Continuous Data Stream Query in the Cloud

Li, J; Zhang, P; Tan, J; Liu, P; Guo, L

Continuous Data Stream Query in the Cloud

Li, J Zhang, P

Tan, J Liu, P Guo, L

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 2389 - 2392
Issue Date:: 2011-01

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Field	Value	Language
dc.contributor.author	Li, J	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Tan, J	en_US
dc.contributor.author	Liu, P	en_US
dc.contributor.author	Guo, L	en_US
dc.contributor.editor	al, BBE	en_US
dc.date	2011-10-24	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 2389 - 2392	en_US
dc.identifier.isbn	978-1-4503-0717-8	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28930
dc.description.abstract	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.	en_US
dc.publisher	ACM	en_US
dc.relation.ispartof	Proceedings of the 20th ACM international conference on Information and knowledge management	en_US
dc.relation.ispartof	ACM international conference on Information and knowledge management	en_US
dc.relation.isbasedon	10.1145/2063576.2063974	en_US
dc.rights	© ACM 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 2389 - 2392 http://doi.acm.org/10.1145/2063576.2063974	en_US
dc.title	Continuous Data Stream Query in the Cloud	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Glasgow, UK	en_US
utslib.for	0805 Distributed Computing	en_US
dc.location.activity	Glasgow, UK	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2011-10-24	en_US

Abstract:

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.

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http://hdl.handle.net/10453/28930