Scalable collective spatial keyword query

Peijun He, Hao Xu, Xiang Zhao, Zhitao Shen

Abstract: Spatial keyword queries have been widely studied recently, along with the emergence of large amount of geo-textual data. We consider the problem of scalable collective spatial keyword queries in this paper. Such query has a wide spectrum of applications; for instance, to find the best (nearest) area to organize a friend get-together where bars, restaurants and accommodations are nearby, and compose a group of members from different professional domains, e.g., computing, accounting, etc, for a specific task, etc. While existing algorithms processes the queries well, we observe their shortcomings in handling large-scale datasets. To this end, we propose a distributed solution following Spark programming paradigm. Moreover, a grid-based optimization technique is further proposed to enhance the efficiency. Extensive experiments on various datasets confirm that the proposed algorithm efficiently solves the problem at scale.