Progressive and selective merge: computing top-k with ad-hoc ranking functions

The family of threshold algorithm (ie, TA) has been widely studied for efficiently computing top-k queries. TA uses a sort-merge framework that assumes data lists are pre-sorted, and the ranking functions are monotone. However, in many database applications, attribute values are indexed by tree-structured indices (eg, B-tree, R-tree), and the ranking functions are not necessarily monotone. To answer top-k queries with ad-hoc ranking functions, this paper studies anindex-merge paradigm that performs progressive search over the space of joint states composed by multiple index nodes. We address two challenges for efficient query processing. First, to minimize the search complexity, we present a double-heap algorithm which supports not only progressive state search but also progressive state generation. Second, to avoid unnecessary disk access, we characterize a type of "empty-state" that does not contribute to the final results, and propose a new materialization model, join-signature, to prune empty-states. Our performance study shows that the proposed method achieves one order of magnitude speed-up over baseline solutions.