Document reordering for faster intersection

A lot of research has studied how to optimize inverted index structures in search engines through suitable reassignment of document identifiers. This approach was originally proposed to allow for better compression of the index, but subsequent work showed that it can also result in significant speed-ups for conjunctive queries and even certain types of disjunctive top-k algorithms. However, we do not have a good understanding of why this happens, and how we could directly optimize an index for query processing speed. As a result, existing techniques attempt to optimize for size, and treat speed increases as a welcome side-effect. In this paper, we take an initial but important step towards understanding and modeling speed increases due to document reordering. We define the problem of minimizing the cost of queries given an inverted index and a query distribution, relate it to work on adaptive set intersection, and show that it is fundamentally different from that of minimizing compressed index size. We then propose a heuristic algorithm for finding a document reordering that minimizes query processing costs under suitable cost models. Our experiments show significant increases in the speed of intersections over state-of-the-art reordering techniques.