In this paper, we propose route of interest (ROI) query which allows users to specify their interests with query keywords and returns a route such that (i) its distance is less than a distance threshold and (ii) its relevance to the query keywords is maximized. ROI query is particularly helpful for tourists and city explorers. For example, a tourist may wish to find a route from a scenic spot to her hotel to cover many artware shops. It is challenging to efficiently answer ROI query due to its NP-hard complexity. Novelly, we propose an adaptive route sampling framework that adaptively computes a route according to a given response time, and gradually improve the quality of the route with time. Moreover, we design a suite of route sampling techniques under this framework. Experiments on real data suggest that our proposed solution can return high quality routes within a short response time.
CITATION STYLE
Li, W., Cao, J., Guan, J., Yiu, M. L., & Zhou, S. (2016). Retrieving routes of interest over road networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9658, pp. 109–123). Springer Verlag. https://doi.org/10.1007/978-3-319-39937-9_9
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