2,466 research outputs found
Enhancing process models to improve business performance:a methodology and case studies
No full text\u3cp\u3eProcess mining is not only about discovery and conformance checking of business processes. It is also focused on enhancing processes to improve the business performance. While from a business perspective this third main stream is definitely as important as the others if not even more, little research work has been conducted. The existing body of work on process enhancement mainly focuses on ensuring that the process model is adapted to incorporate behavior that is observed in reality. It is less focused on improving the performance of the process. This paper reports on a methodology that creates an enhanced model with an improved performance level. The enhancements of the model limit incorporated behavior to only those parts that do not violate any business rules. Finally, the enhanced model is kept as close to the original model as possible. The practical relevance and feasibility of the methodology is assessed through two case studies. The result shows that the process models improved through our methodology, in comparison with state-of the art techniques, have improved KPI levels while still adhering to the desired prescriptive model.\u3c/p\u3
Detection of batch activities from event logs
Full text linkOrganizations carry out a variety of business processes in order to serve their clients. Usually supported by information technology and systems, process execution data is logged in an event log. Process mining uses this event log to discover the process' control-flow, its performance, information about the resources, etc. A common assumption is that the cases are executed independently of each other. However, batch work-the collective execution of cases for specific activities-is a common phenomenon in operational processes to save costs or time. Existing research has mainly focused on discovering individual batch tasks. However, beyond this narrow setting, batch processing may consist of the execution of several linked tasks. In this work, we present a novel algorithm which can also detect parallel, sequential and concurrent batching over several connected tasks, i.e., subprocesses. The proposed algorithm is evaluated on synthetic logs generated by a business process simulator, as well as on a real-world log obtained from a hospital's digital whiteboard system. The evaluation shows that batch processing at the subprocess level can be reliably detected.We would like to thank Leon Bein (Master student at HassoPlattner Institute) for extending the simulator Scylla and forsupporting the generation of the syntactic event logs. We wouldalso like to sincerely thank the reviewers for their constructivefeedback during the review process.2
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No full textPhotograph of Felix Almaraz, Author and Teacher, "The Tragic Calvalier."
fmannhardt/pddp: Release v0.1 (BISE Paper)
No full text<p>This is the version on which the BISE paper is baed: <a href="https://doi.org/10.1007/s12599-019-00613-3">https://doi.org/10.1007/s12599-019-00613-3</a></p>
Trust and Privacy in Process Analytics
No full textThis paper summarizes the panel discussion at the 1st Workshop on Trust and Privacy in Process Analytics (TPPA) co-located with the 2nd International Conference on Process Mining. The panel discussed to what extend trust and privacy is embedded in applications of process mining and took place on 5th October 2020. The virtual session was chaired by Felix Mannhardt and Agnes Koschmider and the invited panelists were Moe Wynn, Jana Lange, Lars Biermann and Florian Tschorsch. The major challenges that this panel identified related to privacy-preserving process mining are to include (user-centric) privacy filters, understanding the privacy-utility trade-off and to link privacy-preserving techniques with dataset quality
Experience-Based Resource Allocation for Remaining Time Optimization
Full text linkPrescriptive process analytics aims to suggest interventions for those process instances that are predicted to not achieve a satisfactory outcome. Typical interventions are recommending a task to be performed by a specific resource. State-of-the-art prescriptive resource allocation techniques typically propose interventions that allocate the best-fitting resources at a given time. This may result in those resources to become more skilled at the task over time whereas other less experienced resource are rarely allocated. In the long run, such system may result in a unbalanced situation in which some expert resources are overloaded with very high workload and the less experienced resource are assigned fewer tasks and fail to improve. This paper proposes an approach for resource allocation to process instances that aims at a more balanced workload distribution among the resources, even if this means slightly lower process improvements in the short term. Experiments on event logs related to two real processes show that we indeed achieve a more balanced workload distribution, which often yields an overall higher improvement of the whole set of running process instances
Heuristic mining revamped : an interactive, data-aware, and conformance-aware miner
Full text linkProcess discovery methods automatically infer process models based on events logs that are recorded by information systems. Several heuristic process discovery methods have been proposed to cope with less structured processes and the presence of noise in the event log. However, (1) a large parameter space needs to be explored, (2) several of the many available heuristics can be chosen from, (3) data attributes are not used for discovery, (4) discovered models are not visualized as described in literature, and (5) existing tools do not give reliable quality diagnostics for discovered models. We present the interactive Data-aware Heuristics Miner (iDHM), a modular tool that attempts to address those five issues. The iDHM enables quick interactive exploration of the parameter space and several heuristics. It uses data attributes to improve the discovery procedure and provides built-in conformance checking to get direct feedback on the quality of the model. It is the first tool that visualizes models using the concise Causal Net (C-Net) notation. We provide a walk-through of the iDHM by applying it to a large event log with hospital billing information
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