Similarity query processing using disk arrays

Similarity queries are fundamental operations that are used extensively in many modern applications, whereas disk arrays 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 technique which is based on a careful investigation of the currently 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 simulation. The performance results conducted, demonstrate that the proposed approach outperforms by factors a previous 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.