Minimal re-computation for exploratory data analysis in astronomy

We present a technique to automatically minimise the re-computation when a data analysis program is iteratively changed, or added to, as is often the case in exploratory data analysis in astronomy. A typical example is flagging and calibration of demanding or unusual observations where visual inspection suggests improvement to the processing strategy. The technique is based on memoization and referentially transparent tasks. We describe the implementation of this technique for the CASA radio astronomy data reduction package. We also propose a technique for optimising efficiency of storage of memoized intermediate data products using copy-on-write and block level de-duplication and measure their practical efficiency. We find that the minimal recomputation technique improves the efficiency of data analysis while reducing the possibility for user error and improving the reproducibility of the final result. It also aids exploratory data analysis on batch-schedule cluster computer systems.