The DOI matches its Crossref record; 290 of the 290 references it cites were found in a registry or an index.
Process mining techniques and applications – A systematic mapping study
Abstract
Abstract Process mining is a growing and promising study area focused on understanding processes and to help capture the more significant findings during real execution rather than, those methods that, only observed idealized process model. The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. We also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review. In this article, an overview regarding process mining is presented, the main research topics are identified, followed by identification of the most applied process mining algorithms, and finally application domains among different business segments are reported on. It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements. In application domains, the segments with major case studies are healthcare followed by information and communication technology, manufacturing, education, finance, and logistics.
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Cites
Systematic Mapping Studies in Software Engineering
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008) · Electronic Workshops in Computing · doi:10.14236/ewic/ease2008.8 · Cited by 3,239
Industry 4.0: state of the art and future trends
Xu, L. D., Xu, E. L., & Li, L. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1444806 · Cited by 3,221
Ranade, V. V. (2009) · American Journal of Therapeutics · doi:10.1097/mjt.0b013e31817c941f · Cited by 2,775
Workflow mining: discovering process models from event logs
van der Aalst, W., Weijters, T., & Maruster, L. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.47 · Cited by 2,203
Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal
Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017) · International Journal of Production Research · doi:10.1080/00207543.2017.1308576 · Cited by 2,036
van der Aalst, W., Adriansyah, A., de Medeiros, A. K. A., Arcieri, F., Baier, T., Blickle, T., Bose, J. C., van den Brand, P., Brandtjen, R., Buijs, J., et al. (2012) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-642-28108-2_19 · Cited by 1,210
Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics
Günther, C. W., & van der Aalst, W. M. P. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-75183-0_24 · Cited by 890
Discovering models of software processes from event-based data
Cook, J. E., & Wolf, A. L. (1998) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/287000.287001 · Cited by 870
van der Aalst, W. (2016) · CERN Document Server (European Organization for Nuclear Research) · doi:10.1007/978-3-662-49851-4 · Cited by 866
Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols
Lim, L., Bannert, M., van der Graaf, J., Molenaar, I., Fan, Y., Kilgour, J., Moore, J., & Gašević, D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.749749 · Cited by 44
Metadata
- Authors
- Garcia, C. D. S., Meincheim, A., Faria Junior, E. R., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., Santos, E. A. P., & Scalabrin, E. E.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 133
- Pages
- 260-295
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2944362491
- Cited by
- 334
- References
- 290
- FWCI
- 48.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Garcia, C. D. S., Meincheim, A., Faria Junior, E. R., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., Santos, E. A. P., & Scalabrin, E. E. (2019). Process mining techniques and applications – A systematic mapping study. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2019.05.003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
