借助最新的商业智能理论和方法,建立面向中小型企业的商业智能平台软件MDDAS(Multi-Dimensionality Data Analyze System,多维数据分析系统)。MDDAS应用固定报表、仪表盘、电子地图和OLAP等工具,在序列趋势模型、周期模型、结构模型、...借助最新的商业智能理论和方法,建立面向中小型企业的商业智能平台软件MDDAS(Multi-Dimensionality Data Analyze System,多维数据分析系统)。MDDAS应用固定报表、仪表盘、电子地图和OLAP等工具,在序列趋势模型、周期模型、结构模型、对比模型、关联模型、统计模型、模拟模型等模型技术的辅助下,对原料,产品,企业,供应商,销售,库存,市场综合,效益综合,品牌综合等分析评价指标在不同统计口径(时间口径、空间口径等)下进行分析、评价、监督和预警,给企业管理决策者提供及时、准确的信息。MDDAS具有通用性、灵活性、敏捷性,可以为应用MDDAS的中小型企业提供有益的帮助。展开更多
Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is...Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.展开更多
文摘借助最新的商业智能理论和方法,建立面向中小型企业的商业智能平台软件MDDAS(Multi-Dimensionality Data Analyze System,多维数据分析系统)。MDDAS应用固定报表、仪表盘、电子地图和OLAP等工具,在序列趋势模型、周期模型、结构模型、对比模型、关联模型、统计模型、模拟模型等模型技术的辅助下,对原料,产品,企业,供应商,销售,库存,市场综合,效益综合,品牌综合等分析评价指标在不同统计口径(时间口径、空间口径等)下进行分析、评价、监督和预警,给企业管理决策者提供及时、准确的信息。MDDAS具有通用性、灵活性、敏捷性,可以为应用MDDAS的中小型企业提供有益的帮助。
文摘Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.
