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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Efficient Continuous Skyline Query Processing Scheme over Large Dynamic Data Sets

  • Li, He (School of Software, Xidian University) ;
  • Yoo, Jaesoo (School of Information and Communication Engineerin, Chungbuk National University)
  • Received : 2016.01.11
  • Accepted : 2016.07.20
  • Published : 2016.12.01
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Abstract

Performing continuous skyline queries of dynamic data sets is now more challenging as the sizes of data sets increase and as they become more volatile due to the increase in dynamic updates. Although previous work proposed support for such queries, their efficiency was restricted to small data sets or uniformly distributed data sets. In a production database with many concurrent queries, the execution of continuous skyline queries impacts query performance due to update requirements to acquire exclusive locks, possibly blocking other query threads. Thus, the computational costs increase. In order to minimize computational requirements, we propose a method based on a multi-layer grid structure. First, relational data object, elements of an initial data set, are processed to obtain the corresponding multi-layer grid structure and the skyline influence regions over the data. Then, the dynamic data are processed only when they are identified within the skyline influence regions. Therefore, a large amount of computation can be pruned by adopting the proposed multi-layer grid structure. Using a variety of datasets, the performance evaluation confirms the efficiency of the proposed method.

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References

  1. S. Borzonyi, D. Kossmann, and K. Stocker, "The Skyline Operator," Proc. Int. Conf. Data Eng., Heideberg, Germany, Apr. 2-6, 2001, pp. 421-430.
  2. K.L. Tan, P.K. Eng, and B.C. Ooi, "Efficient Progressive Skyline Computation," Proc. VLDB, Rome, Italy, Sept. 11-14, 2001, pp. 301-310.
  3. D. Papadias et al., "An Optimal and Progressive Algorithm for Skyline Queries," Proc. SIGMOD Int. Conf. Manag. Data, San Diego, CA, USA, June 9-12, 2003, pp. 467-478.
  4. D. Papadias et al., "Progressive Skyline Computation in Database System," ACM J., vol. 30, no. 1, Mar. 2005, pp. 41-82.
  5. J. Chomicki et al., "Skyline with Presorting," Proc. Int. Conf. Data Eng., Bangalore, India, Mar, 5-8, 2003, pp. 717-720.
  6. D. Kossmann, F. Ramsak, and S. Rost, "Shooting Stars in the Sky: an Online Algorithm for Skyline Queries," Proc. VLDB, Hong Kong, China, Aug. 20-23, 2002, pp. 275-286.
  7. C.Y. Chan et al., "Finding k-Dominant Skylines in High Dimensional Space," Proc. SIGMOD Int. Conf. Manag. Data, Chicago, IL, USA, June 27-29, 2006, pp. 503-514.
  8. X.M. Lin et al., "Stabbing the Sky: Efficient Skyline Computation over Sliding Windows," Proc. Int. Conf. Data Eng., Tolkyo, Japan, Apr. 5-8, 2005, pp. 502-513.
  9. Y.F. Tao and D. Papadias, "Maintaining Sliding Window Skylines on Data Streams," IEEE Trans. Knowl. Data Eng., vol. 18, no. 3, Mar. 2006, pp. 377-391. https://doi.org/10.1109/TKDE.2006.48
  10. Y.W. Lee, K.Y. Lee, and M.H. Kim, "Efficient Processing of Multiple Continuous Skyline Queries over a Data Stream," Inform. Sci., vol. 221, Feb. 2013, pp. 316-337. https://doi.org/10.1016/j.ins.2012.09.040
  11. H. Wang et al., "Efficient Processing of Continuous Skyline Query over Smarter Traffic Data Stream for Cloud Computing," Discrete Dynamics Nature Soc., vol. 2013, 2013, pp. 1-10.
  12. Z.Y. Huang et al., "Continuous Skyline Queries for Moving Objects," IEEE Trans. Knowl. Data Eng., vol. 18, no. 12, Dec. 2006, pp. 1645-1658. https://doi.org/10.1109/TKDE.2006.185
  13. M.W. Lee and S.W. Hwang, "Continuous Skylining on Volatile Moving Data," Proc. Int. Conf. Data Eng., Shanghai, China, Mar. 29-Apr. 2, 2009, pp. 1568-1575.
  14. L. Tian et al., "Continuous Monitoring of Skyline Query over Highly Dynamic Moving Objects," Proc. Int. Workshop Data Eng. Wireless Mobile Access, Beijing, China, June 10, 2007, pp. 59-66.
  15. L. Tian et al., "Grid Index based Algorithm for Continuous Skyline Computation," Chinese J. Comput., vol. 6, no. 6, June 2008, pp. 998-1012.
  16. H.T. Kung, F. Luccio, and F.P. Preparata, "On Finding the Maxima of a Set of Vectors," J. ACM, vol. 22, no. 4, Oct. 1975, pp. 469-476. https://doi.org/10.1145/321906.321910
  17. J. Lee and S. Hwang, "QSkycube: Efficient Skycube Computation using Point-Based Space Partitioning," Proc. VLDB Endowment, vol. 4, no. 3, Dec. 2010, pp. 185-196. https://doi.org/10.14778/1929861.1929865
  18. J. Lee and S. Hwang, "Toward Efficient Multidimensional Subspace Skyline Computation," VLDB J., vol. 23, no. 1, Feb. 2014, pp. 129-145. https://doi.org/10.1007/s00778-013-0317-y
  19. M. Morse, J.M. Patel, and W.I. Grosky, "Efficient Continuous Skyline Computation," Inform. Sci., vol. 177, no. 17, Sept. 2007, pp. 3411-3437. https://doi.org/10.1016/j.ins.2007.02.033
  20. J. Lim et al., "A Continuous Reverse Skyline Query Processing Method in Moving Objects Environments," Data Knowl. Eng., Vol. 104, July 2015, pp. 1-14.
  21. Y. Hsueh et al., "SkyEngine: Efficient Skyline Search Engine for Continuous Skyline Computations," IEEE Conf. Data Eng., Hannover, Germany, Apr. 11-16, 2011, pp. 1316-1319.
  22. H. Li et al., "An Efficient Grid Method for Continuous Skyline Computation over Dynamic Data Set," J. Contents, vol. 6, no. 1, 2010, pp. 47-51.
  23. X.W. Wang and Y. Jia, "Grid-Bsed Probabilistic Skyline Retrieval on Distributed Uncertain Data," Int. Conf. DASFAA, Hong Kong, China, Apr. 22-25, 2011, pp. 538-547.