Khodabakhsh, AtharArı, İsmailBakır, M.Alagoz, S. M.2020-04-172020-04-172018-09-07978-1-5386-7232-72379-7703http://hdl.handle.net/10679/6521https://doi.org/10.1109/BigDataCongress.2018.00042It is necessary to develop accurate, yet simple and efficient models that can be used with high-speed industrial data streams. In this paper, we develop a mode identification technique using stream analytics and show that it may be more effective than batch models, especially for time-varying systems. These industrial systems continuously monitor hundreds of sensors, but the relationships among variables change over time, which are identified as different operational modes. To detect drifts among modes, predictive modeling techniques such as regression analysis, K-means and DBSCAN clustering are used over sensor data streams from an oil refinery and models are updated in real-time using window-based analysis. Finally, an adaptive window size tuning approach based on the TCP congestion control algorithm is discussed, which reduces model update costs as well as prediction errors.engrestrictedAccessconferenceObject24224600045016040003410.1109/BigDataCongress.2018.00042Operational mode identificationSensorLinear regressionStream dataAdaptive windowOutlier detection2-s2.0-85057376045