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As datasets grow it becomes infeasible to process them completely with adesired model. For giant datasets, we frame the order in which computation isperformed as a decision problem. The order is designed so that partialcomputations are of value and early stopping yields useful results. Ourapproach comprises two related tools: a decision framework to choose the orderto perform computations, and an emulation framework to enable estimation of theunevaluated computations. The approach is applied to the problem of computingsimilarity matrices, for which the cost of computation grows quadratically withthe number of objects. Reasoning about similarities before they are observedintroduces difficulties as there is no natural space and hence comparisons aredifficult. We solve this by introducing a computationally convenient form ofmultidimensional scaling we call `data directional scaling'. High qualityestimation is possible with massively reduced computation from the naiveapproach, and can be scaled to very large matrices. The approach is applied tothe practical problem of assessing genetic similarity in population genetics.The use of statistical reasoning in decision making for large scale problemspromises to be an important tool in applying statistical methodology to BigData.

A general decision framework for structuring computation using Data Directional Scaling to process massive similarity matrices