Identifying Missing Sensor Values in IoT Time Series Data: A Weight-Based Extension of Similarity Measures for Smart Manufacturing
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A weighted version of the DTW algorithm is a suitable similarity measure for missing sensor values.
“In previous work [49], a weighted version of the DTW algorithm is introduced as a suitable similarity measure for this assumption.”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [49]
The DQI failure of missing sensor values is present if one or more values that should have been observed are not contained in the time series.
“The DQI failure of missing sensor values is present if one or more values that should have been observed are not contained in the time series [49].”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [49]
Pre-processing such as abstraction or removal of irrelevant data points would make it impossible to recognize DQI-specific patterns, such as missing values.
“Such pre-processing would make it impossible to recognize DQI-specific patterns, such as missing values [49].”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [49]
For missing sensor values, cases in which values are recorded at different intervals are recognized as similar.
“For this type of DQI, this provides the advantage that cases in which values are recorded at different intervals are recognized as similar [49].”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [49]
These results have already been observed for the partial approach to address the missing sensor values [49].
“These results have already been observed for the partial approach to address the missing sensor values [49].”
From Case-Based Data Quality Management for IoT Logs: A Case Study Focusing on Detection of Data Quality Issues (2025) · cites this paper as [49]
Cites
Viewpoint: Human-in-the-loop Artificial Intelligence
Zanzotto, F. M. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11345 · Cited by 380 · see citing works
Sensor data quality: a systematic review
Teh, H. Y., Kempa-Liehr, A. W., & Wang, K. I.-K. (2020) · Journal of Big Data · doi:10.1186/s40537-020-0285-1 · Cited by 377 · see citing works
Explanation in Case-Based Reasoning—Perspectives and Goals
Sørmo, F., Cassens, J., & Aamodt, A. (2005) · Artificial Intelligence Review · doi:10.1007/s10462-005-4607-7 · Cited by 239 · see citing works
DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
Mangler, J., Grüger, J., Malburg, L., Ehrendorfer, M., Bertrand, Y., Benzin, J.-V., Rinderle-Ma, S., Serral Asensio, E., & Bergmann, R. (2023) · Future Internet · doi:10.3390/fi15030109 · Cited by 33 · cited 1× · see citing works
“The error-free data used is publicly available [27] and is represented in the DataStream format [31].”
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Digital Twins of Business Processes: A Research Manifesto
Fornari, F., Compagnucci, I., Donato, M. C. D., Bertrand, Y., Beyel, H. H., Carrión, E., Franceschetti, M., Groher, W., Grüger, J., Kilic, E., et al. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101477 · Cited by 37 · see citing works
“Acknowledging the possibility of hardware or software failures that could lead to the generation of faulty data is necessary, and countermeasures should be taken [49, 79, 84].”
Self-citationSchultheis, A., Jilg, D., Malburg, L., Bergweiler, S., & Bergmann, R. (2024) · Processes · doi:10.3390/pr12122714 · Cited by 6 · see citing works
“…as other process types like analysis processes (e.g., for use in predictive maintenance [22,23] or the identification of data quality issues [24,25]), it is necessary for the information on which the planning is based to be available in a suitable form to be transferred to a planning problem…”
Self-citationPrivacy-Preserving Data Quality Assessment for Time-Series IoT Sensors
Chakraborty, N., Sharma, A., Dutta, J., & Kumar, H. D. (2024) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais64014.2024.10799255 · Cited by 4 · see citing works
“However, ensuring the quality of time-series data IoT sensors, especially in wide-scale deployments such as smart cities is difficult [5], [6], [7].”
Challenges in Data Quality Management for IoT-Enhanced Event Logs
Bertrand, Y., Schultheis, A., Malburg, L., Grüger, J., Asensio, E. S., & Bergmann, R. (2025) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-031-92474-3_2 · Cited by 4 · see citing works
Self-citationIntegration of Time Series Embedding for Efficient Retrieval in Case-Based Reasoning
Weich, J., Schultheis, A., Hoffmann, M., & Bergmann, R. (2025) · International Conference on Case-Based Reasoning · doi:10.1007/978-3-031-96559-3_22 · Cited by 4 · see citing works
“A time series depicts real measured values over a period of time, for example, IoT sensor data from manufacturing processes [39,52].”
Self-citationCase-Based Activity Detection from Segmented Internet of Things Data
Seiger, R., Schultheis, A., & Bergmann, R. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-96559-3_29 · Cited by 2 · see citing works
“However, such an abstraction leads to a loss of information, which can be unsuitable for certain use cases [37].”
Self-citationReal-Time Manufacturing Data Quality: Leveraging Data Profiling and Quality Metrics
Peixoto, T., Oliveira, B., Oliveira, Ó., & Ribeiro, F. (2025) · International Conference on Internet of Things, Big Data and Security · doi:10.5220/0013242900003944 · Cited by 2
“With the advances in industrial technology, the increasing number of sensors deployed for monitoring manufacturing processes leads to the constant generation of large volumes of time series data (Schultheis et al., 2024).”
Schultheis, A., Bertrand, Y., Grüger, J., Malburg, L., Bergmann, R., & Serral Asensio, E. (2025) · IoT · doi:10.3390/iot6040063 · Cited by 1 · see citing works
“The DQI failure of missing sensor values is present if one or more values that should have been observed are not contained in the time series [49].”
InfluentialSelf-citationCBR-FoX: A case-based reasoning software tool for auditing time series predictions
Pérez-Pérez, G. A., Valdez-Ávila, M. F., Orozco-del-Castillo, M., Bermejo-Sabbagh, C., & Recio-García, J. A. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102450 · Cited by 1
Kong, T., Li, H., Zhao, Y., Li, L.-P., Gao, X.-Y., Wu, Q.-L., & Cui, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3746405.3746421 · Cited by 1 · see citing works
Zhan, J., Xu, G., & Xu, X.-D. (2025) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc66453.2025.11304695
“For instance, Alexander Schultheis et al. [15] and Humberto Parejas-Llanovarced et al. [16] applied CBR in smart manufacturing and AIoT systems, respectively, while Stewart Massie et al. [17] developed a CBR-based smart home system that facilitates proactive interventions by comparing residents’…”
Is It Instrumentation? Evaluation of Validity Constraints in Smart Homes
Taleb, R., Villemaire, R., Ngankam, H. K., Gaboury, S., & Hallé, S. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3572655
“Schultheis et al. [48] analyzed IoT-based smart manufacturing and they identified cases of completely missing sensor streams, where entire sections of data, such as temperature sensor readings, were absent.”
- Authors
- Schultheis, A., Malburg, L., Grüger, J., Weich, J., Bertrand, Y., Bergmann, R., & Serral Asensio, E.
- Affiliations
- German Research Centre for Artificial Intelligence (DE)
- KU Leuven (BE)
- Corresponding author
- Schultheis, Alexander
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 240-257
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4399932865
- Cited by
- 9
- References
- 0
- Citations by year
- 2025: 5 · 2024: 4
- FWCI
- 3.38
- Type
- conference paper
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- time series similarity measures, data quality issues, case-based reasoning, smart manufacturing, Internet of Things sensor data, missing data imputation
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