Efficient Join-Index-Based Join Processing: A Clustering Approach

A Join Index is a data structure used for processing join queries in databases. Join indices use pre-computation techniques to speed up online query processing and are useful for data-sets which are updated infrequently. The cost of join computation using a join-index with limited buuer space depends primarily on the page-access sequence used to fetch the pages of the base relations. Given the join-index, we introduce a suite of methods based on clustering to compute the joins. We derive upper-bounds on the lengths of the page-access sequences. Experimental results with Sequoia 2000 data sets show that the clustering method outperforms the existing methods based on sorting and online-clustering heuristics.