Discusses query optimization techniques for data processing at massive scale, comparing optimization strategies and sharing opportunities to reduce processing costs.
Explores scalability challenges in shared-work systems, emphasizing optimization and execution, experimental setups, data-query operators, and the impact of schema on learning.
Covers MLIR, a compiler infrastructure for domain-specific computation, emphasizing the importance of multiple abstraction levels and higher-level semantics.
