Inverse Queries for Multidimensional Spaces

Abstract

Abstract. Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Our experiments show that our framework is significantly more efficient than naive approaches.

Branch-and-bound algorithm for reverse top-k queries

“…The reverse skyline query [6,13] identifies customers that would be interested in a product based on the dominance of the competitors products. In [3], the authors generalize the concept of reverse queries and propose the inverse queries that take as an input more than one data point. In particular, inverse range queries, inverse k-nearest neighbor queries, and inverse skyline queries are studied.…”

Discovering Influential Data Objects over Time

“…Moreover, in [16] the authors define the distance-based reverse top-k query and monitor its result set for mobile devices, when the values of one dimension (distance) change dynamically as devices move. Reverse queries are also studied in [2] following a unified approach. The authors examine the Inverse ǫ-Range, Inverse k−NN and Inverse Dynamic Skyline queries using a three-filter approach.…”