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k-Multi-preference query over road networks

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Abstract

Nowadays, the road network has gained more and more attention in the research area of databases. Existing works mainly focus on standalone queries, such as k-nearest neighbor queries over a single type of objects (e.g., facility like restaurant or hotel). In this paper, we propose a k-multi-preference (kMP) query over road networks, involving complex query predicates and multiple facilities. In particular, given a query graph, a kMP query retrieves of the top-k groups of vertices (of k facility types) satisfying the label constraints and their aggregate distances are the smallest. A naïve solution to this problem is to enumerate all combinations of vertices with k possible facility types and then select the one with the minimum sum distance. This method, however, incurs rather high computation cost due to exponential possible combinations. In addition, the existing solutions to other standalone queries are for a single type of facilities and cannot be directly used to answer kMP queries. Therefore, in this paper, we propose an efficient approach to process a kMP query, which utilizes an index with bounded space and reduces the computation cost of the shortest path queries. We also design effective pruning techniques to filter out false alarms. Through our extensive experiments, we demonstrate the efficiency and effectiveness of our proposed solutions.

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References

  1. Mouratidis K, Yiu ML, Papadias D, Mamoulis N (2006) Continuous nearest neighbor monitoring in road networks. In: Proceedings of the 32nd international conference on very large data bases, pp 43–54

  2. Yiu ML, Mamoulis N, Papadias D (2005) Aggregate nearest neighbor queries in road networks. IEEE Trans Knowl Data Eng 17(6):820–833

    Article  Google Scholar 

  3. Mouratidis K, Bakiras S, Papadias D (2006) Continuous monitoring of top-k queries over sliding windows. In: Proceedings of the 2006 ACM SIGMOD international conference on management of data, pp 635–646

  4. Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: Proceedings of the 28th international conference on very large databases, pp 287–298

  5. Kazemi L, Shahabi C, Sharifzadeh M, Vincent L (2007) Optimal traversal planning in road networks with navigational constraints. GIS 19

  6. Yao Bin, Xiao Xiaokui, Li Feifei, Yifan Wu (2014) Dynamic monitoring of optimal locations in road network databases. VLDB J 23(5):697–720

    Article  Google Scholar 

  7. Zhu AD, Ma H, Xiao X, Luo S, Tang Y, Zhou S (2013) Shortest path and distance queries on road networks: towards bridging theory and practice. In: SIGMOD conference, pp 857–868

  8. Luo S, Luo Y, Zhou S, Cong G, Guan J (2012) DISKs: a system for distributed spatial group keyword search on road networks. Proceedings VLDB Endow 5(12):1966–1969

    Article  Google Scholar 

  9. Song R, Sun W, Zheng B, Zheng Y (2014) PRESS: a novel framework of trajectory compression in road networks. Proc VLDB Endow 7(9):661–672

    Article  Google Scholar 

  10. Huang Y, Bastani F, Jin R, Wang XS (2014) Large scale real-time ridesharing with service guarantee on road networks. Proc VLDB Endow 7(14):2017–2028

    Article  Google Scholar 

  11. Xu Z, Jacobsen HA (2010) Processing proximity relations in road networks. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, pp 243–254

  12. Wu L, Xiao X, Deng D, Cong G, Zhu AD, Zhou S (2012) Shortest path and distance queries on road networks: an experimental evaluation. Proc VLDB Endow 5(5):406–417

    Article  Google Scholar 

  13. Xiao X, Yao B, Li F (2011). Optimal location queries in road network databases. In: Data engineering (ICDE), 2011 IEEE 27th international conference, pp 804–815

  14. Chou YH (1996) Exploring spatial analysis in GIS. Onword Press, Santa Fe

    Google Scholar 

  15. Eppstein D, Goodrich MT (2008) Studying (non-planar) road networks through an algorithmic lens. GIS 16

  16. Gallagher B (2006) Matching structure and semantics: a survey on graph-based pattern matching. AAAI FS 6:45–53

    Google Scholar 

  17. Zou L, Chen L, Özsu MT (2009) Distance-join: pattern match query in a large graph database. PVLDB 2(1):886–897

    Google Scholar 

  18. Martínez C, Valiente G (1997) An algorithm for graph pattern-matching. In: Proceedings fourth South American workshop on string processing, vol 8, pp 180–197

  19. Cheng J, Yu JX, Ding B, Yu PS, Wang H (2008) Fast graph pattern matching. In: Data engineering, 2008, ICDE 2008, IEEE 24th international conference, pp 913–922

  20. Huang Y, Bastani F, Jin R, Wang XS (2014) Large scale real-time ridesharing with service guarantee on road networks. PVLDB 7(14):2017–2028

    Google Scholar 

  21. Shahabi C, Kolahdouzan MR, Sharifzadeh M (2003) A road network embedding technique for K-nearest neighbor search in moving object databases. GeoInformatica 7(3):255–273

    Article  Google Scholar 

  22. Thorup M (2004) Compact oracles for reachability and approximate distances in planar digraphs. J ACM 51(6):993–1024

    Article  MathSciNet  MATH  Google Scholar 

  23. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271

    Article  MathSciNet  MATH  Google Scholar 

  24. Goldberg AV, Kaplan H, Werneck RF (2006) Reach for A*: efficient point-to-point shortest path algorithms. ALENEX 129–143

  25. Ahuja RK, Mehlhorn K, Orlin JB, Tarjan RE (1990) Faster algorithms for the shortest path problem. J Assoc Comput Mach 37:213–223

    Article  MathSciNet  MATH  Google Scholar 

  26. Li F, Cheng D, Hadjieleftheriou M, Kollios G, Teng SH (2005) On trip planning queries in spatial databases. SSTD, pp 273–290

Download references

Acknowledgments

This work was supported by NSFC Joint Fund with Guangdong under Key Project No. U1201258, National Natural Science Foundation of China under Grant No. 61573219, and MOE Project of Humanities and Social Sciences (Project Nos. 15YJAZH042, 15JDSZ20527, 15JDSZ2052).

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Peiguang Lin, Yilong Yin & Peiyao Nie

  2. School of Computer Science and Technology, Shandong University, Jinan, China

    Peiguang Lin & Yilong Yin

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  1. Peiguang Lin
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  2. Yilong Yin
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  3. Peiyao Nie
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Correspondence to Peiguang Lin.

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Lin, P., Yin, Y. & Nie, P. k-Multi-preference query over road networks. Pers Ubiquit Comput 20, 413–429 (2016). https://doi.org/10.1007/s00779-016-0913-0

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