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MantaRay-ProM: An efficient process model discovery algorithm

AI COMMUNICATIONS 2024年第4期37卷 505-524页

作者： Gupta, Shikha Deshmukh, Sonia Kumar, Naveen Univ Delhi Shaheed Sukhdev Coll Business Studies New Delhi India KIET Inst Technol Dept Comp Sci & Informat Technol Kanpur UP India Univ Delhi Dept Comp Sci New Delhi India

Discovering the business process model from an organisation's records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.

关键词： Manta ray foraging optimization process model discovery bio-inspired optimization process mining event log

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AS-SIM: An Approach to Action-State process model discovery 26th

AS-SIM: An Approach to Action-State Process Model Discovery

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26th International Symposium on Methodologies for Intelligent Systems (ISMIS)

作者： Bottrighi, Alessio Guazzone, Marco Leonardi, Giorgio Montani, Stefania Striani, Manuel Terenziani, Paolo Univ Piemonte Orientale DISIT Alessandria Italy Univ Torino Dipartimento Informat Corso Svizzera 185 I-10149 Turin Italy

ISBN: (纸本)9783031165641;9783031165634

process model discovery has gained a lot of attention in recent years, to mine a process model from traces of process executions. In our recent work, we have proposed SIM (Semantic Interactive Miner), an innovative process mining tool able to discover the process model in an incremental way: first, a mining module builds an initial process model, called log-tree, from the available traces;then, such a model is refined interactively with domain experts, through merge and abstraction operations. However, in several contexts, traces are richer: they do not record only actions, but also states (i.e., values of parameters possibly affected by the actions). A typical example is the medical domain, where traces contain both actions and measurements of patients' parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach aiming at discovering a comprehensive model, in which two distinct classes of nodes are considered, to capture both actions and states. We focus on the definition and on the discovery of the initial action-state process model (called action-state log-tree), while in our future work we will extend SIM's merge and abstraction operations accordingly.

关键词： process mining process model discovery Mining action plus state evolution

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Towards Action-State process model discovery

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DATA 2023年第8期8卷

作者： Bottrighi, Alessio Guazzone, Marco Leonardi, Giorgio Montani, Stefania Striani, Manuel Terenziani, Paolo Univ Piemonte Orientale Dept Sci Technol & Innovat Viale Teresa Michel 11 I-15121 Alessandria Italy Azienda Osped SS Antonio & Biagio & Cesare Arrigo Lab Integrato Intelligenza Artificiale & Informat I-15121 Alessandria Italy Univ Piemonte Orientale DISIT I-15121 Alessandria Italy Consorzio Nazl Interuniv Telecomunicaz CNIT I-43124 Parma Italy

process model discovery covers the different methodologies used to mine a process model from traces of process executions, and it has an important role in artificial intelligence research. Current approaches in this area, with a few exceptions, focus on determining a model of the flow of actions only. However, in several contexts, (i) restricting the attention to actions is quite limiting, since the effects of such actions also have to be analyzed, and (ii) traces provide additional pieces of information in the form of states (i.e., values of parameters possibly affected by the actions);for instance, in several medical domains, the traces include both actions and measurements of patient parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach able to mine a process model that comprehends two distinct classes of nodes, to capture both actions and states.

关键词： process mining process model discovery mining action-state evolution

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Multi-Objective Optimization for Automated Business process discovery 11

Multi-Objective Optimization for Automated Business Process ...

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11th International Joint Conference on Knowledge discovery, Knowledge Engineering and Knowledge Management (IC3K) / 11th International Conference on Knowledge discovery and Information Retrieval (KDIR)

作者： Ghazal, Mohamed A. Ghoniemy, Samy Salama, Mostafa A. British Univ Egypt Dept Comp Sci Cairo Egypt

ISBN: (纸本)9789897583827

process Mining is a research field that aims to develop new techniques to discover, monitor and improve real processes by extracting knowledge from event logs. This relatively young research discipline has evidenced efficacy in various applications, especially in application domains where a dynamic behavior needs to be related to process models. process model discovery is presumably the most important task in process Mining since the discovered models can be used as an objective starting points for any further process analysis to be conducted. There are various quality dimensions the model should consider during discovery such as Replay-Fitness, Precision, Generalization, and Simplicity. It becomes evident that process model discovery, with its current given settings, is a Multi-Objective Optimization Problem. However, most existing techniques does not approach the problem as a Multi-Objective Optimization Problem. Therefore, in this work we propose the use of one of the most robust and widely used Multi-Objective Optimizers in process model discovery, the NSGA-II algorithm. Experimental results on a real life event log shows that the proposed technique outperforms existing techniques in various aspects. Also this work tries to establish a benchmarking system for comparing results of Multi-Objective Optimization based process model discovery techniques.

关键词： Multi-Objective Optimization process Mining Multi-Objective Evolutionary Algorithms process model discovery Non-dominated Sorting Genetic Algorithm II

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Compound Trace Clustering to Generate Accurate and Simple Sub-process models 15th

Compound Trace Clustering to Generate Accurate and Simple Su...

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15th International Conference on Service-Oriented Computing (ICSOC)

作者： Sun, Yaguang Bauer, Bernhard Weidlich, Matthias Univ Augsburg Software Methodol Distributed Syst Augsburg Germany Humboldt Univ Berlin Germany

ISBN: (纸本)9783319690346;9783319690353

Business process model discovery targets the construction of conceptual models from event data that has been recorded during the execution of a business process. While a plethora of discovery techniques have been proposed in the literature, most existing techniques fail to cope with complex control-flow patterns as they are observed in event logs of highly flexible processes. In this paper, we follow the idea of splitting-up an event log into sub-logs, before applying process model discovery. This yields a set of sub-process models, one per sub-log, each describing a major variant of the business process. Unlike existing techniques, our clustering approach is guided by the result of model discovery: It first optimises the average complexity of the resulting models, before improving the accuracy of each model in isolation. Our experimental evaluation highlights that our approach yields more accurate sub-process models (that are of comparatively low complexity) than state-of-the-art trace clustering techniques.

关键词： Business process mining process model discovery Trace clustering model fitness improvement model complexity reduction

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Parallel Flexible Heuristic Miner for process discovery

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SN Computer Science 2023年第5期4卷 1-10页

作者： Sahu, Muktikanta Lunia, Palak Mohanty, Sachi Nandan Department of Computer Science and Engineering IIIT Bhubaneswar Gothapatna Odisha Bhubaneswar 751003 India School of Computer Science and Engineering VIT-AP University Beside AP Secretariat Amaravati Andhra Pradesh Amaravati 522237 India

process discovery aims to construct a business process model by extracting valuable information from event logs. To extract process models from event logs, it is essential to have a stable and scalable implementation of the computationally demanding process discovery techniques through parallel computing. This is because the amount of data contained in event logs is growing at an exponential rate. The current study bases its computing strategy on this subject and suggests using the OpenMP Application Programming Interface (API) to construct the Flexible Heuristic Miner (FHM) algorithm for process discovery. The performance of the suggested approach is evaluated based on the speedup factor through a number of different experiments. This was accomplished by conducting an in-depth analysis of the steps that are involved in the FHM algorithm. As a result of this analysis, a suitable parallel programming framework has been proposed to reduce the execution time by making use of data and task parallelism. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： FHM OpenMP process Mining process model discovery Speedup

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Analysis on Online Learning Environment using process Mining Technique for Personal Knowledge Management Mapping 6

Analysis on Online Learning Environment using Process Mining...

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6th International Conference on Research and Innovation in Information Systems (ICRIIS)

作者： Ismail, Shahrinaz Tumin, Faes Univ Kuala Lumpur Malaysian Inst Informat Technol Proc Min Res Cluster ProMineRCE Kuala Lumpur Malaysia

ISBN: (纸本)9781728167268

As proven in recent research on personal knowledge management (PKM) processes in online learning environment, this study took the initiative to prove that these processes happen in the said platform by reducing the biasness in respondents' feedbacks. This paper proposes the usage of process mining technique to discover the PKM model in real online learning platform, by analysing the event logs data retrieved from two-semester duration of academic calendar. The focus of this paper is on how the online activities being mapped to the PKM processes, in which the GUSC model has been used as the appropriate model to represent PKM. GUSC model is based on the common processes of get knowledge, understand knowledge, share knowledge and connect to knowledge source, as proven to be construed at granular level in intelligent multi-agent system. Since there was a gap between the unconfirmed reality of the GUSC processes in online platform and the construed GUSC processes at intelligent software level, this study significantly contributes to the closure of this gap with its results and findings. It is discovered that PKM model does exist in online learning environment, with the right mapping of the online activities to the GUSC processes.

关键词： process mining process model discovery personal knowledge management online learning environment

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Discovering models of Parallel Workflow processes from Incomplete Event Logs 3

Discovering Models of Parallel Workflow Processes from Incom...

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3rd International Conference on model-Driven Engineering and Software Development (modelSWARD)

作者： Lekic, Julijana Milicev, Dragan Univ Pristina Kosovska Mitrov Fac Tech Sci Kneza Milosa 7 Kosovska Mitrovica 38220 Serbia Univ Belgrade Fac Elect Engn Beograd 11120 Serbia

ISBN: (纸本)9789897581366

alpha-algorithm is able to discover a large class of workflow (WF) nets based on the behavior recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of business process models discovering based on examples of traces, i.e., logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modified the existing and introduced a new relation between activities recorded in the event log, which has led to a partial correction of the process models discovering techniques, including the alpha-algorithm. We have also introduced the notions of causally and weakly complete logs, from which our modified algorithm can produce the same result as the original algorithm from complete logs. The effect of these modifications on the speed of the process model discovering is mostly evident for business processes in which many activities can be performed in parallel. Therefore, this paper presents preliminary results obtained from the investigation of opportunities to discover models of parallel processes based on incomplete event logs.

关键词： process Mining process model discovery Parallel Business processes Incomplete Event Log alpha-algorithm

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