Shared-Work Systems: Optimization and Execution Scalability

This lecture discusses the scalability challenges faced by shared-work systems, focusing on optimization and execution. Topics include timely and cost-effective analytics through sharing, scalable shared optimization and execution, informed adaptation through reinforcement learning, and the impact of query volume on throughput and execution. The instructor presents experimental setups, data-query model operators, and the impact of schema on learning. The lecture concludes with insights on the challenges of complex workloads, the importance of specialized operators for execution scalability, and the potential of query partitioning for SLA offering in ad-hoc streaming analytics.