Toward Scalable Indexing for Top-k Queries

Abstract

A top-k query retrieves the best k tuples by assigning scores for each tuple in a target relation with respect to a user-specific scoring function. This paper studies the problem of constructing an indexing structure for supporting top-k queries over varying scoring functions and retrieval sizes. The existing research efforts can be categorized into three approaches: list-, layer-, and view-based approaches. In this paper, we mainly focus on the layer-based approach that pre-materializes tuples into consecutive multiple layers. We first propose a dual-resolution layer that consists of coarse-level and fine-level layers. Specifically, we build coarse-level layers using skylines, and divide each coarse-level layer into fine-level sublayers using convex skylines. To make our proposed dual-resolution layer scalable, we then address the following optimization directions: 1) index construction; 2) disk-based storage scheme; 3) the design of the virtual layer; and 4) index maintenance for tuple updates. Our evaluation results show that our proposed method is more scalable than the state-of-the-art methods.