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    • 3. 发明授权
    • Custom grouping for dimension members
    • 维度成员的自定义分组
    • US07562086B2
    • 2009-07-14
    • US11167413
    • 2005-06-27
    • Akshai M. MirchandaniAlexander BergerThomas P. ConlonEdward Melomed
    • Akshai M. MirchandaniAlexander BergerThomas P. ConlonEdward Melomed
    • G06F7/00
    • G06F17/30592Y10S707/99942
    • Custom grouping for dimension members of an On-Line Analytical Processing (OLAP) cube is facilitated by an interface component that receives a grouping from a user. A modification component alters an attribute of a session OLAP data cube or a virtual OLAP multi-dimensional data cube so that the change becomes readily available without the time-consuming delay associated with rebuilding or reprocessing the underlying structure. A data scheme generation component produces a data definition language (DDL) definition of a user defined attribute executed by an execution component to affect the change to a data cube. Custom groupings can provide an intermediate dimension level for drill down and/or drill up or roll-up between a level with few members and a level with numerous members. A separate dimensional attribute or level can be created and a group defined comprising or mapping a set or collection of discrete members.
    • 由在线分析处理(OLAP)多维数据集的维度成员进行自定义分组,该接口组件从用户接收分组。 修改组件更改会话OLAP数据立方体或虚拟OLAP多维数据立方体的属性,以便更改变得容易获得,而不需要与重建或重新处理底层结构相关的耗时的延迟。 数据方案生成组件产生由执行组件执行的用户定义属性的数据定义语言(DDL)定义,以影响对数据立方体的更改。 自定义分组可以提供中间维度级别,用于在具有少量成员的级别和具有众多成员的级别之间进行深入和/或钻取或汇总。 可以创建单独的维度属性或级别,并且定义的组包括或映射离散成员的集合或集合。
    • 5. 发明申请
    • Discretization of dimension attributes using data mining techniques
    • 使用数据挖掘技术离散维度属性
    • US20060005121A1
    • 2006-01-05
    • US10881262
    • 2004-06-30
    • Alexander BergerEdward MelomedRaman IyerThulusalamatom Krishnamurthi Anand
    • Alexander BergerEdward MelomedRaman IyerThulusalamatom Krishnamurthi Anand
    • G06F17/30
    • G06F16/283G06F2216/03
    • In order to allow the use of data in dimension attributes for grouping members of a dimension, dimension attribute data is analyzed so it can be used as if it were data for a categorical attribute with a manageable number of states. The values possible for the dimension attribute are divided into groups. This is done by determining the distribution of data. An approximate distribution may be determined (by sampling some data) or an actual distribution may be determined (by sampling all data). The distribution is then used to determine the groups into which the range of data values will be divided. Each group is then treated as if it were a state for a categorical-type dimension attribute. A state can be determined for a member by determining which subrange contains the value for the dimension attribute for the member. The number of groups can be determined by a user or determined dynamically, e.g. to best fit the distribution found. The group data may be stored in order to allow further conversion of future cases.
    • 为了允许使用尺度属性中的数据来对维度的成员进行分组,对维度属性数据进行分析,因此可以将其用作具有可管理数量状态的分类属性的数据。 维度属性可能的值被分为几组。 这是通过确定数据的分布来完成的。 可以确定近似分布(通过对某些数据进行采样),或者可以确定实际分布(通过对所有数据进行采样)。 然后使用该分布来确定数据值范围将被划分到的组。 然后将每个组视为对于分类型维度属性的状态。 可以通过确定哪个子范围包含成员的维度属性的值来确定成员的状态。 组的数量可以由用户确定或动态地确定,例如, 最适合发现的分布。 可以存储组数据以便进一步转换未来的情况。