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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5403–5426 | Cite as

An efficient continuous k-nearest neighbor query processing scheme for multimedia data sharing and transmission in location based services

  • Kyoungsoo Bok
  • Yonghun Park
  • Jaesoo YooEmail author
Article
  • 64 Downloads

Abstract

The continuous k-nearest neighbor query is one of the most important query types to share multimedia data or to continuously identify transportable users in LBS. Various methods have been proposed to efficiently process the continuous k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. Furthermore, most methods do not consider the movement of a query. In this paper, we propose a new processing scheme to process the continuous k nearest neighbor query for efficiently support multimedia data sharing and transmission in LBS. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, the proposed scheme significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.

Keywords

Location based service Continuous query Moving object Nearest neighbor Grid index 

Notes

Acknowledgments

This research was supported by the MSIT (Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2013-1-00881) supervised by the IITP (Institute for Information & communication Technology Promotion), by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B3007527), and by the ICT R&D program of MSIT/IITP. [B0101-15-0266, Development of High Performance Visual BigData Discovery Platform for Large-Scale Realtime Data Analysis].

References

  1. 1.
    Bok K, Lim J, Hong S, Yoo J (2017) A multiple RSU collaborative scheduling scheme for data services in vehicular ad hoc networks. Clust Comput 20(2):1167–1178CrossRefGoogle Scholar
  2. 2.
    Cheema MA, Yuan Y, Lin X (2007) CircularTrip: an effective algorithm for continuous kNN queries. In: Proceedings of International Conference on Database Systems for Advanced Applications, p 863–869Google Scholar
  3. 3.
    Gao L, Yao Z, Wang XS (2002) Evaluating continuous nearest neighbor queries for streaming time series via pre-fetching. In: Proceedings of International Conference on Information and Knowledge Management, p 485–492Google Scholar
  4. 4.
    Güting RH, Behr T, Düntgen C (2010) SECONDO: a platform for moving objects database research and for publishing and integrating research implementations. IEEE Data Eng Bull 33:56–63Google Scholar
  5. 5.
    Ha TTT, Won Y, Kim J (2017) An efficient hybrid push-pull methodology for peer-to-peer video live streaming system on mobile broadcasting social media. Multimedia Tools Appl 76(2):2557–2568CrossRefGoogle Scholar
  6. 6.
    Huang Y, Su I, Lin L, Chung Y (2013) Efficient processing of updates for moving objects with varying speed and direction. In: Proceedings of International Conference on Advanced Information Networking and Applications, p 854–861Google Scholar
  7. 7.
    Kalashnikov DV, Prabhakar S, Hambrusch SE (2004) Main memory evaluation of monitoring queries over moving objects. Distrib Parallel Databases 15:117–135CrossRefGoogle Scholar
  8. 8.
    Li G, Li Y, Shu L, Fan P (2011) CkNN query processing over moving objects with uncertain speeds in road networks. In: Proceedings of Asia-Pacific Web Conference, p 65–76Google Scholar
  9. 9.
    Lin Y, Yu Q, Medioni GG (2011) Efficient detection and tracking of moving objects in geo-coordinates. Mach Vis Appl 22:505–520CrossRefGoogle Scholar
  10. 10.
    Liu C, Lai C (2013) Distributed continuous k nearest neighbors search over moving objects on wireless sensor networks. International Journal of Distributed Sensor Networks 1–20Google Scholar
  11. 11.
    Lu W, Shen Y, Chen S, Ooi BC (2012) Efficient processing of k nearest neighbor joins using MapReduce. PVLDB 5:1016–1027Google Scholar
  12. 12.
    Mokbel MF, Xiong X, Aref WG (2004) SINA: scalable incremental processing of continuous queries in spatiotemporal databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, p 623–634Google Scholar
  13. 13.
    Mouratidis K, Papadias D (2007) Continuous nearest neighbor queries over sliding windows. IEEE Trans Knowl Data Eng 19:789–803CrossRefGoogle Scholar
  14. 14.
    Mouratidis K, Hadjieleftheriou M, Papadias D (2005) Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of ACM SIGMOD International Conference on Management of Data, p 634–645Google Scholar
  15. 15.
    Mouratidis K, Yiu ML, Papadias D, Mamoulis N (2006) Continuous nearest neighbor monitoring in road networks. In: Proceedings of International Conference on Very Large Data Bases, p 43–54Google Scholar
  16. 16.
    Nutanong S, Zhang R, Tanin E, Kulik L (2008) The V*-diagram: a query-dependent approach to moving kNN queries. Proceedings of the VLDB Endowment 1: 1095–1106CrossRefGoogle Scholar
  17. 17.
    Park Y, Park H, Seo D, Yoo J (2009) An index structure for efficient k-NN query processing in location based service. In: Proceedings of International Conference on Ubiquitous Information Technologies & Applications, p 1–6Google Scholar
  18. 18.
    Park Y, Seo D, Lim J, Lee J, Kim M, Bao W, Ryu CT, Yoo J (2010) A new spatial index structure for efficient query processing in location based services. In: Proceedings of International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, p 434–441Google Scholar
  19. 19.
    Park Y, Seo D, Bok K, Yoo J (2011) k-nearest neighbor query processing method based on distance relation pattern. In: Proceedings of ACM Conference on Information and Knowledge Management, p 2413–2416Google Scholar
  20. 20.
    Prabhakar S, Xia Y, Kalashnikov DV, Aref WG, Hambrusch SE (2002) Query indexing and velocity constrained indexing: scalable techniques for continuous queries on moving objects. IEEE Trans Comput 51:1124–1140MathSciNetCrossRefGoogle Scholar
  21. 21.
    Raghuwanshi G, Tyagi V (2017) A novel technique for location independent object based image retrieval. Multimedia Tools Appl 76(12):13741–13759CrossRefGoogle Scholar
  22. 22.
    Schmiegelt P, Seeger B, Behrend A, Koch W (2012) Continuous queries on trajectories of moving objects. In: Proceedings of International Database Engineering and Applications Symposium, p 165–174Google Scholar
  23. 23.
    Soldo F, Casetti C, Chiasserini C, Chaparro PA (2001) Video streaming distribution in VANETs. IEEE Transactions on Parallel and Distributed Systems 22(7):1085–1109CrossRefGoogle Scholar
  24. 24.
    Wang H, Zimmermann R (2011) Processing of continuous location-based range queries on moving objects in road networks. IEEE Trans Knowl Data Eng 23:1065–1078CrossRefGoogle Scholar
  25. 25.
    Wang X, Zhang Q, Sun W, Wang W, Shi B (2005) cGridex: efficient processing of continuous range queries over moving objects. In: Proceedings of International Conference on Web-Age Information Management, p 345–356Google Scholar
  26. 26.
    Wang Y, Zhang R, Xu C, Qi J, Gu Y, Yu G (2014) Continuous visible k nearest neighbor query on moving objects. Inf Syst 44:1–21CrossRefGoogle Scholar
  27. 27.
    Wang M, Xu C, Wei Y, Guan J (2015) Propagation-based content dissemination for social mobile interactive multimedia services. In: Proceedings of International Conference on Mobile Multimedia Communications, p 13–19Google Scholar
  28. 28.
    Wu K, Chen S, Yu PS (2004) Processing continual range queries over moving objects using VCR-based query indexes. In: Proceedings of International Conference on Mobile and Ubiquitous Systems, p 226–235Google Scholar
  29. 29.
    Wu K, Chen S, Yu PS (2005) On incremental processing of continual range queries for location-aware services and applications. In: Proceedings of International Conference on Mobile and Ubiquitous Systems, p 261–269Google Scholar
  30. 30.
    Wu H, Peng H, Zhou Q, Yang M, Sun B, Yu B (2007) P2P multimedia sharing over MANET. In: Proceedings of International Conference on MultiMedia Modeling, p 635–642Google Scholar
  31. 31.
    Xiong X, Mokbel MF, Aref WG (2005) SEA-CNN: scalable processing of continuous K-nearest neighbor queries in spatio-temporal databases. In: Proceedings of International Conference on Data Engineering, p 643–654Google Scholar
  32. 32.
    Yu X, Pu KQ, Koudas N (2005) Monitoring k-nearest neighbor queries over moving objects. In: Proceedings of International Conference on Data Engineering, p 631–642Google Scholar
  33. 33.
    Zhang C, Li F, Jestes J (2012) Efficient parallel kNN joins for large data in MapReduce. In: Proceedings of International Conference on Extending Database Technology, p 38–49Google Scholar
  34. 34.
    Zheng B, Lee DL (2001) Semantic caching in location-dependent query processing. In: Proceedings of International Symposium on Spatial and Temporal Databases, p 97–116CrossRefGoogle Scholar
  35. 35.
    Zhu T, Wang C, Lv W, Huang J (2010) Continuous range monitoring of moving objects in road networks. In: Proceedings of International Conference on Intelligent Systems Design and Applications, p 1412–1417Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringChungbuk National UniversityCheongjuSouth Korea
  2. 2.NetReCa Inc.OrangeUSA

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