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Myriad: Scalable and Expressive Data Generation.
Citation key AlexandrovTM2012
Author Alexander Alexandrov, Kostas Tzoumas, Volker Markl
Pages 1890-1893
Year 2012
DOI 10.14778/2367502.2367530
Journal Proceedings of the VLDB Endowment
Volume 5
Number 12
Abstract The current research focus on Big Data systems calls for a rethinking of data generation methods. The traditional sequential data generation approach is not well suited to large-scale systems as generating a terabyte of data may require days or even weeks depending on the number of constraints imposed on the generated model. We demonstrate Myriad, a new data generation toolkit that enables the specification of semantically rich data generator programs that can scale out linearly in a shared-nothing environment. Data generation programs built on top of Myriad implement an efficient parallel execution strategy leveraged by the extensive use of pseudo-random number generators with random access support.
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