Caching stars in the sky: A semantic caching approach to accelerate skyline queries

Abstract

Although multi-criteria decision making has emerged with the advent of skyline queries, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially for non-indexed skyline techniques where the datasets arrive online. In this paper, we propose a caching mechanism that uses the semantics of previous skyline queries to improve the processing time of a new query. In addition to exact queries, such special semantics allow accelerating related queries. We achieve this by generating partial results guaranteed to be in the skyline sets. We also propose an index structure for efficient organization of the cached queries that improve the efficiency. Experiments show the efficiency and scalability of our proposed methods. © 2011 Springer-Verlag Berlin Heidelberg.