The coming end of Moore’s law requires that data systems be more judicious with computation and resources as the growth in data outpaces the availability of computational resources. Current database systems are eager and aggressively consume resources to immediately and quickly complete the task at hand. Intelligently deferring a task to a later point in time can increase result reuse, reduce work that might later be invalidated, or avoid unnecessary work altogether. In this talk I will introduce CrocodileDB, a resource-efficient database system that automatically optimizes deferment based on user-specification and workload prediction. CrocodileDB integrates new ways of specifying timing information, new query execution policies, new task schedulers, and new data loading schemes. In particular, this talk will highlight two new query execution paradigms, Intermittent Query Processing and Incremental-Aware Query Execution.
Aaron J. Elmore is an Assistant Professor in the Department of Computer Science, and the College of the University of Chicago. Aaron was previously a Postdoctoral Associate at MIT. Aaron’s thesis on Elasticity Primitives for Database-as-a-Service was completed at the University of California, Santa Barbara. His recent research interests focus on building data systems that address the growing data deluge. He is currently an associate editor for SIGMOD record, and has served as co-chair for SIGMOD demonstration track, the inaugural SIGMOD student research competition, and VLDB proceeding editor. Aaron has received the NSF early CAREER award for his work on resource efficient database systems.