Indexed and distributed processing of similarity selection and join queries

A similarity query is to find from a collection of items those that are similar to a given query item. Answering similarity queries is important in applications such as DNA sequence assembly, record linkage, and query relaxation, where errors could occur in queries as well as the data. The wide relevance of similarity queries presents to us application-specific constraints and desiderata. In this thesis, we develop and evaluate indexes and algorithms for answering such queries efficiently in the context of four different settings. First, we present a DNA-specific alignment method whose primary focus is on the speed of query execution. Our results show a performance improvement of 2-10X compared to existing state-of-the-art packages. Second, we develop a flexible compression technique for reducing the size of an inverted index to a given amount of space while retaining efficient query processing. In our experimental evaluation we could reduce the index size up to 60% without sacrificing query response times. Third, we study external-memory solutions well-suited for database management systems, where the data and indexes are stored on disk, and demonstrate a substantial benefit over alternative methods. Fourth, we discuss how we incorporated some of our solutions into the ASTERIX parallel database system to optimize similarity queries via secondary indexes. We elaborate on our design choices and query-processing capabilities, and conclude with experiments on large-scale data.