Time and space optimization for processing groups of multi-dimensional scientific queries

Data analysis applications in areas as diverse as remote sensing and telepathology require operating on and pro-cessing very large datasets. For such applications to exe-cute efficiently, careful attention must be paid to the storage, retrieval, and manipulation of the datasets. This paper ad-dresses the optimizations performed by a high performance database system that processes groups of data analysis re-quests for these applications, which we call queries. The system performs end-to-end processing of the requests, for-mulated as PostgreSQL declarative queries. The queries are converted into imperative descriptions, multiple imper-ative descriptions are merged into a single execution plan, the plan is optimized to decrease execution time via com-mon compiler optimization techniques, and, finally, the plan is optimized to decrease memory consumption. The last two steps effectively reduce both the time and space to execute query groups, as shown in the experimental results.