Abstract
Proliferation of ubiquitous smartphones makes location based services prevalent. People carry these devices around everyday and everywhere, which makes mobile volunteered services emerging. As far as we know, little work has been done on the search for mobile spatial textual objects, even though considerable researches have been done on moving objects query and spatial keyword query. In this paper, we study the problem of searching for mobile spatial textual objects in mobile volunteered services: given a set of mobile object and a user query, find the most relevant objects considering both spatial locations and textual descriptions. We model each mobile object as probabilistic instances with time recency. A new hybrid index is proposed for mobile spatial textual objects, called BIG-tree. And we propose an improved threshold algorithm to efficiently process the top-k query based on the index. We evaluate the performance of our approaches on real and synthetic datasets. Experimental results show our solutions outperform the baselines.
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