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Integrated Search for Heterogeneous Data in Process Industry Applications - A Proof of Concept, , and . Proc. IEEE IEEE International Conference on Industrial Informatics (INDIN), Boston, MA, USA, IEEE Press, (2016)Anomaly detection in the time-series data of industrial plants using neural network architectures., , , , , , and . BigDataService, page 222-228. IEEE, (2021)Comparing human and algorithmic anomaly detection for HVAC systems applications., , , , , , , , and . BigDataService, page 155-160. IEEE, (2022)Open set anomaly classification., and . BuildSys@SenSys, page 361-364. ACM, (2021)Improving Visual Defect Detection and Localization in Industrial Thermal Images Using Autoencoders., , , , , , and . J. Imaging, 9 (7): 137 (2023)Integrated search for heterogeneous data in process industry applications - A proof of concept., , , and . INDIN, page 1306-1311. IEEE, (2016)Measuring the Robustness of ML Models Against Data Quality Issues in Industrial Time Series Data., , , , , , , , and . INDIN, page 1-8. IEEE, (2023)Explaining Anomalies in Industrial Multivariate Time-series Data with the help of eXplainable AI., , , , , and . BigComp, page 226-233. IEEE, (2022)A Three-Step Machine Learning Pipeline for Detecting and Explaining Anomalies in the Time Series of Industrial Process Plants.. Deep-BDB, volume 309 of Lecture Notes in Networks and Systems, page 15-26. Springer, (2021)Mining Industrial Logs for System Level Insights., , , , and . BTW (Workshops), volume P-266 of LNI, page 57-64. GI, (2017)