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中文摘要：
What-If分析能够提供比传统的OLAP（on-line analysis processing）分析更加有意义的决策支持信息．基于历史数据的应用场景假设分析需要更加有效的what-if数据视图生成机制的支持．在传统的delta表合并算法的基础上，提出了基于内存记录指针的deltaMap算法来提高what-if数据视图的合并性能．根据OLAP分析的应用特点，提出了pre-merge算法来处理支持分布式计算的聚集运算．根据不同的假设更新类型，对查询重写算法和Acube算法作了详细的性能测试并进行了全面的性能分析对比，在此基础上提出了what—if分析的代价模型，以应用场景模式、假设更新率、假设更新复杂度、查询结果集的基数作为参数，有效地描述系统what-if查询处理策略，为what—if分析的解决方案提供了一个可行的框架结构．
英文摘要：
A what-if analysis can provide a more meaningful information than classical OLAP （on-line analysis processing）. Multi-Scenario hypothesis upon historical data needs efficient what-if data view support. Two novel algorithms of deltaMap and pre-merge, which can greatly improve the performance of delta table algorithm with set operations, are proposed. To analyze the performance of query re-writing algorithm and delta cube algorithm under different what-if update conditions, a global performance analysis and comparison are presented in the experiment section. This paper proposes a cost model for a what-if analysis processing engine, based on different algorithms with parameters such as application scenario, what-if update rate, complexity of what-if updates, memory storage policy, cardinality of query result set etc, that can be used as a practical framework in a what-if analysis system.
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