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Cited by 13Business Process Modeling and AnalysisDigital Transformation in IndustryTechnology and Data Analysis

Defining Data Quality Issues in Process Mining with IoT Data

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Bertrand, Yannis, Van Belle, Rafaël, De Weerdt, Jochen, Serral, Estefanía·2023·Lecture Notes in Business Information Processing
DOI 10.1007/978-3-031-27815-0_31
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Abstract

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.

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  • Time shifts commonly arise from incorrect calibration of individual sensors.

    “Such situations commonly arise, for instance, from incorrect calibration of individual sensors, for example, [36].”

    From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [36]

    Pattern 6 describes activity start or end times being logged incorrectly because sensor detection does not coincide with the activity. It suggests changing sensor placement or range, but does not attribute these time shifts to incorrect calibration of individual sensors or say that calibration errors commonly cause them. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Bertrand et al. reviewed the IoT-BPM literature and found that the most often reported DQIs, i.e., noise, outliers, and missing data, correspond to the most often reported issues in IoT in general by Teh et al.

    “Bertrand et al. [36] reviewed the IoT-BPM literature to describe the IoT DQIs and the event log DQIs encountered by previous research. The results indicate that the most often reported DQIs, i.e., noise, outliers, and missing data, correspond to the most often reported issues in IoT in general by Teh et al. [6].”

    From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [36]

  • Although IoT data quality is acknowledged as a problem in IoT-enhanced BPM, there is little literature proposing methods to address data quality in this context.

    “Although IoT data quality is acknowledged as a problem in IoT-enhanced BPM [11,31,36], there is little literature proposing methods to address data quality in this context.”

    From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [36]

  • Neglecting these errors results in flawed data, which can lead to unreliable analyses and ultimately erroneous decisions.

    “Neglecting these errors results in flawed data, which can lead to unreliable analyses and ultimately erroneous decisions [36].”

    From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [36]

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Cited by

Sorted by citations

Authors
Bertrand, Y., Van Belle, R., De Weerdt, J., & Serral, E.
Affiliations
KU Leuven (BE)
Corresponding author
Bertrand, Yannis
ORCID
Venue
Lecture Notes in Business Information Processing
Publisher
Springer Nature Switzerland
Published
2023
Pages
422-434
ISSN
  • 1865-1348 (ISSN-L)
  • 1865-1356
DOI
10.1007/978-3-031-27815-0_31
OpenAlex
W4360971482
Cited by
13
References
3
Citations by year
2026: 3 · 2025: 4 · 2024: 5 · 2023: 1
FWCI
8.10
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in business information processing (publishedVersion, cc-by)
  • Lirias (submittedVersion, other-oa)
Funders
Fonds Wetenschappelijk Onderzoek (G0B6922N)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Computer Science › Information Systems › Technology and Data Analysis
Keywords
process mining, Internet of Things, data quality, sensor data, event logs, event abstraction, noise, outliers, data preprocessing, business processes

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