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Stream processing with BigData: SSS-MapReduce

Authors: Hidemoto Nakada; Hirotaka Ogawa; Tomohiro Kudoh;

Stream processing with BigData: SSS-MapReduce

Abstract

We propose a Map Reduce based stream processing system, called SSS, which is capable of processing stream along with large scale static data. Unlike the existing stream processing systems that can work only on the relatively small on-memory data-set, SSS can process incoming streamed data consulting the stored data. SSS processes streamed data with continuous Mappers and Reducers, which are periodically invoked by the system. It also supports merge operation on two sets of data, which enables stream data processing with large static data. This poster shows overview of SSS stream processing and preliminary evaluation results.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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