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Aggregate queries are one of the most resource intensive operations for databases systems. Despite scanning and processing large data sets, they only return relatively small outputs, making them predestined for reuse in future queries. The challenge is to manage the infinite number of possible data areas that can be selected for caching, identify the relevant ones and materialize the corresponding aggregates on a reusable level of granularity. While analytical database systems often save materialized data in cubes of aggregated chunks this is not feasible in systems with transactional insert and change processes. The authors present the concept Dynamic Materialized Aggregate Views (DMAV) that materializes aggregates based on mixed application workloads.
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