Multidimensional aggregation plays an important role in decisionmaking systems. A conceptual Multidimensional Aggregation Object (MAO), which consists of measures, scopes and aggregation function, is introduced to represent relationships among aggregators on addressable subsets of data. In the MAO model, aggregations of low-level (intermediate) data can be reused for aggregations on high-level data along the same dimension. Experimental results show that caching intermediate aggregated data can significantly improve performance. Incremental compensating and full recomputing cache-updating approaches are proposed. Execution plans for deriving the aggregations from MAOs are presented. The proposed data aggregation technique can be applied to data-warehousing, OLAP, and data mining tasks.
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- A Multidimensional Aggregation Object (MAO) Framework for Computing Distributive Aggregations
- Springer Berlin Heidelberg
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