Nesime Tatbul

TL;DR: Neural Optimizer as discussed by the authors is a learning-based query optimizer that relies on deep neural networks to generate query executions plans, which can adapt to underlying data patterns and is robust to estimation errors.

TL;DR: BigDAWG is presented, a reference implementation of a new architecture for "Big Data" applications that showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics.

TL;DR: SECRET is a descriptive model that allows users to analyze the behavior of systems and understand the results of window-based queries for a broad range of heterogeneous SPEs.

TL;DR: The demonstration will illustrate the dynamic resource management, query optimization and high availability mechanisms employed by Borealis, using visual performance-monitoring tools as well as the gaming experience.

TL;DR: This work presents a simple transaction model for streams that integrates seamlessly with a traditional OLTP system, and provides both ACID and stream-oriented guarantees, and compares S-Store to two state-of-the-art streaming systems, Esper and Apache Storm, and shows how S- store can sometimes exceed their performance while at the same time providing stronger correctness guarantees.