Efficient query processing and optimizations for big data workloads

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Every aspect of computing has been experiencing exponential growth, from sensory data acquisition throughput to processor power, storage and bandwidth. These exponential improvements are enabling the big data revolution. Big data applications consist of volumes of data that are constantly produced in a streaming fashion (e.g., sensor readings, logs, click-through etc.). In addition typical research and analysis workflows on big data are iterative. Namely a model is built using some data parameters, then iteratively refined using the output of the previous modeling phase. Both such primitives, namely streaming data generation and iterative analysis workflows, provide a lot of opportunity for optimizations.