Abstract Automatic Tuning of Data-Intensive Analytical

Modern industrial, government, and academic organizations are collecting massive amounts of data (“Big Data”) at an unprecedented scale and pace. The ability to perform timely and cost-effective analytical processing of such large datasets in order to extract deep insights is now a key ingredient for success. These insights can drive automated processes for advertisement placement, improve customer relationship management, and lead to major scientific breakthroughs. Existing database systems are adapting to the new status quo while large-scale dataflow systems (like Dryad and MapReduce) are becoming popular for executing analytical workloads on Big Data. Ensuring good and robust performance auto-matically on such systems poses several challenges. First, workloads often analyze a hybrid mix of structured and unstructured datasets stored in nontraditional data layouts. The structure and properties of the data may not be known upfront, and will evolve over time. Complex analysis techniques and rapid development needs necessitate the use of both declarative and procedural programming languages fo