{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:07:47Z","timestamp":1774915667747,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031278143","type":"print"},{"value":"9783031278150","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T00:00:00Z","timestamp":1679788800000},"content-version":"vor","delay-in-days":84,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the processes. In the last years, there has been a growing awareness of the opportunity to use the data these devices generate for process mining (PM) by deriving an event log from a sensor log via event abstraction techniques. However, IoT data are often affected by data quality issues (e.g., noise, outliers) which, if not addressed at the preprocessing stage, will be amplified by event abstraction and result in quality issues in the event log (e.g., incorrect events), greatly hampering PM results. In this paper, we review the literature on PM with IoT data to find the most frequent data quality issues mentioned in the literature. Based on this, we then derive six patterns of poor sensor data quality that cause event log quality issues and propose solutions to avoid or solve them.<\/jats:p>","DOI":"10.1007\/978-3-031-27815-0_31","type":"book-chapter","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T10:03:04Z","timestamp":1679738584000},"page":"422-434","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Defining Data Quality Issues in\u00a0Process Mining with\u00a0IoT Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6407-7221","authenticated-orcid":false,"given":"Yannis","family":"Bertrand","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8704-6017","authenticated-orcid":false,"given":"Rafa\u00ebl","family":"Van Belle","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-0504","authenticated-orcid":false,"given":"Jochen","family":"De Weerdt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7579-910X","authenticated-orcid":false,"given":"Estefan\u00eda","family":"Serral","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"31_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/978-3-030-04191-5_37","volume-title":"Artificial Intelligence XXXV","author":"E Bandis","year":"2018","unstructured":"Bandis, E., Petridis, M., Kapetanakis, S.: Business process workflow mining using machine learning techniques for the rail transport industry. 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