Indexing methods for approximate dictionary searching

The primary goal of this article is to survey state-of-the-art indexing methods for approximate dictionary searching. To improve understanding of the field, we introduce a taxonomy that classifies all methods into direct methods and sequence-based filtering methods. We focus on infrequently updated dictionaries, which are used primarily for retrieval. Therefore, we consider indices that are optimized for retrieval rather than for update. The indices are assumed to be associative, that is, capable of storing and retrieving auxiliary information, such as string identifiers. All solutions are lossless and guarantee retrieval of strings within a specified edit distance k . Benchmark results are presented for the practically important cases of k =1, 2, and 3. We concentrate on natural language datasets, which include synthetic English and Russian dictionaries, as well as dictionaries of frequent words extracted from the ClueWeb09 collection. In addition, we carry out experiments with dictionaries containing DNA sequences. The article is concluded with a discussion of benchmark results and directions for future research.