The model-summary problem and a solution for trees

Modern science is collecting massive amounts of data from sensors, instruments, and through computer simulation. It is widely believed that analysis of this data will hold the key for future scientific breakthroughs. Unfortunately, deriving knowledge from large high-dimensional scientific datasets is difficult. One emerging answer is exploratory analysis using data mining; but data mining models that accurately capture natural processes tend to be very complex and are usually not intelligible. Scientists therefore generate model summaries to find the most important patterns learned by the model. We formalize the model-summary problem and introduce it as a novel problem to the database community. Generating model summaries creates serious data management challenges: Scientists usually want to analyze patterns in different ¿slices¿ and ¿dices¿ of the data space, comparing the effects of various input variables on the output. We propose novel techniques for efficiently generating such summaries for the popular class of tree-based models. Our techniques leverage workload structure on multiple levels. We also propose a scalable implementation of our techniques in MapReduce. For both sequential and parallel implementation, we achieve speedups of one or more orders of magnitude over the naive algorithm, while guaranteeing the exact same results.