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Epsilon 6 0. 7 0. 8 0. 9 0. D10% 60 0 01 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 0. 01 0. 1 0. 2 0. 3 0. Epsilon Fig. 16. T 15I7D2000K: Epsilon vs. mean Probability 4 0. 5 0. Epsilon 6 0. 7 0. 8 0. 9 0. 1% 50 40 T1% D5% D10% 50 40 30 T10% 20 20 20 10 10 10 0 0 01 0. 1% 80 R1% 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 9 01 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 0. 0 01 0. 1 0. Epsilon 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 0. Epsilon Fig. 17. Kosarak: Epsilon vs. mean Probability 6 Conclusions There is a pressing need to have algorithms that handle data in an online fashion with a single scan of data stream whilst operating under resource constraints.

Database cracking. “Database cracking”1 has pioneered focused, incremental, automatic optimization of the representation of a data collection — the more often a key range is queried, the more its representation is optimized for future queries [KM 05, IKM 07a, 07b, 09]. This optimization is entirely automatic. In fact, it is a side effect of queries over key ranges not yet fully optimized. Fig. 10. Column store partitioned by database cracking For example, after the column store illustrated in Figure 10 has been queried with range boundary values c, g, m, s, and u, all key values below c have been assigned to storage locations to the far left, followed by all key values between c and g, etc.

In: IEEE International Conference on Data Mining (ICDM’07), pp. : Multi-dimensional regression analysis of time-series data streams. In: Proceedings of 28th International Conference on Very Large Data Bases, pp. : A new two-phase sampling based algorithm for discovering association rules. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. : Maintaining frequent itemsets over high-speed data streams. In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.

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