Titlebook: Data Warehousing and Knowledge Discovery; 6th International Co Yahiko Kambayashi,Mukesh Mohania,Wolfram W?? Conference proceedings 2004 Spr

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SIT

Answering Approximate Range Aggregate Queries on OLAP Data Cubes with Probabilistic Guaranteesng- based techniques have been proposed to tackle this problem. However, its effectiveness will degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skew but fails to address other requirements of approximate range agg

褲子

Computing Complex Iceberg Cubes by Multiway Aggregation and Boundingruning for constraints. In advanced applications, iceberg constraints are often non-monotone and complex, for example, “Average cost in the range [.., ..] and standard deviation of cost less than .”. The current cubing algorithms either are efficient in aggregation but weak in pruning for such const

WATER

An Aggregate-Aware Retargeting Algorithm for Multiple Fact Data Warehousest be transparent. An aggregate retargeting mechanism redirects a query to available aggregate table(s) whenever possible. In this work, we present an aggregate retargeting mechanism that deals with multiple fact table schemas (MFTS), which are composed of many fact tables related by conformed dimens

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