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Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach

Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach

Harshad Dattatray Markad, S. M. Sangve
Copyright: © 2017 |Volume: 8 |Issue: 2 |Pages: 13
ISSN: 1947-9093|EISSN: 1947-9107|EISBN13: 9781522513384|DOI: 10.4018/IJSE.2017070102
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MLA

Markad, Harshad Dattatray, and S. M. Sangve. "Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach." IJSE vol.8, no.2 2017: pp.25-37. http://doi.org/10.4018/IJSE.2017070102

APA

Markad, H. D. & Sangve, S. M. (2017). Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach. International Journal of Synthetic Emotions (IJSE), 8(2), 25-37. http://doi.org/10.4018/IJSE.2017070102

Chicago

Markad, Harshad Dattatray, and S. M. Sangve. "Parallel Outlier Detection for Streamed Data Using Non-Parameterized Approach," International Journal of Synthetic Emotions (IJSE) 8, no.2: 25-37. http://doi.org/10.4018/IJSE.2017070102

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Abstract

Outlier detection is used in various applications like detection of fraud, network analysis, monitoring traffic over networks, manufacturing and environmental software. The data streams which are generated are continuous and changing over time. This is the reason why it becomes nearly difficult to detect the outliers in the existing data which is huge and continuous in nature. The streamed data is real time and changes over time and hence it is impractical to store data in the data space and analyze it for abnormal behavior. The limitations in data space has led to the problem of real time analysis of data and processing it in FCFS basis. The results regarding the abnormal behavior have to be done very quickly and in a limited time frame and on an infinite set of data streams coming over the networks. To address the problem of detecting outliers on a real-time basis is a challenging task and hence has to be monitored with the help of the processing power used to design the graphics of any processing unit. The algorithm used in this paper uses a kernel function to accomplish the task. It produces timely outcome on high speed multi- dimensional data. This method increases the speed of outlier detection by 20 times and the speed goes on increasing with the increase with the number of data attributes and input data rate.

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