META: An efficient matching-based method for error-tolerant autocompletion

Autocompletion has been widely adopted in many computing systems because it can instantly provide users with results as users type in queries. Since the typing task is tedious and prone to error, especially on mobile devices, a recent trend is to tolerate errors in autocompletion. Existing error-tolerant autocompletion methods build a trie to index the data, utilize the trie index to compute the trie nodes that are similar to the query, called active nodes, and identify the leaf descendants of active nodes as the results. However these methods have two limitations. First, they involve many redundant computations to identify the active nodes. Second, they do not support top-k queries. To address these problems, we propose a matching-based framework, which computes the answers based on matching characters between queries and data. We design a compact tree index to maintain active nodes in order to avoid the redundant computations. We devise an incremental method to effciently answer top-k queries. Experimental results on real datasets show that our method outperforms state-of- the-art approaches by 1-2 orders of magnitude.