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Efficient location-based search of trajectories with location importance

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Abstract

Given a database of trajectories and a set of query locations, location-based trajectory search finds trajectories in the database that are close to all the query locations. Location-based trajectory search has many applications such as providing reference routes for travelers who are planning a trip to multiple places of interest. However, previous studies only consider the spatial aspect of trajectories, which is inadequate for real applications. For example, one may obtain the reference route of a tourist who just passed by a place of interest without paying a visit. We propose the \(k\) Important Connected Trajectories (k-ICT) query by associating trajectories with location importance. For any query location, the result trajectories should contain an important point close to it. We describe an effective method to infer the importance of trajectory points from the temporal information. We also propose efficient R-tree-based and grid-based algorithms to answer \(k\)-ICT queries, and verify the efficiency of our algorithms through extensive experiments on both real and synthetic datasets.

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Notes

  1. http://www.chorochronos.org/?q=node/5.

  2. http://www.chorochronos.org/?q=node/6.

  3. http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx.

  4. http://research.microsoft.com/apps/pubs/?id=152883.

References

  1. Chen Z, Shen HT, Zhou X, Zheng Y, Xie X (2010) Searching trajectories by locations—an efficiency study. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data (SIGMOD), pp 255–266

  2. Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P (2012) User oriented trajectory search for trip recommendation. In: Proceedings of the 15th international conference on extending database technology (EDBT), pp 156–167

  3. Zheng K, Shang S, Yuan NJ, Yang Y (2013) Towards efficient search for activity trajectories. In: Proceedings of the 29th IEEE international conference on data engineering (ICDE), pp 230–241

  4. Yi BK, Jagadish H, Faloutsos C (1998) Efficient retrieval of similar time sequences under time warping. In: Proceedings of the 14th IEEE international conference on data engineering (ICDE), pp 201–208

  5. Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of the 18th IEEE international conference on data engineering (ICDE), pp 673–684

  6. Chen L, Ng R (2004) On the marriage of lp-norms and edit distance. In: Proceedings of the 30th international conference on very large data bases (VLDB), pp 792–803

  7. Chen L, Özsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data (SIGMOD), pp 491–502

  8. Tang LA, Zheng Y, Xie X, Yuan J, Yu X, Han J (2011) Retrieving \(k\)-nearest neighboring trajectories by a set of point locations. In: Advances in spatial and temporal databases—12th international symposium (SSTD), pp 223–241

  9. Vieira MR, Bakalov P, Tsotras VJ (2011) Querying trajectories using flexible patterns. In: Proceedings of the 13th international conference on extending database technology (EDBT), pp 406–417

  10. Hadjieleftheriou M, Kollios G, Bakalov P (2005) Complex spatio-temporal pattern queries. In: Proceedings of the 31th international conference on very large data bases (VLDB), pp 877–888

  11. Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from GPS data. In: Proceedings of the 36th international conference on very large data bases (VLDB), pp 1009–1020

  12. Yang Y, Gong Z, U LH (2011) Identifying points of interest by self-tuning clustering. In: Proceeding of the 34th international ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 1009–1020

  13. Spaccapietra S, Parent C, Damiani ML, de Macêdo JA, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng (DKE) 65(1):126–146

    Article  Google Scholar 

  14. Tietbohl A, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing (SAC), pp 863–868

  15. Rocha JAMR, Oliveira G, Bogorny V (2010) DB-SMoT: a direction-based spatio-temporal clustering method. In: 5th IEEE international conference on intelligent systems (IS), pp 114–119

  16. Zheng K, Zheng Y, Xie X, Zhou X (2012) Reducing uncertainty of low-sampling-rate trajectories. In: Proceedings of the 28th IEEE international conference on data engineering (ICDE), pp 1144–1155

  17. Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs. In: The 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 186–194

  18. Fagin R, Lotem A, Naor M (2001) Optimal aggregation algorithms for middleware. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (PODS)

  19. Lazaridis I, Mehrotra S (2001) Progressive approximate aggregate queries with a multi-resolution tree structure. In: Proceedings of the 2001 ACM SIGMOD international conference on management of data (SIGMOD), pp 401–412

  20. OKabe A, Boots B, Sugihara K, Chiu SN (2009) Spatial tessellations, concepts and applications of Voronoi diagrams, vol 501. Wiley, New York

  21. Wu D, Yiu ML, Jensen CS, Cong G (2011) Efficient continuously moving top-\(k\) spatial keyword query processing. In: Proceedings of the 27th IEEE international conference on data engineering (ICDE), pp 541–552

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Acknowledgments

We thank the reviewers for giving us many constructive comments, with which we have significantly improved our paper. This research is supported in part by GRF Grant HKUST 617610, SHIAE Grant No. 8115048 and MSRA Grant No. 6903555.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong

    Da Yan & James Cheng

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Zhou Zhao & Wilfred Ng

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  1. Da Yan
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  2. James Cheng
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  3. Zhou Zhao
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Correspondence to Da Yan.

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Yan, D., Cheng, J., Zhao, Z. et al. Efficient location-based search of trajectories with location importance. Knowl Inf Syst 45, 215–245 (2015). https://doi.org/10.1007/s10115-014-0787-2

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