SMashQ: spatial mashup framework for k-NN queries in time-dependent road networks

Authors:
Detian Zhang;Chi-Yin Chow;Qing Li;Xinming Zhang;Yinlong Xu
Affiliations:
Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China and Department of Computer Science, City University of Hong Kong, Hong Kong, China and US ...;Department of Computer Science, City University of Hong Kong, Hong Kong, China;Department of Computer Science, City University of Hong Kong, Hong Kong, China and USTC-CityU Joint Advanced Research Center, Suzhou, China;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China and USTC-CityU Joint Advanced Research Center, Suzhou, China;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China and USTC-CityU Joint Advanced Research Center, Suzhou, China
Venue:
Distributed and Parallel Databases
Year:
2013

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Abstract

The k-nearest-neighbor (k-NN) query is one of the most popular spatial query types for location-based services (LBS). In this paper, we focus on k-NN queries in time-dependent road networks, where the travel time between two locations may vary significantly at different time of the day. In practice, it is costly for a LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the best route from a user to a spatial object of interest in terms of the travel time. Thus, we design SMashQ, a server-side spatial mashup framework that enables a database server to efficiently evaluate k-NN queries using the route information and travel time accessed from an external Web mapping service, e.g., Microsoft Bing Maps. Due to the expensive cost and limitations of retrieving such external information, we propose three shared execution optimizations for SMashQ, namely, object grouping, direction sharing, and user grouping, to reduce the number of external Web mapping requests and provide highly accurate query answers. We evaluate SMashQ using Microsoft Bing Maps, a real road network, real data sets, and a synthetic data set. Experimental results show that SMashQ is efficient and capable of producing highly accurate query answers.