Abstract: Techniques are disclosed for multi-granularity hierarchical aggregate selection based on update, storage and response constraints. For example, for a temporal hierarchy of aggregation statistics associated with a plurality of database records, wherein the temporal hierarchy comprises two or more aggregation statistics levels and each level has a different temporal granularity associated therewith, a method comprises iteratively modifying the temporal hierarchy to at least one of: (a) minimize a storage usage cost while satisfying a temporal hierarchy update constraint and a query response time constraint; (b) reduce a temporal hierarchy update time and a query response time while satisfying a storage usage constraint; and (c) minimize a query response time for frequently applied queries that do not shift in time while satisfying the storage usage constraint, wherein the resulting temporal hierarchy that achieves at least one of (a), (b) and (c) is identified as an optimal temporal hierarchy.

Abstract: Techniques are disclosed for multi-granularity hierarchical aggregate selection based on update, storage and response constraints. For example, for a temporal hierarchy of aggregation statistics associated with a plurality of database records, wherein the temporal hierarchy comprises two or more aggregation statistics levels and each level has a different temporal granularity associated therewith, a method comprises iteratively modifying the temporal hierarchy to at least one of: (a) minimize a storage usage cost while satisfying a temporal hierarchy update constraint and a query response time constraint; (b) reduce a temporal hierarchy update time and a query response time while satisfying a storage usage constraint; and (c) minimize a query response time for frequently applied queries that do not shift in time while satisfying the storage usage constraint, wherein the resulting temporal hierarchy that achieves at least one of (a), (b) and (c) is identified as an optimal temporal hierarchy.