process Management Institut für Wirtschaftsinformatik, Universität des Saarlandes, Im Stadtwald Geb. 14.1, D-66123 Saarbrücken
Rozinat, A., van der Aalst, W.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)
Fu, S.S., Chieu, T.C., Yih, J.-S., Kumaran, S.: An Intelligent Event Adaptation Mechanism for Business Performance Monitoring. In: IEEE International Conference on e-Business Engineering (ICEBE 2005), pp. 558–563 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Curbera, F., Doganata, Y., Martens, A., Mukhi, N.K., Slominski, A. (2008). Business Provenance – A Technology to Increase Traceability of End-to-End Operations. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems: OTM 2008. OTM 2008. Lecture Notes in Computer Science, vol 5331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88871-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-88871-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88870-3
Online ISBN: 978-3-540-88871-0
eBook Packages: Computer ScienceComputer Science (R0)