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Collecting, analyzing and gaining insight from large volumes of data is nowthe norm in an ever increasing number of industries. Data analytics techniques,such as machine learning, are powerful tools used to analyze these largevolumes of data. Synthetic data sets are routinely relied upon to train anddevelop such data analytics methods for several reasons: to generate largerdata sets than are available, to generate diverse data sets, to preserveanonymity in data sets with sensitive information, etc. Processing,transmitting and storing data is a key issue faced when handling large datasets. This paper presents an "On the fly" framework for generating bigsynthetic data sets, suitable for these data analytics methods, that is bothcomputationally efficient and applicable to a diverse set of problems. Anexample application of the proposed framework is presented along with amathematical analysis of its computational efficiency, demonstrating itseffectiveness.

An "On The Fly" Framework for Efficiently Generating Synthetic Big Data Sets