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Workloads that comb through vast amounts of data are gaining importance inthe sciences. These workloads consist of "needle in a haystack" queries thatare long running and data intensive so that query throughput limitsperformance. To maximize throughput for data-intensive queries, we put forthLifeRaft: a query processing system that batches queries with overlapping datarequirements. Rather than scheduling queries in arrival order, LifeRaftexecutes queries concurrently against an ordering of the data that maximizesdata sharing among queries. This decreases I/O and increases cache utility.However, such batch processing can increase query response time by starvinginteractive workloads. LifeRaft addresses starvation using techniques inspiredby head scheduling in disk drives. Depending upon the workload saturation andqueuing times, the system adaptively and incrementally trades-off processingqueries in arrival order and data-driven batch processing. Evaluating LifeRaftin the SkyQuery federation of astronomy databases reveals a two-foldimprovement in query throughput.

LifeRaft: Data-Driven, Batch Processing for the Exploration of Scientific Databases