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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Page 41
... data sets. Our costbased optimizer is similar in spirit to the query ... set (e.g., the piece could be all pages in domains X and Y related to topic ... data set in the repository into page clusters. Each page cluster represents a set of ...
... data sets. Our costbased optimizer is similar in spirit to the query ... set (e.g., the piece could be all pages in domains X and Y related to topic ... data set in the repository into page clusters. Each page cluster represents a set of ...
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... set of navigation operators {N1, , , , , Ng), the optimizer explores a ... data from the cluster-level histogram of PageRank distribution). Second ... data set (approximately 600 million links, 300 GB of HTML). Queries Q1 to Q4 correspond ...
... set of navigation operators {N1, , , , , Ng), the optimizer explores a ... data from the cluster-level histogram of PageRank distribution). Second ... data set (approximately 600 million links, 300 GB of HTML). Queries Q1 to Q4 correspond ...
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... data stream domain. This is because the large volumes of data arriving in a stream renders most traditional ... set of data points, we wish to partition them into one or more groups of similar objects. The similarity of the objects with ...
... data stream domain. This is because the large volumes of data arriving in a stream renders most traditional ... set of data points, we wish to partition them into one or more groups of similar objects. The similarity of the objects with ...
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... data stream clustering algorithm must provide the flexibility to compute ... set of multi-dimensional records X1 ..., X, ... arriving at time stamps T1 ... set dimensional points Xi, ... X in T, ...T., is defined as the (2 - d -- 82.
... data stream clustering algorithm must provide the flexibility to compute ... set of multi-dimensional records X1 ..., X, ... arriving at time stamps T1 ... set dimensional points Xi, ... X in T, ...T., is defined as the (2 - d -- 82.
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... data arriving between (t2 – h, t2) are compared to those created by the data arriving between (t1 – h, ti). Another ... set of micro-clusters by M**(t1, t2). • Micro-clusters in W(t1, h) for which none of the corresponding ids are present in ...
... data arriving between (t2 – h, t2) are compared to those created by the data arriving between (t1 – h, ti). Another ... set of micro-clusters by M**(t1, t2). • Micro-clusters in W(t1, h) for which none of the corresponding ids are present in ...
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