无须附加空间的数据立方体联机聚集

以往在数据立方体上实现的联机聚集往往需要附加空间来存储联机聚集估算所需要的信息,极大地影响了数据立方体的存储和维护性能.提出了基于QC-Tree的用于范围查询处理的联机聚集PE(progressively estimate)算法以及它与简单聚集算法相结合的混合聚集算法HPE(hybrid progressively estimate);还提出了一种能够同时处理多个范围查询的联机聚集算法MPE(multiple progressively estimate).与以往联机聚集算法不同,这些算法不需要任何附加空间,而是利用QC-Tree自身保存的聚集数据和语义关系来估算聚集结果.由于QC-Tree是一种极为高效的数据立方体存储结构,因此能够以较理想的性能实现数据立方体上的联机聚集.对算法的分析和实验结果表明,所提出的算法具有较好的性能.

Online Aggregation on Data Cubes Without Auxiliary Information

Typically, online aggregation algorithms on multi-dimensional data need additional auxiliary data for estimation, which make the performance of the storage and maintenance of the data cube worse. This paper presents the PE (progressively estimate) and HPE (hybrid progressively estimate) to progressively estimate the answers for range queries in the QC-Trees. MPE (multiple progressively estimate) is also proposed to simultaneously evaluate batches of range-sum queries. The difference between the algorithms and other online aggregation algorithms on data cubes is that these algorithms do not need any auxiliary information. The idea of this estimation method is to utilize the data stored in the QC-Tree itself. As a result, this algorithm will not deteriorate the performance of the storage and maintenance of the data cubes. Analysis and experimental results show that the algorithms provide an accurate estimation in far less time than the normal algorithms.