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Distributed stream clustering using micro-clusters on Apache Storm
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DOI：10.1016/j.jpdc.2016.06.004.png)
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Abstract
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The recent need to extract real-time insights from data has driven the need for machine learning algorithms that can operate on data streams. Given the current extreme rates of data generation (around 5000 messages per second), these algorithms need to be able to handle data streams of very high velocity. Many current algorithms do not reach this requirement, in some cases processing only tens of messages per second. In this work we address the problem of limited achievable throughput of stream clustering by developing scalable distributed algorithms based on the micro-clustering paradigm that run on cloud platforms. We present two distributed architectures to execute the algorithms in parallel and implement these architectures on the Apache Storm stream processing platform. We demonstrate that we are able to gain close to an order of magnitude of improvement of performance in our experiments. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Stream data mining
Distributed data mining
Stream clustering
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