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http://hdl.handle.net/1942/32722
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DC Field | Value | Language |
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dc.contributor.author | VAN HOUDT, Greg | - |
dc.contributor.author | DEPAIRE, Benoit | - |
dc.contributor.author | MARTIN, Niels | - |
dc.date.accessioned | 2020-11-30T12:30:49Z | - |
dc.date.available | 2020-11-30T12:30:49Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2020-11-12T17:55:30Z | - |
dc.date.submitted | 2020-11-12T17:55:30Z | - |
dc.identifier.citation | Leemans, S.; Leopold, H. (Eds.). Process Mining Workshops ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers, p. 82-93. | - |
dc.identifier.isbn | 9783030726928 | - |
dc.identifier.issn | 1865-1348 | - |
dc.identifier.uri | http://hdl.handle.net/1942/32722 | - |
dc.description.abstract | Due to the rise of IoT, event data becomes increasingly fine-grained. Faced with such data, process discovery often produces incomprehensible spaghetti-models expressed at a granularity level that doesn't match the mental model of a business user. One approach is to use event abstraction patterns to transform the event log towards a more coarse-grained level and to discover process models from this transformed log. Recent literature has produced various (partial) implementations of this approach, but insights how these techniques compare against each other is still limited. This paper focuses on the use of Local Process Models and Combination based Behavioural Pattern Mining to discover event abstraction patterns in combination with the approach of Mannhardt et al. to transform the event log. Experiments are conducted to gain insights into the performance of these techniques. Results are very limited with a general decrease in fitness and precision and only a minimal improvement of complexity. Results also show that the combination of the process discovery algorithm and the event abstraction pattern miner matters. In particular, the combination of Local Process Models with Split Miner seems to improve precision. | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.relation.ispartofseries | Lecture Notes in Business Information Processing | - |
dc.rights | Springer Nature Switzerland AG 2021 | - |
dc.subject.other | Process Mining | - |
dc.subject.other | Unsupervised Learning | - |
dc.subject.other | Event Log | - |
dc.subject.other | Abstraction | - |
dc.title | Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.authors | Leemans, Sander | - |
local.bibliographicCitation.authors | Leopold, Henrik | - |
local.bibliographicCitation.conferencedate | 5 October 2020 | - |
local.bibliographicCitation.conferencename | 2nd International Conference on Process Mining (ICPM 2020) | - |
local.bibliographicCitation.conferenceplace | Padua, Italy | - |
dc.identifier.epage | 93 | - |
dc.identifier.spage | 82 | - |
dc.identifier.volume | 406 | - |
local.bibliographicCitation.jcat | C1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
local.relation.ispartofseriesnr | 406 | - |
dc.identifier.doi | 10.1007/978-3-030-72693-5_7 | - |
local.provider.type | - | |
local.bibliographicCitation.btitle | Process Mining Workshops ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.validation | vabb 2023 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.fullcitation | VAN HOUDT, Greg; DEPAIRE, Benoit & MARTIN, Niels (2021) Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study. In: Leemans, S.; Leopold, H. (Eds.). Process Mining Workshops ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers, p. 82-93.. | - |
item.contributor | VAN HOUDT, Greg | - |
item.contributor | DEPAIRE, Benoit | - |
item.contributor | MARTIN, Niels | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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VanHoudt2021_Chapter_UnsupervisedEventAbstractionIn.pdf | Published version | 305.14 kB | Adobe PDF | View/Open |
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