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Continuous Data Stream Query in the Cloud by Jun Li; Peng Zhang; Jianlong Tan; Ping Liu; Li Guo is a scholarly article available to read on EtoBox.

What is Continuous Data Stream Query in the Cloud about?

Cloud computing represents one of the most important research directions for modern computing systems. Existing research efforts on Cloud computing were all focused on designing advanced storage and query techniques for static data. None of them consider the problem that data in a Cloud may appear as continuous and rapid data streams. To address this problem, in this paper we propose a new LCN-Index framework to handle continuous data stream queries in the Cloud. LCN-Index uses the Map-Reduce computing paradigm to process all the queries. In the Mapping stage, it divides all the queries into a batch of predicate sets which are then deployed onto mapping nodes using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multi-attribute hash index. In so doing, a data stream can be efficiently evaluated by traversing through the LCN-Index framework. Experiments demonstrate the utility of the proposed method.

Author

Jun Li; Peng Zhang; Jianlong Tan; Ping Liu; Li Guo

Publisher

ACM

Published

2011

Language

EN