Efficient maximal reverse skyline query processing

Given a set S of sites and a set O of objects in a metric space, the Optimal Location (OL) problem is about computing a location in the space where introducing a new site (e.g., a retail store) maximizes the number of the objects (e.g., customers) that would choose the new site as their "preferred" site among all sites. However, the existing solutions for the optimal location problem assume that there is only one criterion to determine the preferred site for each object, whereas with numerous real-world applications multiple criteria are used as preference measures. For example, while a single criterion solution might consider the metric distance between the customers and the retail store as the preference measure, a multi-criteria solution might consider the annual membership cost as well as the distance to the retail store to find an optimal location. In this paper, for the first time we develop an efficient and exact solution for the so-called Multi-Criteria Optimal Location (MCOL) problem that can scale with large datasets. Toward that end, first we formalize the MCOL problem as maximal reverse skyline query (MaxRSKY). Given a set of sites and a set of objects in a d-dimensional space, MaxRSKY query returns a location in the space where if a new site s is introduced, the size of the (bichromatic) reverse skyline set of s is maximal. To the best of our knowledge, this paper is the first to define and study MaxRSKY query. Accordingly, we propose a filter-based solution, termed EF-MaxRSKY, that effectively prunes the search space for efficient identification of the optimal location. Our extensive empirical analysis with both real and synthetic datasets show that EF-MaxRSKY is invariably efficient in computing answers for MaxRSKY queries with large datasets containing thousands of sites and objects.