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Finding the Typical Communication Black Hole in Big Data Environment

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Data Mining and Big Data (DMBD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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Abstract

“Black hole” are widely spread in the mobile communication data, which will highly downgrade the mobile service quality. OLAP tools are extensively used for the decision-support application in the multidimensional data model, which just like the mobile communication case. As different dimensions of the mobile data are incomparable and, thus, can hardly generate one unique final value that satisfies all dimensions. We exploit the skyline operator as the postoperation while building data cubes, named as data cube of skyline. As the skyline of a cuboid is not derivable from another cuboid and the skyline operation is holistic, which makes this problem even challeging. In this paper, we propose a method in materializing the cube of skyline in the big communication data and proof its effectiveness and efficiency by extensive experiments.

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Notes

  1. 1.

    For example, it’s meaningless to compare “year” dimension to “region” dimension.

References

  1. Borzsony, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE 2001, pp. 421–430 (2001)

    Google Scholar 

  2. Gray, J., Chaudhuri, S., Bosworth, A., et al.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. In ICDE’96, pp. 152–159

    Google Scholar 

  3. Dehne, F., Eavis, T., Hambrusch, S., Rau-Chaplin, A.: Parallelizing the data cube. Distrib. Parallel Databases 11(2), 181–201 (2002)

    MATH  Google Scholar 

  4. Dehne, F., Eavis, T., Rau-Chaplin, A.: A cluster architecture for parallel data warehousing. In: ISCC’01, pp. 161–168

    Google Scholar 

  5. Goil, S., Choudhary, A.: A parallel scalable infrastructure for OLAP and data mining. In: IDEAS’99, pp. 178–186

    Google Scholar 

  6. Chen, Y., Dehne, F., Eavis, T., Rau-Chaplin, A.: Parallel ROLAP data cube construction on shared-nothing multiprocessors. In: IPDPS’03, pp. 10–18

    Google Scholar 

  7. Tan, K.-L., Eng, P.-K., Ooi, B.C.: Efficient progressive skyline computation. In: VLDB, vol. 1, pp. 301–310 (2001)

    Google Scholar 

  8. Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: PVLDB’02, pp. 275–286

    Google Scholar 

  9. Papadias, D., Tao, Y., Greg, F., Seeger, B.: Progressive skyline computation in database systems. TODS 1, 41–82 (2005)

    Article  Google Scholar 

  10. Bartolini, I., Ciaccia, P., Patella, M.: Efficient sort-based skyline evaluation. TODS 33(4) (2008). 31

    Google Scholar 

  11. Borzsony, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE’01, pp. 421-430

    Google Scholar 

  12. Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE, vol. 3, pp. 717–719 (2003)

    Google Scholar 

  13. Godfrey, P., Shipley, R., Gryz, J.: Algorithms and analyses for maximal vector computation. VLDBJ 16(1), 5–28 (2007)

    Article  Google Scholar 

  14. Zhang, S., Mamoulis, N., Cheung, D.W.: Scalable skyline computation using object-based space partitioning. In: SIGMOD’09, pp. 483–494

    Google Scholar 

  15. Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: a semantic approach based on decisive subspaces. In: PVLDB, pp. 253–264

    Google Scholar 

  16. Pei, J., Yuan, Y., Lin, X., Jin, W., Ester, M., Liu, Q., Wang, W., Tao, Y., Yu, J.X., Zhang, Q.: Towards multidimensional subspace skyline analysis. TODS 31(4), 1335–1381 (2006)

    Article  Google Scholar 

  17. Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient computation of the skyline cube. In: PVLDB’05, pp. 241–252

    Google Scholar 

  18. Zhao, Y., Deshpande, P., Naughton, J.F.: An array-based algorithm for simultaneous multidimensional aggregate. In: SIGMOD’97

    Google Scholar 

  19. Beyer, K., Ramakrishnan, R.: Bottom-up computation of sparse and iceberg cubes. In: SIGMOD’99

    Google Scholar 

  20. Han, J., Pei, J., Dong, G., Wang, K.: Efficient computation of iceberg cubes with complex measures. In: SIGMOD’01, pp. 1–12

    Google Scholar 

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Acknowledgement

This work was supported by Natural Science Foundation of China (No. 61170003).

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Correspondence to Hongyan Li .

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Zhang, J., Hong, S., Lin, Y., Mou, Y., Li, H. (2017). Finding the Typical Communication Black Hole in Big Data Environment. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

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