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Current data-management systems and analysis tools fail to meet scientists' data-intensive needs. A "data vault" approach lets researchers effectively and efficiently explore and analyze information. [ABSTRACT FROM PUBLISHER]

Automatic recycling of intermediate results to improve both query response time and throughput is a grand challenge for state-of-the-art databases. Tuples are loaded and streamed through a tuple-at-a-time processing pipeline, avoiding materialization of intermediates as much as possible. This limits the opportunities for reuse of overlapping computations to DBA-defined materialized views and function/result cache tuning. In contrast, the operator-at-a-time execution paradigm produces fully materialized results in each step of the query plan. To avoid resource contention, these intermediates are evicted as soon as possible. In this article we study an architecture that harvests the byproducts of the operator-at-a-time paradigm in a column-store system using a lightweight mechanism, the recycler. The key challenge then becomes the selection of the policies to admit intermediates to the resource pool, to determine their retention period, and devise the eviction strategy when facing resource limitations. The proposed recycling architecture has been implemented in an open-source system. An experimental analysis against the TPC-H ad-hoc decision support benchmark and a complex, real-world application (SkyServer) demonstrates its effectiveness in terms of self-organizing behavior and its significant performance gains. The results indicate the potentials of recycling intermediates and charts a route for further development of database kernels. [ABSTRACT FROM AUTHOR]