Towards best region search for data exploration

The increasing popularity and growth of mobile devices and locationbased services enable us to utilize large-scale geo-tagged data to support novel location-based applications. This paper introduces a novel problem called the best region search (BRS) problem and provides efficient solutions to it. Given a set O of spatial objects, a submodular monotone aggregate score function, and the size a × b of a query rectangle, the BRS problem aims to find a×b rectangular region such that the aggregate score of the spatial objects inside the region is maximized. This problem is fundamental to support several real-world applications such as most influential region search (e.g., the best location for a signage to attract most audience) and most diversified region search (e.g., region with most diverse facilities). We propose an efficient algorithm called SliceBRS to find the exact answer to the BRS problem. Furthermore, we propose an approximate solution called CoverBRS and prove that the answer found by it is bounded by a constant. Our experimental study with real-world datasets and applications demonstrates the effectiveness and superiority of our proposed algorithms.