Range Query Processing in OLAP Skyline Cubes

Abstract

Skyline cube (SC) is an extension of data cube, where the skyline operation is used to aggregate each cell of tuples with the same values of dimension attributes. To make SC more effective decision making tool, range query regarding SC is potentially promising. This paper discusses the storage structure of SC and range query processing system. R-tree based storage structure is dedicated to multi-dimensional index which associates values of dimension attributes with the corresponding results of extended skyline operation. The sequential processing system and the parallel processing system are built upon R-tree based storage structure to answer range queries. Experimental results show that R-tree based storage structure is allowable, because its storage size is at most 3 times as that of materialized view. Also, both of the sequential processing system and the parallel processing system are superior to the materialized view system in processing range queries. Especially, the parallel processing system takes below 20% times as that of the materizalized view system, even in the case of range query including anti-correlation skyline attributes which entail heavy burdens.