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Moving Objects Advanced Querying

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Moving Objects Management

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Abstract

So far, we have introduced the basic querying for moving objects. There are still some advanced querying for moving objects. It is more difficult to deal with these queries. In this chapter, we introduce a few advanced queries, especially similar trajectory queries and density queries for moving objects. The goal of similar trajectory queries is to find the moving patterns in the trajectories of moving objects, while density queries are to efficiently find dense areas with high concentration of moving objects. We will discuss how to process both the snapshot and continuous density queries in this chapter.

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References

  1. Agrawal R, Faloutsos C, Swami AN (1993) Efficient similarity search in sequence databases. In: Proceedings of the 4th international conference on foundations of data organization and algorithms (FODO 1993), Chicago, pp 69–84

    Google Scholar 

  2. Agrawal R, Lin KI, Sawhney HS, Shim K (1995) Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21st international conference on very large data bases (VLDB 1995), Zurich, pp 490–501

    Google Scholar 

  3. Andoni A, Deza M, Gupta A, Indyk P, Raskhodnikova S (2003) Lower bounds for embedding edit distance into normed spaces. In: Proceedings of the 14th annual ACM-SIAM symposium on discrete algorithms (SODA 2003), Baltimore, pp 523–526

    Google Scholar 

  4. Cai Y, Ng R (2004) Indexing spatio-temporal trajectories with chebyshev polynomials. In: Proceedings of the 2004 ACM SIGMOD international conference on management of data (SIGMOD 2004), Paris, pp 599–610

    Google Scholar 

  5. Chakrabarti K, Keogh E, Mehrotra S, Pazzani M (2002) Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans Database Syst 27(2):151–162

    Article  Google Scholar 

  6. Chan KP, Fu AW (1999) Efficient time series matching by wavelets. In: Proceedings of the 15th international conference on data engineering (ICDE 1999), Sydney, p 126

    Google Scholar 

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

    Google Scholar 

  8. Chen S, Kashyap RL (2001) A spatio-temporal semantic model for multimedia presentations and multimedia database systems. IEEE Trans Knowl Data Eng 13(4):607–622

    Article  Google Scholar 

  9. Chon H, Agrawal D, Abbadi AE (2002) Query processing for moving objects with space-time grid storage model. In: Proceedings of the 3rd international conference on mobile data management (MDM 2002), Singapore, pp 121–129

    Google Scholar 

  10. Cormode G, Muthukrishnan S (2002) The string edit distance matching problem with moves. In: Proceedings of the 13th annual ACM-SIAM symposium on discrete algorithms (SODA 2002), San Francisco, pp 667–676

    Google Scholar 

  11. Douias D, Peucker T (1973) Algorithm for the reduction of the number of points required to represent a line or its character. Am Cartogr 10(42):112–123

    Google Scholar 

  12. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2th international conference on knowledge discovery and data mining (SIGKDD 1996), Portland, pp 226–231

    Google Scholar 

  13. Faloutsos C, Ranganathan M, Manolopoulos Y (1994) Fast subsequence matching in time-series databases. In: Proceedings of the 1994 ACM SIGMOD international conference on management of data (SIGMOD 1994), Minneapolis, pp 419–429

    Google Scholar 

  14. Hadjieleftheriou M, Kollios G, Gunopulos D, Tsotras VJ (2003) On-line discovery of dense areas in spatio-temporal databases. In: Proceedings of the 8th international symposium on advances in spatial and temporal databases (SSTD 2003), Santorini Island, pp 306–324

    Google Scholar 

  15. Jagadish HV, Mendelzon AO, Milo T (1995) Similarity-based queries. In: Proceedings of the 14th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (PODS 1995), San Jose, pp 36–45

    Google Scholar 

  16. Jensen CS, Lin D, Ooi BC, Zhang R (2006) Effective density queries on continuously moving objects. In: Proceedings of the 22nd international conference on data engineering (ICDE 2006), Atlanta, p 71

    Google Scholar 

  17. Keogh E (2002) Exact indexing of dynamic time warping. In: Proceedings of the 28th international conference on very large data bases (VLDB 2002), Hong Kong, pp 406–417

    Google Scholar 

  18. Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. J Knowl Inf Syst 3(3):263–286

    Article  MATH  Google Scholar 

  19. Kollios G, Tsotras VJ, Gunopulos D, Delis A, Hadjieleftheriou M (2001) Indexing animated objects using spatiotemporal access methods. IEEE Trans Knowl Data Eng 13(5):758–777

    Article  Google Scholar 

  20. Korn F, Jagadish H, Faloutsos C (1997) Efficiently supporting Ad hoc queries in large datasets of time sequences. In: Proceedings of the 1997 ACM SIGMOD international conference on management of data (SIGMOD 1997), Tucson, pp 289–300

    Google Scholar 

  21. Martina N and By RA (2004) Spatiotemporal compression techniques for moving point objects. In: Proceedings of the 9th international conference on extending database technology (EDBT 2004), pp 765–782

    Google Scholar 

  22. Papadias D, Zhang J, Mamoulis N, Tao Y (2003) Query processing in spatial network databases. In: Proceedings of the 29th international conference on very large data bases (VLDB 2003), Berlin, pp 802–813

    Google Scholar 

  23. Pfoser D, Jensen CS, Theodoridis Y (2000) Novel approaches in query processing for moving object trajectories. In: Proceedings of the 26th international conference on very large data bases (VLDB 2000), Cairo, pp 395–406

    Google Scholar 

  24. Priyantha N, Miu A, Balakrishnan H, Teller S (2001) The cricket compass for context-aware mobile applications. In: Proceedings of the 7th annual international conference on mobile computing and networking (MOBICOM 2001), Rome, pp 1–14

    Google Scholar 

  25. Rafiei D (1999) On similarity-based queries for time series data. In: Proceedings of the 15th international conference on data engineering (ICDE 1999), Sydney, pp 410–417

    Google Scholar 

  26. Saltenis S, Jensen CS, Leutenegger ST, and Lopez MA (2000) Indexing the positions of continuously moving objects. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data (SIGMOD 2000), Dallas, pp 331–342

    Google Scholar 

  27. Vlachos V, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of the 18th international conference on data engineering (ICDE 2002), San Jose, p 673

    Google Scholar 

  28. Yi B, Faloutsos C (2000) Fast time sequence indexing for arbitrary Lp norms. In: Proceedings of the 26th international conference on very large data bases (VLDB 2000), Cairo, pp 385–394

    Google Scholar 

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

    Google Scholar 

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Authors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. Chinese Academy of Sciences, Beijing, China

    Zhiming Ding & Jiajie Xu

Authors
  1. Xiaofeng Meng
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  2. Zhiming Ding
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  3. Jiajie Xu
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© 2014 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg

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Meng, X., Ding, Z., Xu, J. (2014). Moving Objects Advanced Querying. In: Moving Objects Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38276-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-38276-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38275-8

  • Online ISBN: 978-3-642-38276-5

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