Querying spatial data by dominators in neighborhood

Spatial objects in reality are often associated with geographic locations (e.g., longitude and latitude) as well as multiple quality attributes. Quality attributes make it possible to compare spatial objects according to the dominance concept. Specifically, an object p _i is said to dominate another object p _j if p _i is no worse than p _j on all quality attributes and better than p _j on at least one quality attribute. In many contexts, an object's dominators in its neighborhood indicate the negative effect to the object. In this paper, we study the problem of querying spatial objects by their dominators in the neighborhood. We propose three meaningful score functions to quantify the negative effects of dominators in a spatial object's neighborhood. The most endangered object (MEO) query thus defined has multiple practical applications such as business planning, online war games, and wild animal protection. For processing MEO queries, we design several algorithms that require different indexes on spatial data sets. Each algorithm is generic and flexible such that each can support all three score functions (and even more) without significant changes. We conduct extensive experiments to evaluate the algorithms. The experimental results disclose the performance differences of the algorithms under various settings.