Specially, the paper presented the creating of dimension table, fact table of multi-dimension data model, analyzed the construction of data model, data extraction and data maintenance tools.
其中重点介绍了多维数据模型的维表、事实表的结构设计，分析了数据模型的构建、数据抽取工具和数据维护工具的设计及实现。
The module of generalized database model was played in generalized model service, and multi-dimension data was analyzed and transferred, so decision information was finally obtained.
广义模型服务调用广义模型库中的模块，对多维数据进行分析和转化，从而获得最终的决策信息。
An optimization method for reasoning results is presented, such as recursive grey fitting model for single sequence and attribute correlativity model for multi-dimension data.
分别在单序列时建立递进灰拟合模型，在多维数据集时利用属性相关性，对插值结果进行学习优化。
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