SAMPLE: A Semantic Approach for Multi-perspective Event Log Generation. Grüger, J., Geyer, T., Jilg, D., & Bergmann, R. In Montali, M., Senderovich, A., & Weidlich, M., editors, Process Mining Workshops - ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers, volume 468, of Lecture Notes in Business Information Processing, pages 328–340, 2022. Springer Nature Switzerland. ![SAMPLE: A Semantic Approach for Multi-perspective Event Log Generation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download Data and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.", isbn="978-3-031-27815-0
@inproceedings{GruegerGJB2022,
author = {Joscha Gr{\"{u}}ger and
Tobias Geyer and
David Jilg and
Ralph Bergmann},
editor = {Marco Montali and
Arik Senderovich and
Matthias Weidlich},
title = {{SAMPLE:} {A} Semantic Approach for Multi-perspective Event Log Generation},
booktitle = {Process Mining Workshops - {ICPM} 2022 International Workshops, Bozen-Bolzano,
Italy, October 23-28, 2022, Revised Selected Papers},
series = {Lecture Notes in Business Information Processing},
volume = {468},
pages = {328--340},
publisher = {Springer Nature Switzerland},
year = {2022},
url = {https://link.springer.com/chapter/10.1007/978-3-031-27815-0_24},
doi = {10.1007/978-3-031-27815-0\_24},
timestamp = {Fri, 26 May 2023 07:40:33 +0200},
biburl = {https://dblp.org/rec/conf/icpm/GrugerGJB22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
abstract = {Data and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.",
isbn="978-3-031-27815-0}
}
Downloads: 1
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