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http://hdl.handle.net/1942/36658
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DC Field | Value | Language |
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dc.contributor.author | Goel, Kanika | - |
dc.contributor.author | Leemans, Sander J.J. | - |
dc.contributor.author | MARTIN, Niels | - |
dc.contributor.author | Wynn, Moe T. | - |
dc.date.accessioned | 2022-02-17T10:21:59Z | - |
dc.date.available | 2022-02-17T10:21:59Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2022-02-06T10:49:45Z | - |
dc.identifier.citation | ACM Transactions on Knowledge Discovery from Data, 16(5), ART N° 97. | - |
dc.identifier.issn | 1556-4681 | - |
dc.identifier.uri | http://hdl.handle.net/1942/36658 | - |
dc.description.abstract | Real-life event logs, reflecting the actual executions of complex business processes, are faced with numerous data quality issues. Extensive data sanity checks and pre-processing are usually needed before historical data can be used as input to obtain reliable data-driven insights. However, most of the existing algorithms in process mining, a field focusing on data-driven process analysis, do not take any data quality issues or the potential effects of data pre-processing into account explicitly. This can result in erroneous process mining results, leading to inaccurate or misleading conclusions about the process under investigation. To address this gap, we propose data quality annotations for event logs, which can be used by process mining algorithms to generate quality-informed insights. Using a design science approach, requirements are formulated, which are leveraged to propose data quality annotations. Moreover, we present the 'Quality-Informed visual Miner' plug-in to demonstrate the potential utility and impact of data quality annotations. Our experimental results, utilising both synthetic and real-life event logs, show how the use of data quality annotations by process mining techniques can assist in increasing the reliability of performance analysis results. | - |
dc.language.iso | en | - |
dc.publisher | - | |
dc.subject.other | Additional Key Words and Phrases: Process Mining | - |
dc.subject.other | Data Quality | - |
dc.subject.other | Annotations | - |
dc.subject.other | Metadata | - |
dc.subject.other | Quality-Informed Performance Analysis | - |
dc.subject.other | Quality-Informed Conformance Checking | - |
dc.title | Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 47 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1 | - |
dc.identifier.volume | 16 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | 1601 Broadway, 10th Floor, NEW YORK, NY USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 97 | - |
dc.identifier.doi | 10.1145/3511707 | - |
dc.identifier.isi | 000802146500017 | - |
dc.identifier.eissn | 1556-472X | - |
local.provider.type | CrossRef | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.contributor | Goel, Kanika | - |
item.contributor | Leemans, Sander J.J. | - |
item.contributor | MARTIN, Niels | - |
item.contributor | Wynn, Moe T. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2023 | - |
item.fullcitation | Goel, Kanika; Leemans, Sander J.J.; MARTIN, Niels & Wynn, Moe T. (2022) Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations. In: ACM Transactions on Knowledge Discovery from Data, 16(5), ART N° 97.. | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 1556-4681 | - |
crisitem.journal.eissn | 1556-472X | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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3511707.pdf Restricted Access | Published version | 13.74 MB | Adobe PDF | View/Open Request a copy |
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