On-the-Fly Token Similarity Joins in Relational Databases

Token similarity joins represent data items as sets of tokens, for example, strings are represented as sets of q-grams (sub-strings of length q). Two items are considered similar and match if their token sets have a large overlap. Previous work on similarity joins in databases mainly focuses on expressing the overlap computation with relational operators. The tokens are assumed to preexist in the database, and the token generation cannot be expressed as part of the query. Our goal is to efficiently compute token similarity joins on-the-fly, i.e., without any precomputed tokens or indexes. We define tokenize, a new relational operator that generates tokens and allows the similarity join to be fully integrated into relational databases. This allows us to (1) optimize the token generation as part of the query plan, (2) provide the query optimizer with cardinality estimates for tokens, (3) choose efficient algorithms based on the query context. We discuss algebraic properties, cardinality estimates, and an efficient iterator algorithm for tokenize. We implemented our operator in the kernel of PostgreSQL and empirically evaluated its performance for similarity joins.