<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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</p></abstract>

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

Autonomous Sensor Data Cleaning in Stream Mining Setting

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Autonomous Sensor Data Cleaning in Stream Mining Setting"}

Background: Internet of Things (IoT), earth observation and big scientific experiments are sources of extensive amounts of sensor big data today. We are faced...

Autonomous Sensor Data Cleaning in Stream Mining Setting

Online veröffentlicht: 28. Juli 2018

Seitenbereich: 69 - 79

Eingereicht: 31. Jan. 2018

Akzeptiert: 21. Apr. 2018

DOI: https://doi.org/10.2478/bsrj-2018-0020

Schlüsselwörter
big data, autonomous processing, real-world applications, data cleaning, stream mining, water management, data-centre management, smart-grids

© 2018 Klemen Kenda, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Autonomous Sensor Data Cleaning in Stream Mining Setting

Online veröffentlicht: 28. Juli 2018

Seitenbereich: 69 - 79

Eingereicht: 31. Jan. 2018

Akzeptiert: 21. Apr. 2018

DOI: https://doi.org/10.2478/bsrj-2018-0020

Schlüsselwörterbig data, autonomous processing, real-world applications, data cleaning, stream mining, water management, data-centre management, smart-grids

© 2018 Klemen Kenda, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
big data, autonomous processing, real-world applications, data cleaning, stream mining, water management, data-centre management, smart-grids