인터넷정보학회논문지 (Journal of Internet Computing and Services)

  • 제15권2호
  • /
  • Pages.33-39
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  • 2014
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  • 1598-0170(pISSN)
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  • 2287-1136(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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무선방송환경에서 클라이언트의 공간질의 수를 고려한 효율적인 데이터 스케줄링

Efficient Data Scheduling considering number of Spatial query of Client in Wireless Broadcast Environments

  • Song, Doohee (Information Communication Engineering, Wonkwang University) ;
  • Park, Kwangjin (Information Communication Engineering, Wonkwang University)
  • 투고 : 2013.11.25
  • 심사 : 2014.01.08
  • 발행 : 2014.04.30
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초록

무선방송환경에서 서버가 클라이언트에게 데이터를 전송하는 방식은 다음과 같다. 서버는 클라이언트들이 원하는 데이터 정보를 정리하고, 데이터를 방송주기에 1차원 배열 형태로 전송한다. 클라이언트는 서버에게 전송받은 데이터를 청취하고 필요한 결과 값만을 사용자에게 반환한다. 최근 위치기반 서비스를 이용하는 사용자가 증가하고 객체 수의 증가 및 데이터가 대용량으로 변화되고 있다. 무선방송환경에서 대용량 데이터는 클라이언트의 질의처리시간을 증가시킬 수 있다. 따라서 우리는 무선방송환경에서 주어진 데이터를 효율적으로 스케줄링할 수 있는 클라이언트 기반의 데이터 스케줄링 (Client based Data Scheduling; CDS)을 제안한다. CDS는 맵을 분할하고 분할된 그리드 내에 객체 수 및 객체의 데이터 크기를 고려하여 각 그리드마다 객체들의 총 데이터 크기의 합을 계산한다. 각 그리드 (영역)별 객체들의 총 데이터 크기와 클라이언트 수를 고려한 hot-cold 기법을 적용하여 데이터를 스케줄링 한다. 실험을 통하여 CDS가 기존의 기법보다 클라이언트들의 평균 질의처리시간을 줄이는 것을 확인한다.

How to transfer spatial data from server to client in wireless broadcasting environment is shown as following: A server arranges data information that client wants and transfers data by one-dimensional array for broadcasting cycle. Client listens data transferred by the server and returns resulted value only to server. Recently number of users using location-based services is increasing alongside number of objects, and data volume is changing into large amount. Large volume of data in wireless broadcasting environment may increase query time of client. Therefore, we propose Client based Data Scheduling (CDS) for efficient data scheduling in wireless broadcasting environment. CDS divides map and then calculates total sum of objects for each grid by considering number of objects and data size within divided grids. It carries out data scheduling by applying hot-cold method considering total data size of objects for each grid and number of client. It's proved that CDS reduces average query processing time for client compared to existing method.

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과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. S. Hong, Y. Sin, J. Jang, "Optimization and Performance Analysis of Cloud Computing Platform for Distributed Processing of Big Data", Korea Spatial Information Society, vol. 19, no. 4, pp 55-71, 2011.
  2. H. Jang, J. Chung, G. Diana, S. Jung, "The grid-based distributed indexing on scalable spatial data", Korea Spatial Information Society, vol. 38, no. 1, pp. 29-32, 2011.
  3. J. Choi, H. Lee, Y. Park, "Representation of User Preferred Route Model for Large-scale GPS Data Analysis", Korean Institute of Information Scientists and Engineers, vol. 39, no. 4, pp. 315-327, 2012.
  4. I. Son, H. Li, Y. Park, K. Bok, J. Yoo, "A Location based Routing Method Considering Connectivities and Directionalities in Mobile P2P Environments", Korean Institute of Information Scientists and Engineers, vol. 39, no. 2, pp 170-172, 2012.
  5. C. Chow, F. Mohamed, and L. Hong, "On Efficient and Scalable Support of Continuous Queries in Mobile Peer-to-peer Environments", IEEE Transactions on Mobile Computing, vol.10, no.10, pp.1473-1487, 2011. https://doi.org/10.1109/TMC.2011.104
  6. J. Zheng, M. Zhu, and D. Papadias, "Location-based Spatial Queries", In Proc. Int Conf. of Special Interest Group on Management Of Data, pp. 443-454, 2003.
  7. P. Bellavista, A. Kupper, and S. Helal, "Location-based Services: Back to the Future", IEEE Pervasive Computing vol. 7, no. 2, pp. 85-89, 2008.
  8. D. Kim, E. Jang, S. Park, S. Lee, "Technical Trend of Location-Based Service", Korea Communications Agency, vol. 2 no. 2, pp. 2-24, 2013.
  9. J. Yoo, "Technical and Services Development Trend of Indoor Location Based Services", National IT Industry Promotion Agency, pp. 14-26, 2013.
  10. C. Park, " Services Trend of Location Information and Change of Paradigm", Korea Internet & Security Agency, pp. 24-40, 2013.
  11. H. Hong, "Mobile Internet using Trend and Mobile Search on demand based Search Technique research way", Korean Institute of Communications and Information Sciences, vol. 26, no. 4 pp. 26-30, 2009.
  12. B. Zheng, W. -C. Lee, Ken C. K. Lee, D. L. Lee, and M. Shao, "A Distributed spatial index for Error-Prone wireless Data broadcast", Very Large Data Bases Journal, vol. 18, no. 4, pp. 959-986, 2009. https://doi.org/10.1007/s00778-009-0137-2
  13. K. Park, H. Choo, and V. Patrick, "A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems", Wireless Networks vol. 16, no. 4, pp. 1011-1031, 2010. https://doi.org/10.1007/s11276-009-0185-y
  14. D. Song, K. Park, "A Hierarchical Bitmap-based Spatial Index use k-Nearest Neighbor Query Processing on the Wireless Broadcast Environment", Korea Society of Computer Information, vol. 17, no. 1, pp. 203-209, 2012. https://doi.org/10.9708/jksci.2012.17.1.203
  15. C. Gotsman and M. Lindenbaum. "On the Metric Properties of Discrete Space-Filling Curves," IEEE Transactions on Image Processing, vol. 5, no. 5, pp. 794-797, May, 1996. https://doi.org/10.1109/83.499920
  16. Y. Wang, C. Xu, Y. Gu, M. Chen and G. Yu, "Spatial query processing in road networks for wireless data broadcast", Wireless Networks, vol. 19, no. 4, pp 477-494, 2013. https://doi.org/10.1007/s11276-012-0479-3
  17. J. J. Levandoski, P-A Larson and R. Stoica, "Identifying Hot and Cold Data in Main-Memory Databases", IEEE International Conference on Data Engineering, pp. 26-37, 2013.
  18. S. Kang, "A Study on Efficient Cut-off Point between Hot and Cold Items for Data Broadcast Scheduling", Korean Society of Broadcast Engineers vol. 15, no. 6, pp. 845-852 , 2010. https://doi.org/10.5909/JBE.2010.15.6.845
  19. H. Lim, S. Jung, "A Heterogeneous Data Broadcast Scheduling Scheme over Multiple Wireless Broadcast Channels", Korean Institute of Information Scientists and Engineers, vol. 38, no. 4, pp.257-262, 2011.
  20. K. Sin, S. Jung, "An R-tree Scheduling Method for kNN Query Processing in Multiple Wireless Broadcast Channels", Korean Institute of Information Scientists and Engineers, vol. 38, no. 3, pp. 194-199, 2011.
  21. A. Guttman, "R-Trees: A Dynamic Index Structure for Spatial Searching." In Proc. of Special Interest Group on Management Of Data, vol. 14, no. 2, pp. 47-57, 1984.