A Decidable Logic for Describing Linked Data Structures
ESOP '99 Proceedings of the 8th European Symposium on Programming Languages and Systems
Reachability and connectivity queries in constraint databases
Journal of Computer and System Sciences - Special issue on PODS 2000
Provenance and scientific workflows: challenges and opportunities
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Auditing Business Process Compliance
ICSOC '07 Proceedings of the 5th international conference on Service-Oriented Computing
Efficient Compliance Checking Using BPMN-Q and Temporal Logic
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Business Provenance --- A Technology to Increase Traceability of End-to-End Operations
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
The Open Provenance Model: An Overview
Provenance and Annotation of Data and Processes
Semantics and complexity of SPARQL
ACM Transactions on Database Systems (TODS)
Effect of Using Automated Auditing Tools on Detecting Compliance Failures in Unmanaged Processes
BPM '09 Proceedings of the 7th International Conference on Business Process Management
The VLDB Journal — The International Journal on Very Large Data Bases
ProM 4.0: comprehensive support for real process analysis
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Modeling control objectives for business process compliance
BPM'07 Proceedings of the 5th international conference on Business process management
Provenance collection support in the kepler scientific workflow system
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
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Unmanaged business processes are an inevitable part of the operation of enterprises of all kinds. At the same time, monitoring such processes and measuring compliance against business policies is particularly difficult due to the effort needed to manually piece together information. A major challenge here is the presumption of incomplete or missing information and the uncertainty in making inferences about what actually occured. This work proposes the use of a probabilistic provenance model to track the history of various process artifacts and reconstruct process traces. The major contribution of the work is the method of modeling the uncertainty associated with raw information as well as inferred relationships in a first class manner within the provenance model. We apply the techniques described here to a real world problem and compare results obtained in previous work where such uncertainties were ignored. The techniques described here are widely applicable to unmanaged business processes.