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Improving the performance of process discovery algorithms by instance selection

Mohammadreza Sani, Zelst van, der van
2020 Computer Science and Information Systems  
Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes.  ...  This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms.  ...  Acknowledgments We would like to thank the Alexander von Humboldt (AvH) scholarship for supporting us to do this research.  ... 
doi:10.2298/csis200127028s fatcat:hkxmsv7bdfdfnj6lyb5legibnu

Data Mining for Process Modeling: A Clustered Process Discovery Approach

Renato Cirne, Caio Melquiades, Renan Leite, Eronita Leijden, Alexandre Maciel, Fernando Buarque de Lima Neto
2020 Proceedings of the 2020 Federated Conference on Computer Science and Information Systems  
Process mining has emerged as a new scientific research topic on the interface between process modeling and event data gathering.  ...  And as a result, the use of the clustering techniques coupled with process discovery showed significant gains in the generation of process models, unlike the standard approach.  ...  One of the main focuses of the study of process mining and the object of this study is process discovery, where, based on observed behavior, a process model capable of reproducing event logs is inferred  ... 
doi:10.15439/2020f95 dblp:conf/fedcsis/CirneMLLMN20 fatcat:4jijs5jyfjexhheuylcb2lxkpu

The impact of biased sampling of event logs on the performance of process discovery

Mohammadreza Fani Sani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
2021 Computing  
AbstractWith Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes.  ...  However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data.  ...  To view a copy of this licence, visit  ... 
doi:10.1007/s00607-021-00910-4 fatcat:gprk7efzazfgrkicnrhskrrguq

Using Meta-learning to Recommend Process Discovery Methods [article]

Sylvio Barbon Jr, Paolo Ceravolo, Ernesto Damiani, Gabriel Marques Tavares
2021 arXiv   pre-print
Our experimental analysis also provided significant insights on the importance of log features in generating recommendations, paving the way to a deeper understanding of the discovery algorithms.  ...  However, selecting the suitable method for a specific event log highly relies on human expertise, hindering its broad application.  ...  The authors would like to thank CNPq (National Council for the Scientific and Technological Development) for their financial support under Grant of Project 420562/2018-4 and 309863/2020-1 and the program  ... 
arXiv:2103.12874v1 fatcat:d5oo6pvjwff27prxgiub3i5vju

Towards Goal-Oriented Process Mining

Mahdi Ghasemi
2018 2018 IEEE 26th International Requirements Engineering Conference (RE)  
The growth of our digital world makes it possible to record many types of events. In particular, the number of business processes whose events are being logged is significantly increasing.  ...  Process mining is an approach that exploits event logs to discover real processes executed in organizations, enabling them to (re)design and improve process models.  ...  ACKNOWLEDGMENT This research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).  ... 
doi:10.1109/re.2018.00066 dblp:conf/re/Ghasemi18 fatcat:ppdrg5ionbazviw4utt6yfvoju

Scalable process discovery and conformance checking

Sander J. J. Leemans, Dirk Fahland, Wil M. P. van der Aalst
2016 Journal of Software and Systems Modeling  
We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes  ...  In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework.  ...  Evaluation To understand the impact of 'big data' event logs on process discovery and quality assessment, we conducted a series of experiments to answer the following research questions: RQ1 What is the  ... 
doi:10.1007/s10270-016-0545-x pmid:29706859 pmcid:PMC5910523 fatcat:23pxm6bspjazlbx2qd52ops7m4

Privacy-Preserving Process Mining in Healthcare

Anastasiia Pika, Moe T. Wynn, Stephanus Budiono, Arthur H.M. ter Hofstede, Wil M.P. van der Aalst, Hajo A. Reijers
2020 International Journal of Environmental Research and Public Health  
We also advocate the recording of privacy metadatato capture information about privacy-preserving transformations performed on an event log.  ...  We demonstratehow some of these anonymisation methods affect various process mining results using three publiclyavailable healthcare event logs.  ...  The article [11] evaluates the impact of the log transformation on the results of process discovery and performance analysis algorithms using three real-life logs including a hospital log.  ... 
doi:10.3390/ijerph17051612 pmid:32131516 pmcid:PMC7084661 fatcat:cicpwfjvorhezh5gofom7minke

Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Zeeshan Tariq, Naveed Khan, Darryl Charles, Sally McClean, Ian McChesney, Paul Taylor
2020 Algorithms  
For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the  ...  of the process are not considered while clustering the process log.  ...  Acknowledgments: This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.  ... 
doi:10.3390/a13100244 fatcat:trxnnwssl5bnxfmnvcm67txwby

An Integrated Framework for Process Discovery Algorithm Evaluation [article]

Toon Jouck, Alfredo Bolt, Benoît Depaire, Massimiliano de Leoni, Wil M.P. van der Aalst
2018 arXiv   pre-print
The growing amount of algorithms for process discovery has raised the question of which algorithms perform best on a given event log.  ...  It supports two main evaluation objectives: benchmarking process discovery algorithms and sensitivity analysis, i.e. studying the effect of model and log characteristics on a discovery algorithm's accuracy  ...  In the context of process discovery this implies that one needs to test a specific algorithm on more than one event log to accurately assess the effect of that algorithm on model quality.  ... 
arXiv:1806.07222v1 fatcat:ckkbh7wxtvbzlaqnefo7ke6lju

Event Log Preprocessing for Process Mining: A Review

Heidy M. Marin-Castro, Edgar Tello-Leal
2021 Applied Sciences  
The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks.  ...  Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app112210556 fatcat:lls2qf6llnddxbdnego2okk2ya

Discovering Duplicate Tasks in Transition Systems for the Simplification of Process Models [chapter]

Javier de San Pedro, Jordi Cortadella
2016 Lecture Notes in Computer Science  
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.  ...  An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.  ...  This work has been partially supported by funds from the Spanish Ministry for Economy and Competitiveness and the European Union (FEDER funds) under grant TIN2013-46181-C2-1-R, and the Generalitat de Catalunya  ... 
doi:10.1007/978-3-319-45348-4_7 fatcat:4vhrw2rdh5h2bgtgiow5rq67pi

Automated Discovery of Process Models from Event Logs: Review and Benchmark [article]

Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Andrea Marrella, Massimo Mecella, Allar Soo
2018 arXiv   pre-print
One of the most widely studied process mining operations is automated process discovery.  ...  Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes.  ...  and RoMA -Resilence of Metropolitan Areas (SCN_00064).  ... 
arXiv:1705.02288v3 fatcat:7wz5qz76tnaxhmvoo4il2awonu

A Detailed Description on Unsupervised Heterogeneous Anomaly Based Intrusion Detection Framework

Asif Iqbal Hajamydeen, Nur Izura Udzir
2019 Scalable Computing : Practice and Experience  
Several logs from multiple sources are used as input and this data are processed by all the modules of the framework.  ...  The result achieved shows the direction or pathway to design anomaly detectors that can utilize raw traffic logs collected from heterogeneous sources on the network monitored and correlate the events across  ...  To perform the selection process, a feature in the log is set as class attribute to recognise the association of the fixed feature with further features and the features selected are noted.  ... 
doi:10.12694/scpe.v20i1.1465 fatcat:6hhyrhwfxnc5rigrox7uhk3kru

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, Fabrizio M. Maggi, Andrea Marrella, Massimo Mecella, Allar Soo
2018 IEEE Transactions on Knowledge and Data Engineering  
One of the most widely studied process mining operations is automated process discovery.  ...  Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes.  ...  ACKNOWLEDGMENTS This research is partly funded by the Australian Research Council (grant DP150103356) and the Estonian Research Council (grant IUT20-55).  ... 
doi:10.1109/tkde.2018.2841877 fatcat:s2sca5k24bgafplv7f5x72s2ny

Using Rough Set Theory to Find Minimal Log with Rule Generation

Tahani Nawaf Alawneh, Mehmet Ali Tut
2021 Symmetry  
Data pre-processing is a major difficulty in the knowledge discovery process, especially feature selection on a large amount of data.  ...  Algorithm complexity is related to the search of the minimal subset of attributes, which requires computing an exponential number of possible subsets.  ...  Related Works As feature selection denotes the operation of selecting a subset of attributes from the original large set of attributes, the selected subset should be of the most important and relevant  ... 
doi:10.3390/sym13101906 fatcat:5y4lxxnfvjdf7noevy6cakzlc4
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