Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Continuously Maintaining Quantile Summaries of the Most Recent N Elements over a Data Stream
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Effective Computation of Biased Quantiles over Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Power-conserving computation of order-statistics over sensor networks
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Supporting ranking and clustering as generalized order-by and group-by
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Many data stream monitoring applications involve rank queries and hence a number of efficient evaluation algorithms are proposed recently. Most of these techniques assume that rank queries are executed directly over the whole data space. However, we observe that many applications often require to perform clustering over the data streams before rank queries are run on each cluster. To address the problem, we propose a novel algorithm for integral clustering and ranking processing and we refer to such integrated queries as cluster-based rank queries. The algorithm includes two phases, namely the online phase which maintains the required data structures and statistics, and the query phase which uses these data structures to process queries. Extensive experiments indicate that the proposed algorithm is efficient in both space consumption and query processing.