Efficient processing of substring match queries with inverted q-gram indexes

With the widespread of the internet, text-based data sources have become ubiquitous and the demand of effective support for string matching queries becomes ever increasing. The relational query language SQL also supports LIKE clause over string data to handle substring matching queries. Due to popularity of such substring matching queries, there have been a lot of study on designing efficient indexes to support the LIKE clause in SQL. Among them, q-gram based indexes have been studied extensively. However, how to process substring matching queries efficiently with such indexes has received very little attention until recently. In this paper, we show that the optimal execution of intersecting posting lists of q-grams for substring matching queries should be decided judiciously. Then we present the optimal and approximate algorithms based on cost estimation for substring matching queries. Performance study confirms that our techniques improve query execution time with q-gram indexes significantly compared to the traditional algorithms.