Example-based Spatial Search at Scale

Searching spatial objects is a fundamental task in spatial services such as online maps. Traditional search methods are based on filtering conditions, burdening users to specify their requirements. This paper focuses on spatial search via examples. Particularly, the user can specify an example, which is a set of objects of interest, and the purpose is to find a list of results, each containing a set of objects with similar properties to the given example. We conducted a user study, showing that a search interface based on examples can effectively complement existing approaches. However, the existing example-based search is not scalable, hindering its applications to larger datasets. To address this challenge, we propose two new algorithms, namely HSP and LORA, to efficiently answer example-based spatial queries. HSP is an algorithm based on a hierarchical partitioning of the search space, and it achieves up to 20 times faster than the state-of-the-art algorithm. LORA further improves the efficiency, running up to 5000 times faster than the state-of-the-art algorithm. We present a systematic evaluation to demonstrate the efficacy of our algorithms.