Proceedings 2003 VLDB Conference: 29th International Conference on Very Large Databases (VLDB)Morgan Kaufmann, 2003 M12 2 - 1050 pages Proceedings of the 29th Annual International Conference on Very Large Data Bases held in Berlin, Germany on September 9-12, 2003. Organized by the VLDB Endowment, VLDB is the premier international conference on database technology. |
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... Singapore) A Framework for Clustering Evolving Data Streams ......................................................................... 81 Charu C. Aggarwal (IBM T. J. Watson Resch. Ctr., USA), Jiawei Han, Jianyong Wang ( ...
... Singapore) A Framework for Clustering Evolving Data Streams ......................................................................... 81 Charu C. Aggarwal (IBM T. J. Watson Resch. Ctr., USA), Jiawei Han, Jianyong Wang ( ...
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... for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces ....... 452 Jianjun Zhou, Jörg Sander (University of Alberta, Canada) OLAP & Data Mining Finding Hierarchical Heavy Hitters in Data Streams ...
... for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces ....... 452 Jianjun Zhou, Jörg Sander (University of Alberta, Canada) OLAP & Data Mining Finding Hierarchical Heavy Hitters in Data Streams ...
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... Clustering Abstracting away the details of the problem of histogram-based approximation, one would see some striking similarities with the traditional problem of clustering [73]: the joint data distribution is partitioned into buckets ...
... Clustering Abstracting away the details of the problem of histogram-based approximation, one would see some striking similarities with the traditional problem of clustering [73]: the joint data distribution is partitioned into buckets ...
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... clustering, similarity indexing, etc.) as well as for more focused Web analysis queries. To illustrate the types of queries that a trained information analyst could execute over WebBase, consider the following two examples. In Example 1 ...
... clustering, similarity indexing, etc.) as well as for more focused Web analysis queries. To illustrate the types of queries that a trained information analyst could execute over WebBase, consider the following two examples. In Example 1 ...
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... clusters. Each page cluster represents a set of pages that belong to the same top-level domain (e.g., all pages in a cluster may belong to stanford.edu), have lexicographically similar URLs, and possess similar out-neighborhoods, i.e. ...
... clusters. Each page cluster represents a set of pages that belong to the same top-level domain (e.g., all pages in a cluster may belong to stanford.edu), have lexicographically similar URLs, and possess similar out-neighborhoods, i.e. ...
Contents
17 | |
31 | |
Part 4 Industrial Sessions | 935 |
Part 5 Panels | 1041 |
Part 6 Demo Sessions | 1051 |
Author Index | 1153 |
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Common terms and phrases
ACM SIGMOD algorithm applications approach attribute average bisimulation bucket buffer cache misses clustering compressed compute Conf constraints contains context nodes corresponding cost Data Bubble data mining data set data stream database systems DBLP defined denote distance distributed edge efficient elements engine estimation evaluation example execution experiments Figure function global graph hash join hash table histograms ICDE implementation input integration interface join join algorithm load matching merge algorithm method micro-clusters MJoin operator optimization output PAC-Man Pagerank parameter partition path expression performance predicate probe problem Proc query optimization query plan query processing ranking relation repository retrieval rithm scalability scan schema Section selection semantics sequence server shows SIGMOD storage stored structure subtree techniques tion tree pattern tuples Unicode update VLDB Web.Views window workload XFDs XML document XML query XPath