在龐大的字串資料庫中做有效相似搜尋需要有效能的索引結構的支持，當我們要搜尋的字串可以是任意長度的時，好的索引結構設計變成一個極大挑戰，MRS是現有的方法提供一個低計算量的下限函數，讓我們能夠有效率的在資料庫中取出擁有較相似子字串的候選人，因此，我們只需要少部分的資料庫中的字串做真實昂貴edit distance的計算以便得到最後的解答，利用下限函數的過濾大大的降低查詢處理的時間，而我們的方法，主要是希望提升MRS的方法的效能。我們方法事實上只是改變原本MRS中資料庫的字串和查詢字串的角色，僅管簡單，但是我們的方法減少了10倍硬碟分頁的存取，而我們的索引結構所需的記憶體使用量只要原來MRS的一半。

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 [11], 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.
Keywords: String Index, Similarity Search, Edit Distance, Near Neighbor Query

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