In Proceedings of the ACM SIGMOD Conference, 2015.
With the rapidly decreasing prices for storage and storage systems ever larger
data sets become economical. While only few years ago only successful transactions
would be recorded in sales systems, today every user interaction will be stored for ever deeper
analysis and richer user modeling. This has led to the development of
big data systems, which offer high scalability and novel forms of analysis.
Due to the rapid development and ever increasing variety of the big data
landscape, there is a pressing need for tools for testing and benchmarking.
Vendors have little options to showcase the performance of their systems but to use trivial data sets like TeraSort or WordCount. Since customers' real data is typically subject to privacy regulations and rarely can be utilized, simplistic proof-of-concepts have to be used, leaving both, customers and vendors, unclear of the target use-case performance. As a solution, we present an automatic approach to data synthetization from existing data sources. Our system enables a fully automatic generation of large amounts of complex, realistic, synthetic data.
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