Symmetric and Asymmetric Aggregate Function in Massively Parallel Computing

Applications of aggregation for information summary have great meanings in various fields. In big data era, processing aggregate function in parallel is drawing researchers' attention. The aim of our work is to propose a generic framework enabling to map an arbitrary aggregation into a generic algorithm and identify when it can be efficiently executed on modern large-scale data-processing systems. We describe our preliminary results regarding classes of symmetric and asymmetric aggregation that can be mapped, in a systematic way, into efficient MapReduce-style algorithms.

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