APA 6th Edition Kenda, K. & Mladenić, D. (2018). Autonomous Sensor Data Cleaning in Stream Mining Setting. Business Systems Research, 9 (2), 69-79. https://doi.org/10.2478/bsrj-2018-0020
MLA 8th Edition Kenda, Klemen and Dunja Mladenić. "Autonomous Sensor Data Cleaning in Stream Mining Setting." Business Systems Research, vol. 9, no. 2, 2018, pp. 69-79. https://doi.org/10.2478/bsrj-2018-0020. Accessed 22 Nov. 2019.
Chicago 17th Edition Kenda, Klemen and Dunja Mladenić. "Autonomous Sensor Data Cleaning in Stream Mining Setting." Business Systems Research 9, no. 2 (2018): 69-79. https://doi.org/10.2478/bsrj-2018-0020
Harvard Kenda, K., and Mladenić, D. (2018). 'Autonomous Sensor Data Cleaning in Stream Mining Setting', Business Systems Research, 9(2), pp. 69-79. https://doi.org/10.2478/bsrj-2018-0020
Vancouver Kenda K, Mladenić D. Autonomous Sensor Data Cleaning in Stream Mining Setting. Business Systems Research [Internet]. 2018 [cited 2019 November 22];9(2):69-79. https://doi.org/10.2478/bsrj-2018-0020
IEEE K. Kenda and D. Mladenić, "Autonomous Sensor Data Cleaning in Stream Mining Setting", Business Systems Research, vol.9, no. 2, pp. 69-79, 2018. [Online]. https://doi.org/10.2478/bsrj-2018-0020
Abstracts Background: Internet of Things (IoT), earth observation and big scientific experiments are sources of extensive amounts of sensor big data today. We are faced with large amounts of data with low measurement costs. A standard approach in such cases is a stream mining approach, implying that we look at a particular measurement only once during the real-time processing. This requires the methods to be completely autonomous. In the past, very little attention was given to the most time-consuming part of the data mining process, i.e. data pre-processing. Objectives: In this paper we propose an algorithm for data cleaning, which can be applied to real-world streaming big data. Methods/Approach: We use the short-term prediction method based on the Kalman filter to detect admissible intervals for future measurements. The model can be adapted to the concept drift and is useful for detecting random additive outliers in a sensor data stream. Results: For datasets with low noise, our method has proven to perform better than the method currently commonly used in batch processing scenarios. Our results on higher noise datasets are comparable. Conclusions: We have demonstrated a successful application of the proposed method in real-world scenarios including the groundwater level, server load and smart-grid data.