LIQUID: Linking Data Quality Issues to Deviations in IoT-Enhanced Process Mining. Imenkamp, C., Bertrand, Y., Malburg, L., Maldonado, A., Peeperkorn, J., Schiessle, P., & Koschmider, A. In Enterprise Design, Operations, and Computing (EDOC 2026), of Lecture Notes in Computer Science, 2026. Springer.. Accepted for publication.abstract bibtex IoT-enhanced process mining allows deriving insights from raw sensor data. However, data quality issues (e.g., missing events due to inadequate sampling or range limits) are typically removed during abstraction from sensor data to event logs. This can lead to faulty decisions. Hence, the contextual information needed to explain conformance deviations is lost. This hinders analysts' ability to diagnose root causes and decreases trust in process mining results. We present LIQUID (LinkIng Data QUality Issues to Deviations), a quality-aware pipeline extension that preserves data quality metadata across transformation steps and applies probabilistic causal backtracking to trace deviations to their sensor-level origins. Evaluation results on five real-world IoT datasets show high detection accuracy and reliable root-cause attribution, complemented by actionable explanations.
@inproceedings{ImenkampEtAl2026LIQUID,
title = {{LIQUID: Linking Data Quality Issues to Deviations in IoT-Enhanced Process Mining}},
author = {Christian Imenkamp and Yannis Bertrand and Lukas Malburg and Andrea Maldonado and Jari Peeperkorn and Pascal Schiessle and Agnes Koschmider},
year = {2026},
booktitle = {{Enterprise Design, Operations, and Computing (EDOC 2026)}},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {IoT-enhanced process mining allows deriving insights from raw sensor data. However, data quality issues (e.g., missing events due to inadequate sampling or range limits) are typically removed during abstraction from sensor data to event logs. This can lead to faulty decisions. Hence, the contextual information needed to explain conformance deviations is lost. This hinders analysts' ability to diagnose root causes and decreases trust in process mining results. We present LIQUID (LinkIng Data QUality Issues to Deviations), a quality-aware pipeline extension that preserves data quality metadata across transformation steps and applies probabilistic causal backtracking to trace deviations to their sensor-level origins. Evaluation results on five real-world IoT datasets show high detection accuracy and reliable root-cause attribution, complemented by actionable explanations.},
note = {Accepted for publication.},
keywords = {Explainability, Causal Reasoning, IoT-Enhanced Process Mining, Insight Generation}
}
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