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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)

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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)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)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)Defining software architectures for big data enabled operator support systems., , , , , , and . INDIN, page 1288-1292. IEEE, (2016)Defining Software Architectures for Big Data Enabled Operator Support Systems, , , , , , and . Proc. IEEE International Conference on Industrial Informatics (INDIN), Boston, MA, USA, IEEE Press, (2016)XAI for Operations in the Process Industry - Applications, Theses, and Research Directions., , , , , and . AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, volume 2846 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Integrated search for heterogeneous data in process industry applications - A proof of concept., , , and . INDIN, page 1306-1311. IEEE, (2016)Explaining Anomalies in Industrial Multivariate Time-series Data with the help of eXplainable AI., , , , , and . BigComp, page 226-233. IEEE, (2022)