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Computing and Combinatorics: 20th International Conference, by Zhipeng Cai, Alexander Zelikovsky, Anu Bourgeois

By Zhipeng Cai, Alexander Zelikovsky, Anu Bourgeois

This booklet constitutes the refereed complaints of the 20 th overseas convention on Computing and Combinatorics, COCOON 2014, held in Atlanta, GA, united states, in August 2014. The fifty one revised complete papers offered have been conscientiously reviewed and chosen from a hundred and ten submissions. there has been a co-organized workshop on computational social networks (CSoNet 2014) the place eight papers have been approved. The papers disguise the next issues: sampling and randomized equipment; good judgment, algebra and automata; database and knowledge buildings; parameterized complexity and algorithms; computational complexity; computational biology and computational geometry; approximation set of rules; graph idea and algorithms; video game idea and cryptography; scheduling algorithms and circuit complexity and CSoNet.

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Additional resources for Computing and Combinatorics: 20th International Conference, COCOON 2014, Atlanta, GA, USA, August 4-6, 2014. Proceedings

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In: PODS 2010: Proceedings of the Twenty-ninth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems of Data, pp. 41–52. ACM, New York (2010) 26. , vol. 1. , Redwood City (1997) 27. : Compressed counting. In: SODA 2009: Proceedings of the Nineteenth Annual ACM -SIAM Symposium on Discrete Algorithms, pp. 412–421. Society for Industrial and Applied Mathematics, Philadelphia (2009) 28. : Open problems in data streams and related topics. pdf (2007) 29. : Space-efficient estimation of statistics over sub-sampled streams.

369–380. Springer, Heidelberg (2004) 21. : On estimating frequency moments of data streams. P. ) APPROX and RANDOM. LNCS, vol. 4627, pp. 479–493. Springer, Heidelberg (2007) 22. : Optimal approximations of the frequency moments of data streams. In: STOC 2005: Proceedings of the Thirty-Seventh Annual ACM Symposium on Theory of Computing, pp. 202–208. ACM, New York (2005) 23. : Univariate discrete distributions. Wiley-Interscience (2005) 24. : On the exact space complexity of sketching and streaming small norms.

Is it possible to increase μ by repeating the same element many times. However, the bound from [3] is for algorithms that are based solely on sampled data. In our model, we first sample and then we can apply an arbitrary algorithm, including the sketching algorithm for F2 from [1]. In this case the lower bound on the 16 V. Braverman and G. Vorsanger number of samples from [3] becomes the lower bound on the length of the sampled stream Dp which is F˜1 . 3 from [29]. The authors explicitly state that their bound is for the case when m = α(n).

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