基于压缩的属性划分存储结构及其上OLAP操作

A compression-based attribute partition storage structure and OLAP queries

信息量的急剧膨胀向数据库工作者提出了挑战。如何有效地管理这些海量数据是学术界和工业界面临的一个重要问题。将数据压缩技术与海量数据仓库有机结合,提出了基于压缩的属性划分存储结构。不仅可以支持压缩数据上的直接操作,而且利用维属性上的布尔运算提高了OLAP操作的性能,减少了计算量。理论分析结果表明,该数据压缩方法可以获得很高的数据压缩比。

The tremendous increase of information give a great challenge to DB researchers. How to organize the massive data efficiently becomes an important issue both on the academic and industry fields. The authors studied the compressing problem on the massive data warehouse, and proposed a storage structure based on compressed attribute partition which compresses the data set by coding the dimension attributes. Not only can it support operations directly on compressed datasets without the need to first decompress them, but also reduce the computation. The boolean operation on dimensional attributes also improves the OLAP performance. The analytical results show that we can obtain very high compression ratio and efficiency by this kind of compression storage structure.