2,731 research outputs found

    Discovering duplicate tasks in transition systems for the simplification of process models

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    This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft

    Learning process models in IoT Edge

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    The alignment of formal, structured and unstructured process descriptions

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    Nowadays organizations are experimenting a drift on the way processes are managed. On the one hand, formal notations like Petri nets or Business Process Model and Notation (BPMN) enable the unambiguous reasoning and automation of designed processes. This way of eliciting processes by manual design, which stemmed decades ago, will still be an important actor in the future. On the other hand, regulations require organizations to store their process executions in structured representations, so that they are known and can be analyzed. Finally, due to the different nature of stakeholders within an organization (ranging from the most technical members, e.g., developers, to less technical), textual descriptions of processes are also maintained to enable that everyone in the organization understands their processes. In this paper I will describe techniques for facilitating the interconnection between these three process representations. This requires interdisciplinary research to connect several fields: business process management, formal methods, natural language processing and process mining.Peer ReviewedPostprint (author's final draft

    Behavioral-based algorithms for process model simplification

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    The analysis of processes, either by Business Process Management (BPM) or Process Mining (PM) techniques, has become a must for every organization in order to improve their performance. The role of the process model, a diagrammatic representation of the process, is crucial in most of the BPM and PM phases. During past years, the amount of process-related data that has been gathered by information systems has greatly increased. With more information and behavior related to the processes being recorded, the apparition of complex process models ---with hundreds of edges and activities--- has become more common, hindering the analysis of the process during most stages of BPM and PM. For this reason, the simplification of complex process models is a promising research field that can help the analysis of complex processes
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