Database-Inspired Optimizations for Statistical Analysis
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Computing complex statistics on large amounts of data is no longer a corner case, but a daily challenge. However, current tools such as GNU R were not built to efficiently handle large data sets. We propose to vastly improve the execution of R scripts by interpreting them as a declaration of intent rather than an imperative order set in stone. This allows us to apply optimization techniques from the columnar data management research field. We have implemented several of these optimizers in Renjin, an open-source execution environment for R scripts targeted at the Java virtual machine. The demonstration of our approach using a series of micro-benchmarks and experiments on complex survey analysis show orders-of-magnitude improvements in analysis cost.