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Titlebook: Advances in Database Technology - EDBT ‘98; 6th International Co Hans-J?rg Schek,Gustavo Alonso,Isidro Ramos Conference proceedings 1998 Sp

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樓主: Sediment
31#
發(fā)表于 2025-3-26 22:43:54 | 只看該作者
An evaluation of alternative disk scheduling techniques in support of variable bit rate continuous 978-3-642-58337-7
32#
發(fā)表于 2025-3-27 03:36:07 | 只看該作者
33#
發(fā)表于 2025-3-27 07:09:56 | 只看該作者
34#
發(fā)表于 2025-3-27 10:18:55 | 只看該作者
35#
發(fā)表于 2025-3-27 14:06:12 | 只看該作者
Complex aggregation at multiple granularities,ion taking place via an information system, such an agreement is called a "grammar" or a "conceptual schemea" of this information system. "Information analysis" is that field and also that phase of development of an information system in which the grammar or the conceptual schemea of the information
36#
發(fā)表于 2025-3-27 20:14:08 | 只看該作者
Jacob W. P. Keeling,Rita K. Millerch is used only for maintaining and updating a new data structure we designed, the maximum frequent candidate set. It is used to prune candidates in the bottom-up search. A very important characteristic of the algorithm is that it does not require explicite examination of every frequent itemset. The
37#
發(fā)表于 2025-3-28 01:02:28 | 只看該作者
Jongkuen Lee,David Dominguez-Solaperiments demonstrate that incremental generalization can be performed efficiently at a low degree of overly generalization. Furthermore, an optimal cardinality for the sets of updates can be determined experimentally yielding the best efficiency.
38#
發(fā)表于 2025-3-28 03:21:47 | 只看該作者
39#
發(fā)表于 2025-3-28 06:49:11 | 只看該作者
Tong Li,Yannick Gachet,Sylvie Tournier%-quantile, results in a bad query performance in high-dimensional spaces. Therefore, we use a sophisticated unbalanced split strategy, which leads to a much better space partitioning. An exhaustive experimental evaluation shows that our technique clearly outperforms both classic index construction
40#
發(fā)表于 2025-3-28 12:16:32 | 只看該作者
Eishi Noguchi,Mariana C. Gadaletaportant because, as we show, existing datacube evaluation algorithms can be used to compute multi-feature cubes that are distributive or algebraic, without any increase in I/O complexity. We evaluate the CPU performance of computing multi-feature cubes using the datacube evaluation algorithm of Ross
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