Wanna improve process mining results?
Abstract
The growing interest in process mining is fueled by the increasing availability of event data. Process mining techniques use event logs to automatically discover process models, check conformance, identify bottlenecks and deviations, suggest improvements, and predict processing times. Lion's share of process mining research has been devoted to analysis techniques. However, the proper handling of problems and challenges arising in analyzing event logs used as input is critical for the success of any process mining effort. In this paper, we identify four categories of process characteristics issues that may manifest in an event log (e.g. process problems related to event granularity and case heterogeneity) and 27 classes of event log quality issues (e.g., problems related to timestamps in event logs, imprecise activity names, and missing events). The systematic identification and analysis of these issues calls for a consolidated effort from the process mining community. Five real-life event logs are analyzed to illustrate the omnipresence of process and event log issues. We hope that these findings will encourage systematic logging approaches (to prevent event log issues), repair techniques (to alleviate event log issues) and analysis techniques (to deal with the manifestation of process characteristics in event logs).
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Towards a better assessment of event logs quality
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“…lead to wrong discovered process models, since the event logs quality has an influence not only on the process mining results but also on the choice of process mining algorithm (e.g. applying alpha algorithm [4] to real-life event logs is unrealistic, or applying heuristic miner [5] to less…”
MethodAlharbi, A., Bulpitt, A. J., & Johnson, O. A. (2017) · Lecture notes in business information processing · doi:10.1007/978-3-319-65015-9_6 · Cited by 21 · see citing works
Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations
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“For example, incorrect timestamps can alter the order of events and, moreover, result in the calculation of incorrect activity durations [5].”
BackgroundInfluentialProcess mining applications in software engineering
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Root-cause analysis of process-data quality problems
Andrews, R., Emamjome, F., ter Hofstede, A. H. M., & Reijers, H. A. (2021) · Journal of Business Analytics · doi:10.1080/2573234x.2021.1947751 · Cited by 19 · see citing works
“Missing cases (i.e., where actual executions of a process do not appear in an event log) were identified in Bose et al. (2013) as a quality issue that can distort the process mining results and hinder the discovery of critical paths in the processes.”
BackgroundMethodResultInfluentialDaQAPO: Supporting flexible and fine-grained event log quality assessment
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“The result list was cross-checked with related papers on event log quality [20,23] and one highly cited paper on outlier detection for process discovery [3].”
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Franceschetti, M., Seiger, R., López, H. A., Burattin, A., García‐Bañuelos, L., & Weber, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47262-6_15 · Cited by 18 · see citing works
“Imperfection patterns are quality issues, in line with the results of [10], which result in the observational ambiguity types presented here.”
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A Purpose-Guided Log Generation Framework
Burattin, A., Re, B., Rossi, L., & Tiezzi, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16103-2_14 · Cited by 17 · see citing works
All that glitters is not gold: Four maturity stages of process discovery algorithms
van der Werf, J. M. E. M., Polyvyanyy, A., van Wensveen, B. R., Brinkhuis, M. J. S., & Reijers, H. A. (2022) · Information Systems · doi:10.1016/j.is.2022.102155 · Cited by 17 · see citing works
Schmid, S. J., Moder, L., Hofmann, P., & Röglinger, M. (2023) · Information Systems · doi:10.1016/j.is.2023.102246 · Cited by 17 · see citing works
Multi-perspective conformance checking of uncertain process traces: An SMT-based approach
Felli, P., Gianola, A., Montali, M., Rivkin, A., & Winkler, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106895 · Cited by 17 · see citing works
AI-Empowered Process Mining for Complex Application Scenarios: Survey and Discussion
Folino, F., & Pontieri, L. (2021) · Journal on Data Semantics · doi:10.1007/s13740-021-00121-2 · Cited by 17 · see citing works
“In fact, the quality of most reallife logs ranges in the middle of these two extremes [5,82].”
BackgroundProcess mining-driven analysis of COVID-19’s impact on vaccination patterns
Augusto, A., Deitz, T., Faux, N. G., Manski-Nankervis, J.-A. E., & Daniel Capurro, N. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104081 · Cited by 17 · see citing works
Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
Salazar-Fernandez, J. P., Muñoz-Gama, J., Maldonado‐Mahauad, J., Bustamante, D., & Sepúlveda, M. (2021) · Applied Sciences · doi:10.3390/app11094265 · Cited by 16 · see citing works
“First, the conclusions derived from the BPPM and from process mining in general depend largely on the accuracy and completeness of the information used [51].”
MethodDemystifying data governance for process mining: Insights from a Delphi study
Goel, K., Martin, N., & ter Hofstede, A. H. M. (2024) · Information & Management · doi:10.1016/j.im.2024.103973 · Cited by 16 · see citing works
Augmenting and Assisting Model Elicitation Tasks with 3D Virtual World Context Metadata
Brown, R. A., Rinderle‐Ma, S., Kriglstein, S., & Kabicher, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-45563-0_3 · Cited by 16 · see citing works
“Such a hint addresses issues raised by [3], instead of the interviewee relying on their memory, the priming of the world will push the user towards a temporally aligned specification of the to-do list.”
BackgroundMethodInfluentialA process mining methodology for modeling unstructured processes
Stefanini, A., Aloini, D., Benevento, E., Dulmin, R., & Mininno, V. (2020) · Knowledge and Process Management · doi:10.1002/kpm.1649 · Cited by 17 · see citing works
Pradhan, S. K., Jans, M. J., & Martin, N. (2025) · ACM Computing Surveys · doi:10.1145/3712587 · Cited by 15 · see citing works
“Several articles investigate conceptual aspects, such as different types, characteristics, and measures of quality [4, 22, 192, 200].”
Martin, N. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-11641-5_18 · Cited by 15 · see citing works
Assessing and improving measurability of process performance indicators based on quality of logs
Cappiello, C., Comuzzi, M., Plebani, P., & Fim, M. (2021) · Information Systems · doi:10.1016/j.is.2021.101874 · Cited by 15 · see citing works
Lexicon-based content analysis of BIM logs for diverse BIM log mining use cases
Jang, S., Lee, G., Shin, S., & Roh, H. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102079 · Cited by 14 · see citing works
Prototype Selection Using Clustering and Conformance Metrics for Process Discovery
Sani, M. F., Boltenhagen, M., & van der Aalst, W. M. P. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-66498-5_21 · Cited by 14 · see citing works
Defining Data Quality Issues in Process Mining with IoT Data
Bertrand, Y., Van Belle, R., De Weerdt, J., & Serral, E. (2023) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-031-27815-0_31 · Cited by 13 · see citing works
Process Mining in Data Science: A Literature Review
Ahmed, R., Faizan, M., & Burney, A. I. (2019) · International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) · doi:10.1109/macs48846.2019.9024806 · Cited by 12 · see citing works
“Second model Spaghetti procedures are naturally met in product improvement, supply controlling, sales/CRM, and service’ [12] [13].”
BackgroundUsing multi-target feature evaluation to discover factors that affect business process behavior
Delias, P., Lagopoulos, A., Tsoumakas, G., & Grigori, D. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.03.022 · Cited by 11 · see citing works
Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log
Horita, H., Kurihashi, Y., & Miyamori, N. (2020) · Data · doi:10.3390/data5030082 · Cited by 11 · see citing works
“However, due to technical (deviations could occur even for automatic logging systems due to machine breakdowns, system bugs, and resource constraints [3]) or human (human error) reasons, the event log may contain missing values [4].”
BackgroundDesigning synchronizer module in CMMS software based on lean smart maintenance and process mining
Mazloumi, S. H. H., Moini, A., & Kermani, M. A. M. A. (2022) · Journal of Quality in Maintenance Engineering · doi:10.1108/jqme-10-2021-0077 · Cited by 11 · see citing works
Applying Process Mining in Small and Medium Sized IT Enterprises – Challenges and Guidelines
Eggert, M., & Dyong, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16103-2_11 · Cited by 11 · see citing works
Decision Discovery in Business Processes
de Leoni, M., & Mannhardt, F. (2018) · Encyclopedia of Big Data Technologies · doi:10.1007/978-3-319-63962-8_96-1 · Cited by 11 · see citing works
“For example, decisions taken by process workers may depend on contextual factors, which are not encoded in the system and, thus, not available in event logs [7].”
BackgroundTruncated Trace Classifier. Removal of Incomplete Traces from Event Logs
Bernard, G., & Andritsos, P. (2020) · Lecture notes in business information processing · doi:10.1007/978-3-030-49418-6_10 · Cited by 10 · see citing works
Bagging Recurrent Event Imputation for Repair of Imperfect Event Log with Missing Categorical Events
Sim, S., Bae, H., & Liu, L. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3118381 · Cited by 10 · see citing works
“Despite the continuous development in the process mining area, if the quality of the event log used for analysis is low, the quality of the analysis result will be low, even with good process mining techniques [16].”
BackgroundMethodInfluentialExploration and Assessment of Event Data
Bodesinsky, P., Alsallakh, B., Gschwandtner, T., & Miksch, S. (2015) · doi:10.2312/eurova.20151106 · Cited by 10 · see citing works
“Before applying automated algorithms complex data has to be made understandable to find recurring patterns and subsequences in event data, group the data and check for errors [BMvdA13].”
MethodModel as You Do: Engaging an S-BPM Vendor on Process Modelling in 3D Virtual Worlds
Harman, J., Brown, R. A., Kannengiesser, U., Meyer, N., & Rothschädl, T. (2015) · Springer eBooks · doi:10.1007/978-3-319-17542-3_7 · Cited by 10 · see citing works
“Process model elicitation still poses a huge challenge with respect to the quality of the resulting process models, independently of whether the information was gathered from interviews (Kabicher and Rinderle-Ma 2011), by exploiting existing data sources (Dunkl 2013), or by process mining (Bose et al. 2013).”
BackgroundAn Expert Lens on Data Quality in Process Mining
Andrews, R., Emamjome, F., ter Hofstede, A. H. M., & Reijers, H. A. (2020) · International Conference on Process Mining (ICPM) · doi:10.1109/icpm49681.2020.00018 · Cited by 10 · see citing works
van der Werf, J. M. E. M., Polyvyanyy, A., van Wensveen, B. R., Brinkhuis, M. J. S., & Reijers, H. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79382-1_9 · Cited by 10 · see citing works
Decision Discovery in Business Processes
de Leoni, M., & Mannhardt, F. (2019) · Encyclopedia of Big Data Technologies · doi:10.1007/978-3-319-77525-8_96 · Cited by 10 · see citing works
Salazar-Fernandez, J. P., Muñoz-Gama, J., & Sepúlveda, M. (2024) · Higher Education · doi:10.1007/s10734-024-01230-0 · Cited by 9 · see citing works
“Third, the conclusions obtained through PM depend on the completeness of the information used (Bose et al., 2013).”
Dental Extractions under General Anesthesia: New Insights from Process Mining
Fox, F., Whelton, H. P., Johnson, O. A., & Aggarwal, V. R. (2022) · JDR Clinical & Translational Research · doi:10.1177/23800844221088833 · Cited by 9 · see citing works
A NLP-Oriented Methodology to Enhance Event Log Quality
Ramos-Gutiérrez, B., Varela‐Vaca, Á. J., Ortega, F. J., Gómez-López, M. T., & Wynn, M. T. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-79186-5_2 · Cited by 9 · see citing works
The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study
van Hulzen, G., Martin, N., & Depaire, B. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-72693-5_24 · Cited by 8 · see citing works
Data Improvement to Enable Process Mining on Integrated Non-log Data Sources
Dunkl, R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53856-8_62 · Cited by 8 · see citing works
“[4] collected and categorized a multitude of DQIs in this area, also covering the ones we identified, but offers no solutions or concepts to improve data relating to theses DQIs.”
BackgroundIntegration of Process Mining and Simulation: A Survey of Applications and Current Research
Keith, B. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77703-0_29 · Cited by 8 · see citing works
Data Cleaning for Process Mining with Smart Contract
Ekici, B., Tarhan, A., & Özsoy, A. (2019) · doi:10.1109/ubmk.2019.8907140 · Cited by 8 · see citing works
“For example, the study [10] elicits a list of common data quality issues that are encountered in event logs and discusses the impact of these issues on the performance of process mining.”
BackgroundChen, Q., Lu, Y., Tam, C. S., & Poon, S. K.-S. (2022) · Future Internet · doi:10.3390/fi14060181 · Cited by 8 · see citing works
“Additionally, Bose et al. [26] and Aalst [27] raised concerns for event data quality in process mining.”
BackgroundTowards Event Log Management for Process Mining - Vision and Research Challenges
van Cruchten, R. M. E. R., & Weigand, H. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-05760-1_12 · Cited by 8 · see citing works
Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach
Butt, N. A., Mahmood, Z., Sana, M. U., Díez, I. D. L. T., Galán, J. C., Brie, S., & Ashraf, I. (2023) · Applied Sciences · doi:10.3390/app13074145 · Cited by 8 · see citing works
“Success-fully navigating issues and difficulties associated with analyzing event logs is crucial to any process mining process [25].”
BackgroundInfluentialSchuh, G., Gützlaff, A., Cremer, S., Schmitz, S., & Ayati, A. (2020) · doi:10.1109/ieem45057.2020.9309946 · Cited by 8 · see citing works
“Thereby it depends on the information system, whether both timestamps or either start/end timestamps are logged [20].”
BackgroundAll That Glitters Is Not Gold: Towards Process Discovery Techniques with GuaranteesPreprint
van der Werf, J. M. E. M., Polyvyanyy, A., van Wensveen, B. R., Brinkhuis, M. J. S., & Reijers, H. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12764 · Cited by 8 · see citing works
Workarounds as a Cause of Mismatches in Business Processes—
Bartelheimer, C., Löhr, B., Reineke, M., Aßbrock, A., & Beverungen, D. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00943-5 · Cited by 7 · see citing works
“Each level can carry further contextual data, called data payload (van der Aalst et al. 2012; Bose et al. 2013).”
Reviewing Process Mining Applications and Techniques in Education
Sypsas, A., & Kalles, D. (2022) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2022.13106 · Cited by 7 · see citing works
Event Log Data Quality Issues and Solutions
Dakić, D., Stefanović, D., Lolić, T., Žižakov, M., & Stevanov, B. (2023) · Mathematics · doi:10.3390/math11132858 · Cited by 7 · see citing works
A compliance assessment system for Incident Management process
Palma, A., Acitelli, G., Marrella, A., Bonomi, S., & Angelini, M. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104070 · Cited by 7 · see citing works
Extracting Best-Practice Using Mixed-Methods
Poppe, E., Pika, A., Wynn, M. T., Eden, R., Andrews, R., & ter Hofstede, A. H. M. (2021) · Business & Information Systems Engineering · doi:10.1007/s12599-021-00698-9 · Cited by 7 · see citing works
Mining unstructured processes: An exploratory study on a distance learning domain
Maita, A. R. C., Fantinato, M., Peres, S. M., Thom, L. H., & Hung, P. C. K. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966261 · Cited by 7 · see citing works
“process mining open issues as those discussed in [10].”
BackgroundPre-hospital Retrieval and Transport of Road Trauma Patients in Queensland
Andrews, R., Wynn, M. T., Vallmuur, K., ter Hofstede, A. H. M., Bosley, E., Elcock, M., & Rashford, S. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-11641-5_16 · Cited by 7 · see citing works
Event Log Reconstruction Using Autoencoders
Nguyen, H. T. C., & Comuzzi, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17642-6_28 · Cited by 7 · see citing works
Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
El-Gharib, N. M., & Amyot, D. (2022) · Algorithms · doi:10.3390/a15060180 · Cited by 7 · see citing works
“[27] that are not handled by our method, such as the fixing of incorrect or imprecise timestamps.”
BackgroundMethodAnalysis of Business Processes with Enterprise Ontology and Process Mining
Caetano, A., Pinto, P. L., Mendes, C., da Silva, M. M., & Borbinha, J. L. (2015) · Lecture notes in business information processing · doi:10.1007/978-3-319-19297-0_6 · Cited by 7 · see citing works
Towards Process Mining of EMR Data - Case Study for Sepsis Management
de Vries, G.-J., Neira, R. A. Q., Geleijnse, G., Dixit, P. M., & Mazza, B. F. (2017) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0006274405850593 · Cited by 7 · see citing works
“Bose et al. distinguish 27 classes of quality issues with event logs (Bose et al., 2013).”
BackgroundMinería de procesos de software: una revisión de experiencias de aplicación
Universidad Católica del Norte, A. 0., Keith, B., Zepeda, V. V., & Universidad Católica del Norte, A. 0. (2017) · RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação · doi:10.17013/risti.21.51-66 · Cited by 6 · see citing works
“Aun así, la eficiencia y efectividad de la minería de procesos puede verse beneficiada sustancialmente de conjuntos de datos bien estructurados y definidos, además de guías de recolección que permitan obtener conjuntos de datos de alta calidad (Samalikova et al., 2011; Bose et al., 2013).”
BackgroundAn interpretable deep fusion framework for event log repair
Yuan, Y., Fang, X., Lü, K., & Zhang, Z. (2025) · Information Systems · doi:10.1016/j.is.2025.102548 · Cited by 6 · see citing works
Visual analytics for the interpretation of fluency tests during Alzheimer evaluation
Lhuillier, A., Hurter, C., Jouffrais, C., Barbeau, E. J., & Amieva, H. (2015) · Workshop on Visual Analytics in Healthcare (VAHC) · doi:10.1145/2836034.2836037 · Cited by 6 · see citing works
“Different taxonomy of the data quality problems exist [3,6,15,21,23,26].”
BackgroundOsses, A. S., Arias, M., Silva, L. Q. D., Rojas, E., Cobo, B. F., Muñoz-Gama, J., & Sepúlveda, M. (2016) · International Conference of the Chilean Computer Science Society (SCCC) · doi:10.1109/sccc.2016.7836000 · Cited by 6 · see citing works
“Una de las principales dificultades al aplicar minería de procesos es la calidad de los datos a utilizar [25].”
BackgroundCut to the Trace! Process-Aware Partitioning of Long-Running Cases in Customer Journey Logs
Bernard, G., Senderovich, A., & Andritsos, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79382-1_31 · Cited by 6 · see citing works
- Authors
- Bose, R. P. J. C., Mans, R. S., & van der Aalst, W. M. P.
- Affiliations
- Eindhoven University of Technology (NL)
- Venue
- 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)
- Publisher
- IEEE
- Published
- 2013-04
- Pages
- 127-134
- OpenAlex
- W2092898466
- Cited by
- 192
- References
- 54
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 14 · 2023: 16 · 2022: 23 · 2021: 24 · 2020: 13 · 2019: 20 · 2018: 13 · 2017: 10 · 2016: 10 · 2015: 13 · 2014: 2 · 2013: 1
- FWCI
- 13.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (publishedVersion)
- TU/e Research Portal (submittedVersion)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Stichting voor de Technische Wetenschappen
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- process mining, event logs, timestamps, process characteristics, process discovery
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