Efficient query processing and optimizations for big data workloads

从感官数据采集吞吐量到处理器功率、存储和带宽，计算的各个方面都在经历指数级增长。这些指数级的改进正在推动大数据革命。大数据应用包括以流方式不断产生的大量数据（例如，传感器读数、日志、点击等）。此外，典型的大数据研究和分析工作流程是迭代的。也就是说，使用一些数据参数构建模型，然后使用前一个建模阶段的输出迭代地改进模型。这两种原语，即流数据生成和迭代分析工作流，都为优化提供了很多机会。

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.