Autonomously improving query evaluations over multidimensional data in distributed hash tables

The proliferation of observational devices and sensors with networking capabilities has led to growth in both the rates and sources of data that ultimately contribute to extreme-scale data volumes. Datasets generated in such settings are often multidimensional, with each dimension accounting for a feature of interest. We posit that efficient evaluation of queries over such datasets must account for both the distri-bution of data values and the patterns in the queries them-selves. Configuring query evaluation by hand is infeasible given the data volumes, dimensionality, and the rates at which new data and queries arrive. In this paper, we de-scribe our algorithm to autonomously improve query evalu-ations over voluminous, distributed datasets. Our approach autonomously tunes for the most dominant query patterns and distribution of values across a dimension. We evaluate our algorithm in the context of our system, Galileo, which is a hierarchical distributed hash table used for managing geospatial, time-series data. Our system strikes a balance between memory utilization, fast evaluations, and search space reductions. Empirical evaluations reported here are performed on a dataset that is multidimensional and com-prises a billion files. The schemes described in this work are broadly applicable to any system that leverages distributed hash tables as a storage mechanism