Abstract
We present two new algorithms for range-efficient F 0 estimating problem and improve the previously best known result, proposed by Pavan and Tirthapura in [15]. Furthermore, these algorithms presented in our paper also improve the previously best known result for Max-Dominance Norm Problem.
The work described in this paper was fully supported by a grant from CityU (SRG 7001969).
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References
Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. Journal of Computer and System Sciences 58, 137–147 (1999)
Bar-Yossef, Z., Kumar, R., Sivakumar, D.: Reductions in streaming algorithms, with an application to counting triangles in graphs. In: Proceedings of 13th ACM-SIAM Symposium on Discrete Algorithms, pp. 623–632 (2002)
Trevisan, L., et al.: Counting Distinct Elements in a Data Stream. In: Rolim, J.D.P., Vadhan, S.P. (eds.) RANDOM 2002. LNCS, vol. 2483, pp. 1–10. Springer, Heidelberg (2002)
Carter, J.L., Wegman, M.N.: Universal classes of hash functions. Journal of Computer and System Sciences 18(2), 143–154 (1979)
Cormode, G., et al.: Comparing data streams using hamming norms (How to zero in). In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 335–345 (2002)
Muthukrishnan, S.M., Cormode, G.: Estimating Dominance Norms of Multiple Data Streams. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 148–160. Springer, Heidelberg (2003)
Cormode, G.: Stable distributions for stream computations: it’s as easy as 0,1,2. In: Workshop on Management and Processing of Massive Data Streams, at FCRC (2003)
Flajolet, P., Durand, M.: Loglog Counting of Large Cardinalities. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 605–617. Springer, Heidelberg (2003)
Flajolet, P., Martin, G.N.: Probabilistic counting algorithms for data base applications. Journal of Computer and System Sciences 31, 182–209 (1985)
Ganguly, S., Garofalakis, M., Rastogi, R.: Tracking set-expression cardinalities over continuous update streams. The International Journal on Very Large Data Bases 13, 354–369 (2004)
Giroire, F.: Order statistics and estimating cardinalities of massive data sets. Discrete Mathematics and Theoretical Computer Science AD, 157–166 (2005)
Indyk, P.: Stable distributions, pseudorandom generators, embeddings and data stream computation. In: Proceedings of the 40th Symposium on Foundations of Computer Science, pp. 189–197 (2000)
Muthukrishnan, S.: Data streams: algorithms and applications. Invited talk at 14th ACM-SIAM Symposium on Discrete Algorithms. Available from http://athos.rutgers.edu/~muthu/stream-1-1.ps
Nisan, N.: Pseudorandom generators for space-bounded computation. In: Proceedings of the 22nd Symposium on Theory of Computation, pp. 204–212 (1990)
Pavan, A., Tirthapura, S.: Range-efficient computation of F 0 over massive data stream. In: Proceedings of the 21st International Conference on Data Engineering, pp. 32–43 (2005)
Wegman, M.N., Carter, J.L.: New hash functions and their use in authentication and set equality. Journal of Computer and System Science 22, 265–279 (1981)
Weron, R.: On the Chambers-Mallows-Stuck method for simulating skewed stable random variables. Technical report, Hugo Steinhaus Center for Stochastic Methods, Wrocław (1996)
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Sun, H., Poon, C.K. (2007). Two Improved Range-Efficient Algorithms for F 0 Estimation. In: Cai, JY., Cooper, S.B., Zhu, H. (eds) Theory and Applications of Models of Computation. TAMC 2007. Lecture Notes in Computer Science, vol 4484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72504-6_60
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DOI: https://doi.org/10.1007/978-3-540-72504-6_60
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