一种高度浓缩和语义保持的数据立方

Quotient Cube和QC-tree试图在浓缩一个数据立方尺寸的同时,保持该数据立方蕴涵的语义,但是,前者没有语义关系的存储,后者存储的语义关系是晦涩模糊的.为此提出了下钻立方结构,首次从语义角度考虑数据立方存储,存储的不是类的内容,而是类之间的直接下钻关系.下钻立方不仅能够极大地减小数据立方的存储尺寸,而且可以清晰地表达原数据立方蕴涵的下钻语义.此外,下钻立方具有较高的查询响应性能,这一点在范围查询中表现得尤其显著.实验和分析表明,下钻立方在存储尺寸和查询响应方面明显优于QC-tree,适于用来组织和存储数据立方.

A Highly Condensed and Semantics-Preserving Data Cube

In order to support fast response for ad-hoc and complex OLAP queries, a data cube approach is introduced. Among the various data cube methods proposed in the literature, quotient cube and QC-tree are two important ones, because they try to condense the size of a data cube, while keeping its semantics. However, the former does not store any semantics and the latter stores the semantics in an obscure and implicit manner. To follow this trend and solve the existing problem, drill-down cube is proposed in this paper. Drill-down cube considers the data cube store from the point of view of drill-down semantics, which stores the drill-down semantics between classes, not the content of classes. In a drill-down cube, each class is represented as a node and each direct drill-down relation is captured and represented as an edge between two nodes. The analysis and experiments show that drill-down cube not only reduces the storage size of a data cube dramatically, but also captures the drill-down semantics of the data cube naturally and clearly. The query answering against a drill-down cube, including both point queries and range queries, is also discussed. The key idea behind is to drill down to all target nodes from the root. The query answering of drill-down cube performs fairly well, especially for range queries, and this is verified in the empirical evaluation.