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http://hdl.handle.net/1942/25912
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | JOUCK, Toon | - |
dc.contributor.author | DEPAIRE, Benoit | - |
dc.date.accessioned | 2018-04-16T13:22:06Z | - |
dc.date.available | 2018-04-16T13:22:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Business & Information Systems Engineering, 61(6), p.695-712. | - |
dc.identifier.issn | 2363-7005 | - |
dc.identifier.uri | http://hdl.handle.net/1942/25912 | - |
dc.description.abstract | Within the process mining domain, research on comparing control-flow (CF) discovery techniques has gained importance. A crucial building block of empirical analysis of CF discovery techniques is getting the appropriate evaluation data. Currently, there is no answer to the question of how to collect such evaluation data. This paper introduces a methodology for generating artificial event data (GED) and an implementation called the Process Tree and Log Generator. The GED methodology and its implementation provide users with full control over the characteristics of the generated event data and an integration within the ProM framework. Unlike existing approaches, there is no tradeoff between including long-term dependencies and soundness of the process. The contributions of this paper provide a necessary step in the empirical analysis of CF discovery algorithms. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER VIEWEG-SPRINGER FACHMEDIEN WIESBADEN GMBH | - |
dc.rights | Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2018 | - |
dc.subject.other | Artificial event logs | - |
dc.subject.other | Process discovery | - |
dc.subject.other | Empirical analysis | - |
dc.title | Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms: A Process Tree and Log Generator | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 712 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 695 | - |
dc.identifier.volume | 61 | - |
local.format.pages | 18 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | ABRAHAM-LINCOLN STASSE 46, WIESBADEN, 65189, GERMANY | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.source.type | Article | - |
dc.identifier.doi | 10.1007/s12599-018-0541-5 | - |
dc.identifier.isi | WOS:000496706700005 | - |
dc.identifier.eissn | 1867-0202 | - |
local.provider.type | Web of Science | - |
local.uhasselt.uhpub | yes | - |
item.fullcitation | JOUCK, Toon & DEPAIRE, Benoit (2018) Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms: A Process Tree and Log Generator. In: Business & Information Systems Engineering, 61(6), p.695-712.. | - |
item.accessRights | Restricted Access | - |
item.contributor | JOUCK, Toon | - |
item.contributor | DEPAIRE, Benoit | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2020 | - |
item.validation | vabb 2020 | - |
crisitem.journal.issn | 2363-7005 | - |
crisitem.journal.eissn | 1867-0202 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PTandLogGenerator.pdf Restricted Access | Peer-reviewed author version | 836.28 kB | Adobe PDF | View/Open Request a copy |
Jouck-Depaire2018_Article_GeneratingArtificialDataForEmp.pdf Restricted Access | Published version | 1.24 MB | Adobe PDF | View/Open Request a copy |
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