Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions

TL;DR

This paper introduces a novel top-k query framework that combines attractive and repulsive dimensions in a scoring function, enabling more effective data retrieval and revealing hidden data characteristics.

Contribution

It proposes a new scoring function integrating attractive and repulsive dimensions, along with scalable index structures and empirical validation showing significant performance improvements.

Findings

Performance gain of 10-100x over existing methods

Effective discovery of hidden data characteristics

Versatile application scenarios

Abstract

In this paper, we formulate a top-k query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of attractive and repulsive dimensions into a general framework to overcome the weakness of traditional distance or similarity measures. We study the properties of the proposed class of scoring functions and develop efficient and scalable index structures that index the isolines of the function. We demonstrate various scenarios where the query finds application. Empirical evaluation demonstrates a performance gain of one to two orders of magnitude on querying time over existing state-of-the-art top-k techniques. Further, a qualitative analysis is performed on a real dataset to highlight the potential of the proposed query in discovering hidden data characteristics.