Parallel computing TEDA for high frequency streaming data clustering

Gu, Xiaowei and Angelov, Plamen Parvanov and Gutierrez, German and Iglesias, Jose Antonio and Sanchi, Araceli (2016) Parallel computing TEDA for high frequency streaming data clustering. In: Advances in Big Data. Springer, GRC, pp. 238-253. ISBN 9783319478975

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Abstract

In this paper, a novel online clustering approach called Parallel_TEDA is introduced for processing high frequency streaming data. This newly proposed approach is developed within the recently introduced TEDA theory and inherits all advantages from it. In the proposed approach, a number of data stream processors are involved, which collaborate with each other efficiently to achieve parallel computation as well as a much higher processing speed. A fusion center is involved to gather the key information from the processors which work on chunks of the whole data stream and generate the overall output. The quality of the generated clusters is being monitored within the data processors all the time and stale clusters are being removed to ensure the correctness and timeliness of the overall clustering results. This, in turn, gives the proposed approach a stronger ability of handling shifts/drifts that may take place in live data streams. The numerical experiments performed with the proposed new approach Parallel_TEDA on benchmark datasets present higher performance and faster processing speed when compared with the alternative well-known approaches. The processing speed has been demonstrated to fall exponentially with more data processors involved. This new online clustering approach is very suitable and promising for real-time high frequency streaming processing and data analytics.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
85802
Deposited By:
Deposited On:
03 Apr 2017 15:10
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Oct 2020 07:42