Answering why-not questions on metric probabilistic range queries

Abstract

Metric probabilistic range queries (MPRQ) have received substantial attention due to their utility in multimedia and text retrieval, decision making, etc. Existing MPRQ studies generally aim to improve query efficiency and resource usage. In contrast, we define and offer solutions to why-not questions on MPRQ. Given an original metric probabilistic range query and a why-not set W of uncertain objects that are absent from the query result, a why-not question on MPRQ explains why the uncertain objects in W do not appear in the query result, and provides refinements of the original query and/or W with the minimal penalty, so that the uncertain objects in W appear in the result of the refined query. Specifically, we propose a framework that consists of three efficient solutions, one that modifies the original query, one that modifies the why-not set, and one that modifies both the original query and the why-not set. Extensive experiments using both real and synthetic data sets offer insights into the properties of the proposed algorithms, and show that they are effective and efficient.