Perturbation analysis of database queries
Brett Walenz, Jun Yang · Proceedings of the VLDB Endowment · 2016
We present a system, Perada , for parallel perturbation analysis of database queries. Perturbation analysis considers the results of a query evaluated with (a typically large number of) different parameter settings, to help discover leads and evaluate claims from data. Perada simplifies the development of general, ad hoc perturbation analysis by providing a flexible API to support a variety of optimizations such as grouping, memoization, and pruning; by automatically optimizing performance through run-time observation, learning, and adaptation; and by hiding the complexity of concurrency and failures from its developers. We demonstrate Perada's efficacy and efficiency with real workloads applying perturbation analysis to computational journalism.