Our paper entitled “Discovering Mixture-Based Best Regions of Arbitrary Shape” by Dimitrios Skoutas, Dimitris Sacharidis and Kostas Patroumpas was presented at SIGSPATIAL 2021 on November 4th.

Given a collection of geospatial points of different types, mixture-based best region search aims at discovering spatial regions exhibiting either very high or very low mixture with respect to the types of enclosed points. Existing works detect fixed-shape regions, such as circles or rectangles, thus often missing interesting regions occurring in real-world data that may have arbitrary shapes. In this paper, we formulate the problem of mixture-based best region search for arbitrarily shaped regions, introducing certain desired properties to ensure their cohesiveness and completeness. Since computing exact solutions to this problem has exponential cost with respect to the number of points, we propose anytime algorithms that efficiently search the space of candidate solutions to produce high-scoring regions under any given time budget. Our experiments on several real-world datasets show that our algorithms can produce high-quality results even within tight time constraints.