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632 results on '"process discovery"'

1. An Approach for Discovering Data-Driven Object Lifecycle Processes

Author

Breitmayer, Marius, Arnold, Lisa, Goth, David, Reichert, Manfred, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Araújo, João, editor, de la Vara, Jose Luis, editor, Santos, Maribel Yasmina, editor, and Assar, Saïd, editor

Published
2024
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2. Imposing Rules in Process Discovery: An Inductive Mining Approach

Author

Norouzifar, Ali, Dees, Marcus, van der Aalst, Wil, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Araújo, João, editor, de la Vara, Jose Luis, editor, Santos, Maribel Yasmina, editor, and Assar, Saïd, editor

Published
2024
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7. Unveiling AI Efficiency: Loan Application Process Optimization Using PM4PY Tool

Author

Tripathi, Anukriti, Rai, Aditi, Singh, Uphar, Vyas, Ranjana, Vyas, O. P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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8. Phenotypes vs Processes: Understanding the Progression of Complications in Type 2 Diabetes. A Case Study

Author

Tornero-Costa, Roberto, Martinez-Millana, Antonio, Merino-Torres, Juan-Francisco, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Juarez, Jose M., editor, Fernandez-Llatas, Carlos, editor, Bielza, Concha, editor, Johnson, Owen, editor, Kocbek, Primoz, editor, Larrañaga, Pedro, editor, Martin, Niels, editor, Munoz-Gama, Jorge, editor, Štiglic, Gregor, editor, Sepulveda, Marcos, editor, and Vellido, Alfredo, editor

Published
2024
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9. Declarative Process Mining for Software Processes: The RuM Toolkit and the Declare4Py Python Library

Author

Alman, Anti, Donadello, Ivan, Maggi, Fabrizio Maria, Montali, Marco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kadgien, Regine, editor, Jedlitschka, Andreas, editor, Janes, Andrea, editor, Lenarduzzi, Valentina, editor, and Li, Xiaozhou, editor

Published
2024
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10. Discovering Process Models with Long-Term Dependencies while Providing Guarantees and Filtering Infrequent Behavior Patterns.

Author

Mannel, Lisa L. and van der Aalst, Wil M. P.

Subjects
*FLEXIBLE manufacturing systems, *PETRI nets, *ALGORITHMS, *NOISE
Abstract

In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language and are therefore often used. In this paper, we extend the eST-Miner process discovery algorithm. The eST-Miner computes a set of Petri net places which are considered to be fitting with respect to a certain fraction of the behavior described by the given event log as indicated by a given noise threshold. It evaluates all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., these sets do not need to be consistent. This allows the algorithm to abstract from infrequent behavioral patterns occurring only in some traces. However, when combining places into a Petri net by connecting them to the corresponding uniquely labeled transitions, the resulting net can replay exactly those traces from the event log that are allowed by the combination of all inserted places. Thus, inserting places one-by-one without considering their combined effect may result in deadlocks and low fitness of the Petri net. In this paper, we explore adaptions of the eST-Miner, that aim to select a subset of places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Furthermore, current place evaluation techniques tend to block the execution of infrequent activity labels. Thus, a refined place fitness metric is introduced and thoroughly investigated. In our experiments we use real and artificial event logs to evaluate and compare the impact of the various place selection strategies and place fitness evaluation metrics on the returned Petri net. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Automated modelling of discrete-event processes. Discovering Petri nets including silent transitions by classifying event traces.

Author

Álvarez-Pérez, Yolanda and López-Mellado, Ernesto

Abstract

This paper addresses the problem of automated modelling of discrete-event processes from event traces that capture the process behaviour. A novel method for building Petri nets (PN) that include silent transitions is presented. The method discovers workflow nets (WFN), a class of PN, from logs of event traces λ. The proposed approach is based on a classification of traces such that λ is partitioned into four classes: normal traces (λN), which do not involve the firing of silent transitions; skip traces (λS), redo traces (λR), and switch traces (λSW), which involve the firing of skip, redo, and switch type silent transitions, respectively. Afterwards, traces in λN are used to build a base WFN N, which may include silent transitions of type initialise and finalise. Then, N is refined by adding the silent transitions corresponding to the classes oftraces λS, λR, and λSW. The algorithms of the method have polynomial-time complexity; implementation and tests using artificial event logs are presented. [ABSTRACT FROM AUTHOR]

Published
2023
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12. Object-Centric Process Mining: An Introduction

Author

van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Cerone, Antonio, editor

Published
2023
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13. An SQL-Based Declarative Process Mining Framework for Analyzing Process Data Stored in Relational Databases

Author

Riva, Francesco, Benvenuti, Dario, Maggi, Fabrizio Maria, Marrella, Andrea, Montali, Marco, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Di Francescomarino, Chiara, editor, Burattin, Andrea, editor, Janiesch, Christian, editor, and Sadiq, Shazia, editor

Published
2023
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14. Optimizing the Solution Quality of Metaheuristics Through Process Mining Based on Selected Problems from Operations Research

Author

Kinast, Alexander, Braune, Roland, Doerner, Karl F., Rinderle-Ma, Stefanie, van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, Di Francescomarino, Chiara, editor, Burattin, Andrea, editor, Janiesch, Christian, editor, and Sadiq, Shazia, editor

Published
2023
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15. Context-Based Activity Label-Splitting

Author

van Zelst, Sebastiaan J., Tai, Jonas, Langenberg, Moritz, Lu, Xixi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Di Francescomarino, Chiara, editor, Burattin, Andrea, editor, Janiesch, Christian, editor, and Sadiq, Shazia, editor

Published
2023
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16. Incremental Discovery of Process Models Using Trace Fragments

Author

Schuster, Daniel, Föcking, Niklas, van Zelst, Sebastiaan J., van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Di Francescomarino, Chiara, editor, Burattin, Andrea, editor, Janiesch, Christian, editor, and Sadiq, Shazia, editor

Published
2023
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17. Trace Clustering Based on Activity Profile for Process Discovery in Education

Author

Hachicha, Wiem, Ghorbel, Leila, Champagnat, Ronan, Zayani, Corinne Amel, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Pllana, Sabri, editor, Casalino, Gabriella, editor, Ma, Kun, editor, and Bajaj, Anu, editor

Published
2023
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18. Unblocking Inductive Miner : While Preserving Desirable Properties

Author

Huang, Tsung-Hao, van der Aalst, Wil M. P., van der Aalst, Wil, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Guizzardi, Giancarlo, Series Editor, van der Aa, Han, editor, Bork, Dominik, editor, Proper, Henderik A., editor, and Schmidt, Rainer, editor

Published
2023
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20. Comparing Ordering Strategies for Process Discovery Using Synthesis Rules

Author

Huang, Tsung-Hao, van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Troya, Javier, editor, Mirandola, Raffaela, editor, Navarro, Elena, editor, Delgado, Andrea, editor, Segura, Sergio, editor, Ortiz, Guadalupe, editor, Pautasso, Cesare, editor, Zirpins, Christian, editor, Fernández, Pablo, editor, and Ruiz-Cortés, Antonio, editor

Published
2023
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21. Improving Declarative Process Mining with a Priori Noise Filtering

Author

Christfort, Axel Kjeld Fjelrad, Debois, Søren, Slaats, Tijs, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Cabanillas, Cristina, editor, Garmann-Johnsen, Niels Frederik, editor, and Koschmider, Agnes, editor

Published
2023
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23. Statistical Model Checking in Process Mining: A Comprehensive Approach to Analyse Stochastic Processes.

Author

Mangi, Fawad Ali, Su, Guoxin, and Zhang, Minjie

Subjects
PROCESS mining, STOCHASTIC processes, STATISTICAL models, STOCHASTIC systems, STOCHASTIC models
Abstract

The study of business process analysis and optimisation has attracted significant scholarly interest in the recent past, due to its integral role in boosting organisational performance. A specific area of focus within this broader research field is process mining (PM). Its purpose is to extract knowledge and insights from event logs maintained by information systems, thereby discovering process models and identifying process-related issues. On the other hand, statistical model checking (SMC) is a verification technique used to analyse and validate properties of stochastic systems that employs statistical methods and random sampling to estimate the likelihood of a property being satisfied. In a seamless business setting, it is essential to validate and verify process models. The objective of this paper is to apply the SMC technique in process mining for the verification and validation of process models with stochastic behaviour and large state space, where probabilistic model checking is not feasible. We propose a novel methodology in this research direction that integrates SMC and PM by formally modelling discovered and replayed process models and apply statistical methods to estimate the results. The methodology facilitates an automated and proficient evaluation of the extent to which a process model aligns with user requirements and assists in selecting the optimal model. We demonstrate the effectiveness of our methodology with a case study of a loan application process performed in a financial institution that deals with loan applications submitted by customers. The case study highlights our methodology's capability to identify the performance constraints of various process models and aid enhancement efforts. [ABSTRACT FROM AUTHOR]

Published
2023
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24. An automated materials and processes identification tool for material informatics using deep learning approach

Author

M. Saef Ullah Miah, Junaida Sulaiman, Talha Bin Sarwar, Nur Ibrahim, Md Masuduzzaman, and Rajan Jose

Subjects
Materials discovery, Process discovery, Materials 4.0, Material informatics, Entity-value extraction, Knowledge graph, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

This article reports a tool that enables Materials Informatics, termed as MatRec, via a deep learning approach. The tool captures data, makes appropriate domain suggestions, extracts various entities such as materials and processes, and helps to establish entity-value relationships. This tool uses keyword extraction, a document similarity index to suggest relevant documents, and a deep learning approach employing Bi-LSTM for entity extraction. For example, materials and processes for electrical charge storage under an electric double layer capacitor (EDLC) mechanism are demonstrated herewith. A knowledge graph approach finds and visualizes different latent knowledge sets from the processed information. The MatRec received an F1 score of 9̃6% for entity extraction, 8̃3% for material-value relationship extraction, and 8̃7% for process-value relationship extraction, respectively. The proposed MatRec could be extended to solve material selection issues for various applications and could be an excellent tool for academia and industry.

Published
2023
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25. Object-Centric Process Mining: Unraveling the Fabric of Real Processes.

Author

van der Aalst, Wil M. P.

Subjects
*PROCESS mining, *BUSINESS process management, *INSURANCE claims
Abstract

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a "three-dimensional" view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis

Author

Erica Tavazzi, Roberto Gatta, Mauro Vallati, Stefano Cotti Piccinelli, Massimiliano Filosto, Alessandro Padovani, Maurizio Castellano, and Barbara Di Camillo

Subjects
Amyotrophic lateral sclerosis, Progression trajectories, Process mining, Process discovery, Patient stratification, Prognosis, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients’ quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients’ characteristics. Methods We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS–BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients’ characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS–BS. Results We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS–BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria. Conclusions We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories.

Published
2023
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27. Discovery of effective infrequent sequences based on maximum probability path

Author

Ke Lu, Xianwen Fang, Na Fang, and Esther Asare

Subjects
effective infrequent sequences, noise activity, maximum probability path, conditional probability entropy, state transition matrix, process discovery, Electronic computers. Computer science, QA75.5-76.95
Abstract

Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong transfer relationships between behaviours based on maximum probability paths. The algorithm divides episodic traces into two categories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activities that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state transfer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the model and reduces noise activity, but also improves the quality of the model.

Published
2022
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28. Using Process Mining Techniques to Discover the Collective Behaviour of Educators in a Learning Community Platform

Author

Esteban Bajo, Oscar, Amarasinghe, Ishari, Gutiérrez-Páez, Nicolás Felipe, Hernández-Leo, Davinia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wong, Lung-Hsiang, editor, Hayashi, Yugo, editor, Collazos, Cesar A., editor, Alvarez, Claudio, editor, Zurita, Gustavo, editor, and Baloian, Nelson, editor

Published
2022
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29. Financial Process Mining Characteristics

Author

Lopata, Audrius, Butleris, Rimantas, Gudas, Saulius, Rudžionienė, Kristina, Žioba, Liutauras, Veitaitė, Ilona, Dilijonas, Darius, Grišius, Evaldas, Zwitserloot, Maarten, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lopata, Audrius, editor, Gudonienė, Daina, editor, and Butkienė, Rita, editor

Published
2022
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30. Discovering Sound Free-Choice Workflow Nets with Non-block Structures

Author

Huang, Tsung-Hao, van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Almeida, João Paulo A., editor, Karastoyanova, Dimka, editor, Guizzardi, Giancarlo, editor, Montali, Marco, editor, Maggi, Fabrizio Maria, editor, and Fonseca, Claudenir M., editor

Published
2022
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31. Enabling Multi-process Discovery on Graph Databases

Author

Nour Eldin, Ali, Assy, Nour, Kobeissi, Meriana, Baudot, Jonathan, Gaaloul, Walid, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sellami, Mohamed, editor, Ceravolo, Paolo, editor, Reijers, Hajo A., editor, Gaaloul, Walid, editor, and Panetto, Hervé, editor

Published
2022
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32. Automating Process Discovery Through Meta-learning

Author

Tavares, Gabriel Marques, Junior, Sylvio Barbon, Damiani, Ernesto, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sellami, Mohamed, editor, Ceravolo, Paolo, editor, Reijers, Hajo A., editor, Gaaloul, Walid, editor, and Panetto, Hervé, editor

Published
2022
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33. Analyzing Variable Human Actions for Robotic Process Automation

Author

Martínez-Rojas, A., Jiménez-Ramírez, A., Enríquez, J. G., Reijers, H. A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Di Ciccio, Claudio, editor, Dijkman, Remco, editor, del Río Ortega, Adela, editor, and Rinderle-Ma, Stefanie, editor

Published
2022
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34. Alpha Precision: Estimating the Significant System Behavior in a Model

Author

Depaire, Benoît, Janssenswillen, Gert, Leemans, Sander J. J., van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Di Ciccio, Claudio, editor, Dijkman, Remco, editor, del Río Ortega, Adela, editor, and Rinderle-Ma, Stefanie, editor

Published
2022
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35. Process Discovery Analysis for Generating RPA Flowcharts

Author

Rybinski, Fabian, Schüler, Selina, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Marrella, Andrea, editor, Matulevičius, Raimundas, editor, Gabryelczyk, Renata, editor, Axmann, Bernhard, editor, Bosilj Vukšić, Vesna, editor, Gaaloul, Walid, editor, Indihar Štemberger, Mojca, editor, Kő, Andrea, editor, and Lu, Qinghua, editor

Published
2022
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36. Discovering Directly-Follows Complete Petri Nets from Event Data

Author

van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jansen, Nils, editor, Stoelinga, Mariëlle, editor, and van den Bos, Petra, editor

Published
2022
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37. Optimising Business Process Discovery Using Answer Set Programming

Author

Chesani, Federico, Di Francescomarino, Chiara, Ghidini, Chiara, Grundler, Giulia, Loreti, Daniela, Maggi, Fabrizio Maria, Mello, Paola, Montali, Marco, Tessaris, Sergio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gottlob, Georg, editor, Inclezan, Daniela, editor, and Maratea, Marco, editor

Published
2022
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38. Process Mining: A 360 Degree Overview

Author

van der Aalst, Wil M. P., van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, van der Aalst, Wil M. P., editor, and Carmona, Josep, editor

Published
2022
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39. Foundations of Process Discovery

Author

van der Aalst, Wil M. P., van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, van der Aalst, Wil M. P., editor, and Carmona, Josep, editor

Published
2022
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40. From Place Nets to Local Process Models

Author

Peeva, Viki, Mannel, Lisa L., van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bernardinello, Luca, editor, and Petrucci, Laure, editor

Published
2022
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42. OC: Object-Centric Process Insights

Author

Adams, Jan Niklas, van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bernardinello, Luca, editor, and Petrucci, Laure, editor

Published
2022
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43. Process Discovery Algorithms Recommendation Approach

Author

R’bigui, Hind, Al-Absi, Mohammed Abdulhakim, Cho, Chiwoon, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Pattnaik, Prasant Kumar, editor, Sain, Mangal, editor, and Al-Absi, Ahmed A., editor

Published
2022
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44. Distributed Synthesis of Asynchronously Communicating Distributed Process Models

Author

Kwantes, Pieter, Kleijn, Jetty, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Koutny, Maciej, editor, Kordon, Fabrice, editor, and Moldt, Daniel, editor

Published
2022
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45. A Generic Trace Ordering Framework for Incremental Process Discovery

Author

Schuster, Daniel, Domnitsch, Emanuel, van Zelst, Sebastiaan J., van der Aalst, Wil M. P., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bouadi, Tassadit, editor, Fromont, Elisa, editor, and Hüllermeier, Eyke, editor

Published
2022
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47. Process Logo: An Approach for Control-Flow Visualization of Information System Process in Process Mining

Author

Manoj Kumar, M. V., Prashanth, B. S., Sneha, H. R., Thomas, Likewin, Annappa, B., Murthy, Y. V. S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Shetty, N. R., editor, Patnaik, L. M., editor, Nagaraj, H. C., editor, Hamsavath, Prasad N., editor, and Nalini, N., editor

Published
2022
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48. Comparative Analysis of Process Mining Algorithms in Process Discover

Author

Gomes, André Filipe Domingos, de Lacerda, Ana Cristina Wanzeller Guedes, da Silva Fialho, Joana Rita, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, de Paz Santana, Juan F., editor, de la Iglesia, Daniel H., editor, and López Rivero, Alfonso José, editor

Published
2022
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50. SAMPLING SIMULATION IN PROCESS DISCOVERY.

Author

H. N., Prasetyo, R., Sarno, D. R., Wijaya, R., Budiraharjo, and I., Waspada

Subjects
*SAMPLING (Process), *SAMPLING errors, *CLUSTER sampling, *STATISTICAL sampling, *SAMPLE size (Statistics), *BIG data, *SAMPLING methods
Abstract

Process model resulting from small event log datasets can be easily done because currently available applications are relatively able to do so. However, when faced with event logs from big data, modelling will force the existing applications to work hard. So far, the steps used are sampling of event logs resulting from the system. The problem arising is that the sampling process must be done several times because it has to check the desired fitness value on the sample taken. If the fitness value has not been got, then the sample size is added and the fitness value at a certain iteration is calculated until the required fitness value is met. There are many steps to do with this mechanism. Thus, this paper proposes an alternative way to reduce the steps by using an appropriate sampling technique. The mechanism used is statistical-based sampling simulation in the event log datasets to determine which sampling method is stable in the process modelling. The simulation results show that the sampling method using Cluster Random Sampling with the error rate or Alpha of 1 % has a relatively stable process model and can represent the process model resulting from the event log population. [ABSTRACT FROM AUTHOR]

Published
2023
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