@conference {257, title = {Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning}, booktitle = {28th International Conference on Automated Planning and Scheduling (ICAPS 2018)}, year = {2018}, abstract = {Conformance checking is the problem of verifying if the actual executions of business processes, which are recorded by information systems in dedicated event logs, are compliant with a process model that encodes the process{\textquoteright} constraints. Within conformance checking, alignment-based techniques can exactly pinpoint where deviations are observed. Existing alignment-based techniques rely on the assumption of a perfect knowledge of the order with which process{\textquoteright} activities were executed in reality. However, experience shows that, due to logging errors and inaccuracies, it is not always possible to determine the exact order with which certain activities were executed. This paper illustrates an alignment-based technique where the perfect knowledge assumption of the execution{\textquoteright}s order is removed. The technique transforms the problem of alignment-based conformance checking into a planning problem encoded in PDDL, for which planners can find a correct solution in a finite amount of time. We implemented the technique as a software tool that is integrated with state-of-the-art planners. To showcase its practical relevance and scalability, we report on experiments with a real-life case study and several synthetic ones of increasing complexity.}, keywords = {Automated Planning, Conformance Checking, PDDL, Process Mining, Trace Alignment}, url = {https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739/16951}, author = {Massimiliano de Leoni and Giacomo Lanciano and Andrea Marrella} } @article {258, title = {Models and Logs used in the paper {\textquoteleft}Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning{\textquoteright} accepted for ICAPS 2018}, year = {2018}, publisher = {TU Eindhoven}, keywords = {Computation Theory and Mathematics, Event Logs, Petri nets}, doi = {10.4121/UUID:A02AFEC8-B7C7-42B7-8DFF-36D3DE3032BE}, url = {https://data.4tu.nl/repository/uuid:a02afec8-b7c7-42b7-8dff-36d3de3032be}, author = {Giacomo Lanciano and Massimiliano de Leoni} } @conference {256, title = {A Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning}, booktitle = {Proceedings of the BPM Demo Track and BPM Dissertation Award co-located with 15th International Conference on Business Process Modeling (BPM 2017)}, year = {2017}, abstract = {In Conformance Checking, alignment is the problem of detecting and repairing nonconformity between the actual execution of a business process, as recorded in an event log, and the model of the same process. Literature proposes solutions for the alignment problem that are implementations of planning algorithms built ad-hoc for the specific problem. Unfortunately, in the era of big data, these ad-hoc implementations do not scale sufficiently compared with well-established planning systems. In this paper, we tackle the above issue by presenting a tool, also available in ProM, to represent instances of the alignment problem as automated planning problems in PDDL (Planning Domain Definition Language) for which state-of-the-art planners can find a correct solution in a finite amount of time. If alignment problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with advantages in term of versatility and customization. Furthermore, by employing several processes and event logs of different sizes, we show how our tool outperforms existing approaches of several order of magnitude and, in certain cases, carries out the task while existing approaches run out of memory.}, url = {http://ceur-ws.org/Vol-1920/BPM_2017_paper_187.pdf}, author = {Massimiliano de Leoni and Giacomo Lanciano and Andrea Marrella} }