Fast similarity search in string databases

Efficient similarity search in large string databases requires effective index support. Since long strings have each numerous substrings of arbitrary length, the effective index designs are of great challenge. The existing solution, namely MRS [T. Kahveci et al., (2001)], employs a low-cost lower bound function to sieve out the most similar candidates from the majority of unlikely database substrings. Therefore, only very small portions of string databases require the expensive true edit distance computation to finalize the query. A significant savings in overall query processing cost can be realized by the filtration feature of lower bound functions. In this paper, we seek to improve MRS to its full potential. Specifically, we propose a very simple method that exchanges the roles of database strings and query string in the original MRS design. Despite simplicity, our solution can further improve the query performance by 10 times in terms of disk page accesses while using only half of the original index´s size.