Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
StreaMon: an adaptive engine for stream query processing
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Proceedings of the VLDB Endowment
Indexing multi-dimensional data in a cloud system
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Group detection and relation analysis research for web social network
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
ComMapReduce: An improvement of MapReduce with lightweight communication mechanisms
Data & Knowledge Engineering
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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.