- Volume 17 Issue 8
DOI QR Code
Efficient k-Nearest Neighbor Query Processing Method for a Large Location Data
대용량 위치 데이터에서 효율적인 k-최근접 질의 처리 기법
- Received : 2017.07.18
- Accepted : 2017.08.04
- Published : 2017.08.28
With the growing popularity of smart devices, various location based services have been providing to users. Recently, some location based social applications that combine social services and location based services have been emerged. The demands of a k-nearest neighbors(k-NN) query which finds k closest locations from a user location are increased in the location based social network services. In this paper, we propose an approximate k-NN query processing method for fast response time in a large number of users environments. The proposed method performs efficient stream processing using big data distributed processing technologies. In this paper, we also propose a modified grid index method for indexing a large amount of location data. The proposed query processing method first retrieves the related cells by considering a user movement. By doing so, it can make an approximate k results set. In order to show the superiority of the proposed method, we conduct various performance evaluations with the existing method.
Stream Processing;Continuos Query Processing;Approximate k-NN;LBS;Moving Object
- C. Li, Y. Gu, J. Qi, G. Yu, R. Zhang, and W. Yi, "Processing Moving kNN Queries Using Influential Neighbor Sets," Proceedings of the VLDB Endowment, Vol.8, No.2, pp.113-124, 2014. https://doi.org/10.14778/2735471.2735473
- X. Yi, R. Paulet, E. Bertino, and V. Varadharajan, "Practical k Nearest Neighbor Queries with Location Privacy," Proc. International Conference on Data Engineering, pp.640-651, 2014.
- C. Ji, B. Wang, S. Tao, J. Wu, Z. Wang, L. Tang, T. Zu, and G. Zhao, "Inverted Voronoi-Based kNN Query Processing with MapReduce," Proc. International Conference on Trust, Security, and Privacy in Computing and Communications, pp.2263-2268, 2016.
- W. Kim, Y. Kim, and L. Shim, "Parallel Computation of k-Nearest Neighbor Joins Using MapReduce," Proc. International Conference on Big Data, pp.696-705, 2016.
- J. Maillo, S. Ramirez, I. Triguero, and F. Herrera, "kNN-IS: An Iterative Spark-based Design of The k-Nearest Neighbors Classifier for Big Data," Knowledge Based Systems, Vol.117, pp.3-15, 2017. https://doi.org/10.1016/j.knosys.2016.06.012
- Z. Yu, Y. Liu, X. Yu, and K. Q. Pu, "Scalable Distributed Processing of K Nearest Neighbor Queries over Moving Objects," IEEE Transactions on Knowledge and Data Engineering, Vol.27, No.5, pp.1383-1396, 2016.
- A. K. Haidar, D. Taniar, and M. Safar, "Approximate Algorithms for Static and Continuous Range Queries in Mobile Navigation," Computing, Vol.95, No.10-11, pp.949-976, 2013. https://doi.org/10.1007/s00607-012-0219-7
- C. Fu and D. Cai, "Efanna: An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph," arXiv preprint arXiv:1609.07228, 2016.
- L. Ai, J. Yu, Z. Wu, Y. He, and T. Guan, "Optimized Residual Vector Quantization for Efficient Approximate Nearest Neighbor Search," Multimedia Systems, Vol.23, No.2, pp.169-181, 2017. https://doi.org/10.1007/s00530-015-0470-9
- L. Verdoliva, D. Cozzolino, and G. Poggi, "A Reliable Order-Statistics-Based Approximate Nearest Neighbor Search Algorithm," IEEE Transactions on Image Processing, Vol.26, No.1, pp.237-250, 2017. https://doi.org/10.1109/TIP.2016.2624141
- W. Xue, X. Y. Hong, N. Zhao, R. L. Yang, and L. Zhang, "Predicting Protein Subcellular Localization by Approximate Nearest Neighbor Searching," Proc, Chinese Control And Decision Conference, pp.2842-2846, 2017.
- M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica, "Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters," Proc. USENIX Workshop on Hot Topics in Cloud Computing, 2012.
- M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, "Spark: Cluster Computing with Working Sets," Proc. USENIX Workshop on Hot Topics in Cloud Computing, 2010.
- L. Neumeyer, B. Robbins, A. Nair, and A. Kesari, "S4: Distributed stream computing platform," Proc. International Conference Data Mining Workshops, pp.170-177, 2010.
- 복경수, 육미선, 노연우, 한지은, 김연우, 임종태, 유재수, "빅데이터 환경에서 스트림 질의 처리를 위한 인메모리 기반 점진적 처리 기법," 한국콘텐츠학회논문지, 제16권, 제2호, pp.163-173, 2016.
- X. Xiong, M. F. Mokbel, and W. G. Aref, "Sea-cnn: Scalable Processing of Continuous k-Nearest Neighbor Queries in Spatio-Temporal Databases," Proc. International Conference on In Data Engineering, pp.643-654, 2005.
- 신동희, 김용문, "국내 재난관리 분야의 빅 데이터 활용 정책방안," 한국콘텐츠학회논문지, 제15권, 제2호, pp.377-392, 2015.
- 김연우, 김병훈, 고건식, 최민웅, 송희섭, 김기훈, 유승훈, 임종태, 복경수, 유재수, "실시간 기상 빅데이터를 활용한 홍수 재난안전 시스템 설계 및 구현," 한국콘텐츠학회논문지, 제17권, 제1호, pp.351-362, 2017.
Grant : 초고성능컴퓨팅기반 건강한 고령사회 대응 빅데이터 분석기술개발
Supported by : 정보통신기술진흥센터, 한국과학기술정보연구원, 한국연구재단