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Continuous aggregate nearest neighbor queries

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Abstract

This paper addresses the problem of continuous aggregate nearest-neighbor (CANN) queries for moving objects in spatio-temporal data stream management systems. A CANN query specifies a set of landmarks, an integer k, and an aggregate distance function f (e.g., min, max, or sum), where f computes the aggregate distance between a moving object and each of the landmarks. The answer to this continuous query is the set of k moving objects that have the smallest aggregate distance f. A CANN query may also be viewed as a combined set of nearest neighbor queries. We introduce several algorithms to continuously and incrementally answer CANN queries. Extensive experimentation shows that the proposed operators outperform the state-of-the-art algorithms by up to a factor of 3 and incur low memory overhead.

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Notes

  1. The authors of conceptual partitioning kindly provided us with their code of CPM.

References

  1. Open Optimization Library. http://ool.sourceforge.net/

  2. LOPTI - Mathematical Optimization Library. http://volnitsky.com/project/lopti/

  3. SCILAB-NEWUOA Interface. http://www.inrialpes.fr/bipop/people/guilbert/newuoa/newuoa.html

  4. Matlab-Condor. http://www.applied-mathematics.net/optimization/CONDORdownload.html

  5. Extreme Optimization Numerical Libraries for .NET. http://www.extremeoptimization.com/

  6. Bentley JL, Yao AC-C (1976) An almost optimal algorithm for unbounded searching. Inf Process Lett:5(3):82–87

    Article  Google Scholar 

  7. Brinkhoff T (2002) A framework for generating network based moving objects. Geoinformatica 6(2):153–180

    Article  Google Scholar 

  8. Cai Y, Hua KA, Cao G (2004) Processing range-monitoring queries on heterogeneous mobile objects. In: Proceedings of the international conference on mobile data management, MDM

  9. Cho H-J, Chung C-W (2005) An efficient and scalable approach to CNN queries in a road network. In: Proceedings of the international conference on very large data bases, VLDB, pp 865–876, Trondheim, Norway

  10. Düntgen C, Behr T, Güting RH (2009) BerlinMOD: a benchmark for moving object databases. VLDB J (The International Journal on Very Large Data Bases) 18(6):1335–1368

    Article  Google Scholar 

  11. Elmongui HG, Mokbel MF, Aref WG (2005) Spatio-temporal histograms. In: Proceedings of the international symposium on advances in spatial and temporal databases, SSTD

  12. Gedik B, Liu L (2004) MobiEyes: distributed processing of continuously moving queries on moving objects in a mobile system. In: Proceedings of the international conference on extending database technology, EDBT

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

  14. Hu H, Xu J, Lee DL (2005) A generic framework for monitoring continuous spatial queries over moving objects. In: Proceedings of the ACM international conference on management of data, SIGMOD

  15. Huang Z, Lu H, Ooi BC, Tung AK (2006) Continuous skyline queries for moving objects. IEEE Trans Knowl Data Eng (TKDE) 18(12):1645–1658

    Article  Google Scholar 

  16. Iwerks GS, Samet H, Smith K (2003) Continuous K-nearest neighbor queries for continuously moving points with updates. In: Proceedings of the international conference on very large data bases, VLDB, pp 512–523, Berlin, Germany

  17. Jensen CS, Lin D, Ooi BC (2004) Query and update efficient B  + -tree based indexing of moving objects. In: Proceedings of the international conference on very large data bases, VLDB

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

  19. Kang J, Mokbel MF, Shekhar S, Xia T, Zhang D (2007) Continuous evaluation of monochromatic and bichromatic reverse nearest neighbors. In: Proceedings of the international conference on data engineering, ICDE, pp 806–815, Istanbul, Turkey

  20. Lazaridis I, Porkaew K, Mehrotra S (2002) Dynamic queries over mobile objects. In: Proceedings of the international conference on extending database technology, EDBT

  21. Li H, Lu H, Huang B, Huang Z (2005) Two ellipse-based pruning methods for group nearest neighbor queries. In: Proceedings of the ACM symposium on advances in geographic information systems, ACM GIS.

  22. Mokbel MF, Aref WG (2005) GPAC: generic and progressive processing of mobile queries over mobile data. In: Proceedings of the international conference on mobile data management, MDM

  23. Mokbel MF, Aref WG (2005) PLACE: a scalable location-aware database server for spatiotemporal data streams. Data Eng Bull 28(3):3–10

    Google Scholar 

  24. Mokbel MF, Aref WG (2008) SOLE: scalable online execution of continuous queries on spatio-temporal data streams. VLDB J (The International Journal on Very Large Data Bases) 17(5):971–995

    Article  Google Scholar 

  25. Mokbel MF, Xiong X, Aref WG (2004) SINA: scalable incremental processing of continuous queries in spatio-temporal databases. In Proceedings of the ACM international conference on management of data, SIGMOD

  26. Mouratidis K, Papadias D, Hadjieleftheriou M (2005) Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of the ACM international conference on management of data, SIGMOD

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

  28. Papadias D, Shen Q, Tao Y, Mouratidis K (2004) Group nearest neighbor queries. In: Proceedings of the international conference on data engineering, ICDE

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

    Article  Google Scholar 

  30. Song Z, Roussopoulos N (2001) K-nearest neighbor search for moving query point. In: Proceedings of the international symposium on advances in spatial and temporal databases, SSTD

  31. Tao Y, Papadias D (2003) Spatial queries in dynamic environments. ACM Trans Database Syst (TODS) 28(2):101–139

    Article  Google Scholar 

  32. Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: Proceedings of the international conference on very large data bases, VLDB

  33. U LH, Mamoulis N, Yiu ML (2007) Continuous monitoring of exclusive closest pairs. In: Proceedings of the international symposium on advances in spatial and temporal databases, SSTD, Boston, MA

  34. Xia T, Zhang D (2006) Continuous reverse nearest neighbor monitoring. In: Proceedings of the international conference on data engineering, ICDE, Atlanta, GA

  35. Xiong X, Mokbel MF, Aref WG (2005) SEA-CNN: scalable processing of continuous K-nearest neighbor queries in spatio-temporal databases. In: Proceedings of the international conference on data engineering, ICDE

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

    Article  Google Scholar 

  37. Yu X, Pu KQ, Koudas N (2005) Monitoring K-nearest neighbor queries over moving objects. In: Proceedings of the international conference on data engineering, ICDE

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Authors and Affiliations

  1. Department of Computer and Systems Engineering, Faculty of Engineering, Alexandria University, Alexandria, 21544, Egypt

    Hicham G. Elmongui

  2. Department of Computer Science and Engineering, University of Minnesota - Twin Cities, 200 Union Street SE, Minneapolis, MN, 55455, USA

    Mohamed F. Mokbel

  3. Department of Computer Science, Purdue University, 305 N. University St., West Lafayette, IN, 47907, USA

    Walid G. Aref

Authors
  1. Hicham G. Elmongui
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  2. Mohamed F. Mokbel
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  3. Walid G. Aref
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Corresponding author

Correspondence to Hicham G. Elmongui.

Additional information

This work is supported in part by the National Science Foundation under Grant Numbers IIS-0811954, III-1117766, IIS-0964639, IIS-0811935, CNS-0708604, IIS-0952977 (NSF CAREER) and by a Microsoft Research Gift.

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Elmongui, H.G., Mokbel, M.F. & Aref, W.G. Continuous aggregate nearest neighbor queries. Geoinformatica 17, 63–95 (2013). https://doi.org/10.1007/s10707-011-0149-0

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