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

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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.

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References

  1. Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web-accessible databases. In: Proceedings of the IEEE International Conference on Data Engineering (2002)

    Google Scholar 

  2. Chang, K.C.C., Hwang, S.W.: Minimal probing: supporting expensive predicates for top-k queries. In: Proceedings of the ACM Conference on Management of Data (2002)

    Google Scholar 

  3. Chow, C.Y., Mokbel, M.F., Bao, J., Liu, X.: Query-aware location anonymization for road networks. GeoInformatica 15(3), 571–607 (2011)

    Article  Google Scholar 

  4. Chow, C.Y., Mokbel, M.F., Leong, H.V.: On efficient and scalable support of continuous queries in mobile peer-to-peer environments. IEEE Trans. Mob. Comput. 10(10), 1473–1487 (2011)

    Article  Google Scholar 

  5. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)

    MATH  Google Scholar 

  6. Demiryurek, U., Banaei-Kashani, F., Shahabi, C.: Efficient k-nearest neighbor search in time-dependent spatial networks. In: Proceedings of the International Conference on Database and Expert Systems Applications (2010)

    Google Scholar 

  7. Demiryurek, U., Banaei-Kashani, F., Shahabi, C.: Towards k-nearest neighbor search in time-dependent spatial network databases. In: International Workshop on Databases in Networked Systems (2010)

    Google Scholar 

  8. Fu, T.Y., Peng, W.C., Lee, W.C.: Parallelizing itinerary-based KNN query processing in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 22(5), 711–729 (2010)

    Article  Google Scholar 

  9. Gedik, B., Liu, L.: Mobieyes: a distributed location monitoring service using moving location queries. IEEE Trans. Mob. Comput. 5(10), 1384–1402 (2006)

    Article  Google Scholar 

  10. George, B., Kim, S., Shekhar, S.: Spatio-temporal network databases and routing algorithms: a summary of results. In: Proceedings of the International Symposium on Spatial and Temporal Databases (2007)

    Google Scholar 

  11. Google Maps: http://maps.google.com

  12. Google Maps/Google Earth APIs Terms of Service: http://code.google.com/apis/maps/terms.html

  13. Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: Proceedings of the ACM Conference on Management of Data (2005)

    Google Scholar 

  14. Huang, X., Jensen, C.S., Saltenis, S.: The islands approach to nearest neighbor querying in spatial networks. In: Proceedings of the International Symposium on Spatial and Temporal Databases (2005)

    Google Scholar 

  15. Jensen, C.S.: Database aspects of location-based services. In: Location-Based Services, pp. 115–148. Morgan Kaufmann, San Mateo (2004)

    Chapter  Google Scholar 

  16. Kanoulas, E., Du, Y., Xia, T., Zhang, D.: Finding fastest paths on a road network with speed patterns. In: Proceedings of the IEEE International Conference on Data Engineering (2006)

    Google Scholar 

  17. Lee, D.L., Zhu, M., Hu, H.: When location-based services meet databases. Mob. Inf. Syst. 1(2), 81–90 (2005)

    Google Scholar 

  18. Levandoski, J.J., Mokbel, M.F., Khalefa, M.E.: Preference query evaluation over expensive attributes. In: Proceedings of the International Conference on Information and Knowledge Management (2010)

    Google Scholar 

  19. Microsoft Bing Maps: http://www.bing.com/maps/

  20. Mokbel, M.F., Xiong, X., Aref, W.G.: SINA: scalable incremental processing of continuous queries in spatio-temporal databases. In: Proceedings of the ACM Conference on Management of Data (2004)

    Google Scholar 

  21. Mokbel, M.F., Xiong, X., Hammad, M.A., Aref, W.G.: Continuous query processing of spatio-temporal data streams in PLACE. GeoInformatica 9(4), 343–365 (2005)

    Article  Google Scholar 

  22. Mouratidis, K., Papadias, D., Hadjieleftheriou, M.: Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of the ACM Conference on Management of Data (2005)

    Google Scholar 

  23. Nehme, R.V., Rundensteiner, E.A.: SCUBA: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects. In: Proceedings of the International Conference on Extending Database Technology (2006)

    Google Scholar 

  24. Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: Proceedings of the International Conference on Very Large Data Bases (2003)

    Google Scholar 

  25. Samet, H., Sankaranarayanan, J., Alborzi, H.: Scalable network distance browsing in spatial databases. In: Proceedings of the ACM Conference on Management of Data (2008)

    Google Scholar 

  26. Tanin, E., Harwood, A., Samet, H.: Using a distributed quadtree index in peer-to-peer networks. VLDB J. 16(2), 165–178 (2007)

    Article  Google Scholar 

  27. Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: Proceedings of the International Conference on Very Large Data Bases (2002)

    Google Scholar 

  28. The Google Distance Matrix API (Last visited: May 18, 2012): http://developers.google.com/maps/documentation/distancematrix/

  29. TIGER/Line Shapefiles 2009 for: Hennepin County, Minnesota: (2009). http://www2.census.gov/cgi-bin/shapefiles2009/county-files?county=27053

  30. Vancea, A., Grossniklaus, M., Norrie, M.C.: Database-driven web mashups. In: IEEE ICWE (2008)

    Google Scholar 

  31. Wu, S.H., Chuang, K.T., Chen, C.M., Chen, M.S.: DIKNN: an itinerary-based KNN query processing algorithm for mobile sensor networks. In: Proceedings of the IEEE International Conference on Data Engineering (2007)

    Google Scholar 

  32. Yahoo! Maps: http://maps.yahoo.com

  33. Zhang, D., Chow, C.Y., Li, Q., Zhang, X., Xu, Y.: Efficient evaluation of k-NN queries using spatial mashups. In: Proceedings of the International Symposium on Spatial and Temporal Databases (2011)

    Google Scholar 

  34. Zhu, Q., Lee, D.L., Lee, W.C.: Collaborative caching for spatial queries in mobile P2P networks. In: Proceedings of the IEEE International Conference on Data Engineering (2011)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Detian Zhang & Xinming Zhang

  2. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Detian Zhang, Chi-Yin Chow & Qing Li

  3. USTC-CityU Joint Advanced Research Center, Suzhou, China

    Detian Zhang, Qing Li & Xinming Zhang

Authors
  1. Detian Zhang
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  2. Chi-Yin Chow
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  3. Qing Li
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  4. Xinming Zhang
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  5. Yinlong Xu
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Corresponding author

Correspondence to Chi-Yin Chow.

Additional information

The work described in this paper was partially supported by grants from City University of Hong Kong (Project No. 7002686 and 7002606) and the National Natural Science Foundation of China under Grant No. 61073185 and 61073038.

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Zhang, D., Chow, CY., Li, Q. et al. SMashQ: spatial mashup framework for k-NN queries in time-dependent road networks. Distrib Parallel Databases 31, 259–287 (2013). https://doi.org/10.1007/s10619-012-7110-6

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  • DOI: https://doi.org/10.1007/s10619-012-7110-6

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