VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams

Many applications need to solve the following problem of approximate string matching: from a collection of strings, how to find those similar to a given string, or the strings in another (possibly the same) collection of strings? Many algorithms are developed using fixed-length grams, which are substrings of a string used as signatures to identify similar strings. In this paper we develop a novel technique, called VGRAM, to improve the performance of these algorithms. Its main idea is to judiciously choose high-quality grams of variable lengths from a collection of strings to support queries on the collection. We give a full specification of this technique, including how to select high-quality grams from the collection, how to generate variable-length grams for a string based on the preselected grams, and what is the relationship between the similarity of the gram sets of two strings and their edit distance. A primary advantage of the technique is that it can be adopted by a plethora of approximate string algorithms without the need to modify them substantially. We present our extensive experiments on real data sets to evaluate the technique, and show the significant performance improvements on three existing algorithms.