Abstract Similarity Query Processing Using Disk Arrays*

Similarity queries are fundamental operations that are used extensively in many modern applications, whereas disk ar-rays are powerful storage media of increasing importance. The basic trade-off in similarity query processing in such a system is that increased parallelism leads to higher resource consumptions and low throughput, whereas low parallelism leads to higher response times. Here, we propose a tech-nique which is based on a careful investigation of the cur-rently available data in order to exploit parallelism up to a point, retaining low response times during query processing. The underlying access method is a variation of the R*-tree, which is distributed among the components of a disk array, whereas the system is simulated using event-driven simu-lation. The performance results conducted, demonstrate that the proposed approach outperforms by factors a pre-vious branch-and-bound algorithm and a greedy algorithm which maximizes parallelism as much as possible. Moreover, the comparison of the proposed algorithm to a hypothetical (non-existing) optimal one (with respect to the number of disk accesses) shows that the former is on average two times slower than the latter.