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Aggregate keyword nearest neighbor queries on road networks

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Abstract

Given a group \(\mathcal {Q}\) of query points and a set \(\mathcal {P}\) of points of interest (POIs), aggregate nearest neighbor (ANN) queries find a POI p from \(\mathcal {P}\) that achieves the smallest aggregate distance. Specifically, the aggregate distance is defined over the set of distances between p and all query points in \(\mathcal {Q}\). Existing studies on ANN query mainly consider the spatial proximity, whereas the textual similarity has received considerable attention recently. In this work, we utilize user-specified query keywords to capture textual similarity. We study the aggregate keyword nearest neighbor (AKNN) queries, finding the POI that has the smallest aggregate distance and covers all query keywords. Nevertheless, existing methods on ANN query are either inapplicable or inefficient when applied to the AKNN query. To answer our query efficiently, we first develop a dual-granularity (DG) indexing schema. It preserves abstracts of the road network by a tree structure, and preserves detailed network information by an extended adjacency list. Then, we propose a minimal first search (MFS) algorithm. It traverses the tree and explores the node with the minimal aggregate distance iteratively. This method suffers from false hits arising from keyword tests. Thus, we propose the collaborative filtering technique, which performs keywords test by multiple keyword bitmaps collectively rather than by only one. Extensive experiments on both real and synthetic datasets demonstrate the superiority of our algorithms and optimizing strategies.

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References

  1. Abbasifard MR, Ghahremani B, Naderi H (2014) A survey on nearest neighbor search methods. International Journal of Computer Applications 95(25):39–52

    Article  Google Scholar 

  2. Ahmadi E, Nascimento MA (2016) k-optimal meeting points based on preferred paths. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, p 47

  3. Bao J, Liu X, Zhou R, Wang B (2016) Keyword-aware optimal location query in road network. In: WAIM. Springer, pp 164–177

  4. Cao X, Cong G, Jensen CS, Ooi BC (2011) Collective spatial keyword querying. In: SIGMOD. ACM, pp 373–384

  5. Cao X, Cong G, Guo T, Jensen CS, Ooi BC (2015) Efficient processing of spatial group keyword queries. TODS 40(2):13

    Article  Google Scholar 

  6. Chen K, Sun W, Tu C, Chen C, Huang Y (2012) Aggregate keyword routing in spatial database. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, pp 430–433

  7. Choi DW, Pei J, Lin X (2016) Finding the minimum spatial keyword cover. In: ICDE. IEEE, pp 685–696

  8. Deng K, Sadiq S, Zhou X, Xu H, Fung GPC, Lu Y (2012) On group nearest group query processing. TKDE 24(2):295–308

    Google Scholar 

  9. Elmongui HG, Mokbel MF, Aref WG (2013) Continuous aggregate nearest neighbor queries. GeoInformatica 17(1):63–95

    Article  Google Scholar 

  10. Gao Y, Qin X, Zheng B, Chen G (2015) Efficient reverse top-k boolean spatial keyword queries on road networks. TKDE 27(5):1205–1218

    Google Scholar 

  11. Gao Y, Zhao J, Zheng B, Chen G (2016) Efficient collective spatial keyword query processing on road networks. TITS 17(2):469–480

    Google Scholar 

  12. Guo T, Cao X, Cong G (2015) Efficient algorithms for answering the m-closest keywords query. In: SIGMOD. ACM, pp 405–418

  13. Guttman A (1984) R-trees: a dynamic index structure for spatial searching, vol 14. ACM

  14. Houle ME, Ma X, Oria V (2015) Effective and efficient algorithms for flexible aggregate similarity search in high dimensional spaces. TKDE 27(12):3258–3273

    Google Scholar 

  15. Karypis G, Kumar V (1995) Analysis of multilevel graph partitioning. In: Proceedings of the 1995 ACM/IEEE conference on Supercomputing. ACM, p 29

  16. Li F, Yi K, Tao Y, Yao B, Li Y, Xie D, Wang M (2016) Exact and approximate flexible aggregate similarity search. VLDB J 25(3):317–338

    Article  Google Scholar 

  17. Li M, Chen L, Cong G, Gu Y, Yu G (2016) Efficient processing of location-aware group preference queries. In: CIKM. ACM, pp 559–568

  18. Li Y, Li F, Yi K, Yao B, Wang M (2011) Flexible aggregate similarity search. In: SIGMOD. ACM, pp 1009–1020

  19. Li Z, Xu H, Lu Y, Qian A (2010) Aggregate nearest keyword search in spatial databases. In: APWEB. IEEE, pp 15–21

  20. Long C, Wong RCW, Wang K, Fu AWC (2013) Collective spatial keyword queries: a distance owner-driven approach. In: SIGMOD. ACM, pp 689–700

  21. Luo Y, Chen H, Furuse K, Ohbo N (2007) Efficient methods in finding aggregate nearest neighbor by projection-based filtering. ICCSA pp 821–833

  22. Luo Y, Furuse K, Chen H, Ohbo N (2007) Finding aggregate nearest neighbor efficiently without indexing. In: Proceedings of the 2nd international conference on Scalable information systems. ICST, p 48

  23. Papadias D, Shen Q, Tao Y, Mouratidis K (2004) Group nearest neighbor queries. In: ICDE. IEEE, pp 301–312

  24. Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. TODS 30(2):529–576

    Article  Google Scholar 

  25. Rocha-Junior JB, Nørvåg K (2012) Top-k spatial keyword queries on road networks. In: Proceedings of the 15th international conference on extending database technology. ACM, pp 168–179

  26. Sadasivam S, Baba AI, Ku WS, Chen H (2015) A2n2: approximate aggregate nearest neighbor queries on road networks. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems. ACM, pp 13–22

  27. Shekhar S, Liu DR (1997) Ccam: a connectivity-clustered access method for networks and network computations. TKDE 9(1):102–119

    Google Scholar 

  28. Singh V, Zong B, Singh AK (2016) Nearest keyword set search in multi-dimensional datasets. TKDE 28(3):741–755

    Google Scholar 

  29. Su S, Zhao S, Cheng X, Bi R, Cao X, Wang J (2017) Group-based collective keyword querying in road networks. Inf Process Lett 118:83–90

    Article  Google Scholar 

  30. Sun W, Chen C, Zheng B, Chen C, Zhu L, Liu W, Huang Y (2013) Merged aggregate nearest neighbor query processing in road networks. In: CIKM. ACM, pp 2243–2248

  31. Sun W, Chen C, Zheng B, Chen C, Zhu L, Liu W, Huang Y (2015) Fast optimal aggregate point search for a merged set on road networks. Inform Sci 310:52–68

    Article  Google Scholar 

  32. Sun WW, Chen CN, Zhu L, Gao YJ, Jing YN, Li Q (2015) On efficient aggregate nearest neighbor query processing in road networks. JCST 30(4):781–798

    Google Scholar 

  33. Yan D, Zhao Z, Ng W (2011) Efficient algorithms for finding optimal meeting point on road networks. Proceedings of the VLDB Endowment 4(11)

  34. Yan D, Zhao Z, Ng W (2015) Efficient processing of optimal meeting point queries in euclidean space and road networks. KIS 42(2):319–351

    Google Scholar 

  35. Yiu ML, Mamoulis N, Papadias D (2005) Aggregate nearest neighbor queries in road networks. TKDE 17(6):820–833

    Google Scholar 

  36. Zhang D, Chee YM, Mondal A, Tung AK, Kitsuregawa M (2009) Keyword search in spatial databases: Towards searching by document. In: ICDE. IEEE, pp 688–699

  37. Zhang D, Ooi BC, Tung AK (2010) Locating mapped resources in web 2.0. In: ICDE. IEEE, pp 521–532

  38. Zhao S, Cheng X, Su S, Shuang K (2017) Popularity-aware collective keyword queries in road networks. GeoInformatica 21, pp 1–34

  39. Zhong R, Li G, Tan KL, Zhou L, Gong Z (2015) G-tree: an efficient and scalable index for spatial search on road networks. TKDE 27(8):2175–2189

    Google Scholar 

  40. Zhu L, Jing Y, Sun W, Mao D, Liu P (2010) Voronoi-based aggregate nearest neighbor query processing in road networks. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, pp 518–521

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Acknowledgements

This work was supported by National Program on Key Basic Research Project (i.e., 973 Program) No.2012CB725305, the NSFC Grant No.61428204, the Scientific Innovation Act of STCSM No.13511504200 and No.15JC1402400, National Science and Technology Supporting plan No.2015BAH45F01, the public key plan of Zhejiang Province No.2014C23005, the cultural relic protection science and technology project of Zhejiang Province.

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  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Pengfei Zhang, Huaizhong Lin, Yunjun Gao & Dongming Lu

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  1. Pengfei Zhang
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  2. Huaizhong Lin
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  3. Yunjun Gao
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  4. Dongming Lu
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Correspondence to Huaizhong Lin.

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Zhang, P., Lin, H., Gao, Y. et al. Aggregate keyword nearest neighbor queries on road networks. Geoinformatica 22, 237–268 (2018). https://doi.org/10.1007/s10707-017-0315-0

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