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Data skew primarily refers to a non uniform distribution in a dataset. Skewed distribution can follow common distributions (e.g., Zipfian, Gaussian, Poisson), but many studies consider Zipfian [3] distribution to model skewed datasets. Using a real bibliographic database, [1] provides real-world parameters for the Zipf distribution model. The direct impact of data skew on parallel execution of complex database queries is a poor load balancing leading to high response time.
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Walton et al. [2] classify the effects of skewed data distribution on a parallel execution, distinguishing intrinsic skew from partition skew. Intrinsic skew is skew inherent in the dataset (e.g., there are more citizens in Paris than in Waterloo) and is thus called Attribute value skew (AVS).Partition skew occurs on parallel implementations when the workload is not evenly distributed between nodes, even when input data is uniformly...
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Lynch C. Selectivity estimation and query optimization in large databases with highly skewed distributions of column values. In Proc. 14th Int. Conf. on Very Large Data Bases, 1988, pp. 240–251.
Walton C.B., Dale A.G., and Jenevin R.M. A taxonomy and performance model of data skew effects in parallel joins. In Proc. 17th Int. Conf. on Very Large Data Bases, 1991, pp. 537–548.
Zipf G.K. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley, Reading, MA, 1949.
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© 2009 Springer Science+Business Media, LLC
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Bouganim, L. (2009). Data Skew. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1088
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DOI: https://doi.org/10.1007/978-0-387-39940-9_1088
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
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