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    • 11. 发明授权
    • Parallel classification for data mining in a shared-memory multiprocessor system
    • 在共享内存多处理器系统中进行数据挖掘的并行分类
    • US06230151B1
    • 2001-05-08
    • US09061808
    • 1998-04-16
    • Rakesh AgrawalChing-Tien HoMohammed J. Zaki
    • Rakesh AgrawalChing-Tien HoMohammed J. Zaki
    • G06F1518
    • G06F17/30598G06F8/453G06F2216/03Y10S707/99942
    • A method and system for generating a decision-tree classifier in parallel in a shared-memory multiprocessor system is disclosed. The processors first generate in the shared memory an attribute list for each record attribute. Each attribute list is assigned to a processor. The processors independently determine the best splits for their respective assigned lists, and cooperatively determine a global best split for all attribute lists. The attribute lists are reassigned to the processors and split according to the global best split into the lists for child nodes. The split attribute lists are again assigned to the processors and the process is repeated for each new child node until each attribute list for the new child nodes includes only tuples of the same record class or a fixed number of tuples.
    • 公开了一种在共享存储器多处理器系统中并行生成决策树分类器的方法和系统。 处理器首先在共享存储器中生成每个记录属性的属性列表。 每个属性列表都分配给一个处理器。 处理器独立地确定其各自分配的列表的最佳分割,并且协作地确定所有属性列表的全局最佳分割。 将属性列表重新分配给处理器,并将根据全局最佳拆分拆分为子节点列表。 分割属性列表再次分配给处理器,并且对于每个新的子节点重复该过程,直到新的子节点的每个属性列表仅包括相同记录类或固定数量的元组的元组。
    • 12. 发明授权
    • Method and system for performing range max/min queries on a data cube
    • 在数据立方体上执行范围最大/最小查询的方法和系统
    • US5926820A
    • 1999-07-20
    • US808046
    • 1997-02-27
    • Rakesh AgrawalChing-Tien HoNimrod Megiddo
    • Rakesh AgrawalChing-Tien HoNimrod Megiddo
    • G06F17/30
    • G06F17/30327G06F17/30333G06F17/30489Y10S707/99935
    • A method for performing a range max/min query in a database, in which the data is represented as a multi-dimensional data cube, is disclosed. The method comprises the steps of: partitioning the data cube into multi-level multi-dimensional blocks which are represented by a tree structure; determining the index to the maximum or minimum value for each block; generating a range max/min result from the values of the cells selected from the cells in the query region Q, and the cells referenced by the indexes at the nodes corresponding to the cells in the query region Q, using the tree structure and determined cell indexes. A branch-and-bound method is used to repeatedly reduce the size of the query region from a cell within the region, based on sub-trees whose roots are cells in the region. To further improve the method performance, one or more reference arrays may also be used to quickly traverse the tree in determining the max/min cell indexes.
    • 公开了一种在数据库中执行范围最大/最小查询的方法,其中数据被表示为多维数据立方体。 该方法包括以下步骤:将数据立方体分割成由树结构表示的多层多维块; 确定每个块的最大值或最小值的索引; 使用树结构和确定的单元从从查询区域Q中的单元格选择的单元格的值和由与查询区域Q中的单元相对应的节点处的索引引用的单元生成范围最大/最小的结果 索引。 使用分支绑定方法,以区域内的单元格为基础的子树,重复地减少区域内的单元格的查询区域的大小。 为了进一步改进方法性能,也可以使用一个或多个参考阵列来确定最大/最小信元索引。
    • 16. 发明授权
    • Method for generating hierarchical fault-tolerant mesh architectures
    • 生成层次容错网格架构的方法
    • US5513313A
    • 1996-04-30
    • US521645
    • 1995-08-31
    • Jehoshua BruckRobert E. CypherChing-Tien Ho
    • Jehoshua BruckRobert E. CypherChing-Tien Ho
    • G06F11/20G06F11/00
    • G06F11/2051
    • A method is disclosed, for use with a multiprocessing hardware mesh architecture including nodes and a network of interconnections between the nodes, for defining and implementing a target logical mesh architecture utilizing a given subset of the nodes and the interconnections of the hardware architecture. Typically, the hardware mesh architecture includes redundant nodes and interconnections, sot hat the target logical mesh architecture may be defined from the hardware architecture several different ways. As a consequence, the target logical mesh architecture may be defined even in the presence of faulty nodes or interconnections in the hardware architecture. Frequently, the logical mesh is defined in terms of some regular pattern of interconnections. The method of the invention facilitates the definition of the desired logical mesh architecture from the hardware architecture, given the possibility that one or more faults are present, by initially defining logical blocks of nodes from among the functional nodes of the hardware architecture. Then, functional edges between the nodes defined within the logical blocks are defined as logical interconnections between the nodes of the logical blocks, such that the logical blocks, with the interconnections, are structurally consistent with portions of the logical mesh. Finally, additional edges are defined as logical interconnections between nodes in different logical blocks, these additional edges also being consistent with the structure of the logical mesh. The result is that the logical mesh has been fully defined from functional nodes and interconnections in the hardware architecture.
    • 公开了一种用于包括节点和节点之间的互连网络的多处理硬件网格架构的方法,用于使用节点的给定子集和硬件架构的互连来定义和实现目标逻辑网格架构。 通常,硬件网格架构包括冗余节点和互连,所以目标逻辑网格架构可以从硬件架构中定义几种不同的方式。 因此,即使在存在硬件架构中的故障节点或互连的情况下,也可以定义目标逻辑网格架构。 通常,逻辑网格是根据一些规则的互连模式来定义的。 考虑到存在一个或多个故障的可能性,本发明的方法通过从硬件体系结构的功能节点中首先定义逻辑块的节点,有助于从硬件架构定义期望的逻辑网格架构。 然后,在逻辑块之间定义的节点之间的功能边界被定义为逻辑块的节点之间的逻辑互连,使得具有互连的逻辑块在结构上与逻辑网格的部分一致。 最后,额外的边被定义为不同逻辑块中的节点之间的逻辑互连,这些附加边也与逻辑网格的结构一致。 结果是逻辑网格已经从硬件架构中的功能节点和互连完全定义。
    • 20. 发明授权
    • Method and system for performing range-sum queries on a data cube
    • 在数据立方体上执行范围和查询的方法和系统
    • US5799300A
    • 1998-08-25
    • US764564
    • 1996-12-12
    • Rakesh AgrawalChing-Tien HoRamakrishnan Srikant
    • Rakesh AgrawalChing-Tien HoRamakrishnan Srikant
    • G06F17/30
    • G06F17/30592G06F17/30489Y10S707/99935
    • A method for performing a range-sum query in a database, in which the data is represented as a multi-dimensional data cube, is disclosed. The method comprises the steps of: selecting a subset of the dimensions of the data cube; computing a set of prefix-sums along the selected dimensions, based on the aggregate values in the cube corresponding the queried ranges; and generating a range-sum result based on the computed prefix-sums. Two d-dimensional arrays A and P are used for representing the data cube and the prefix-sums of the data cube, respectively. By maintaining the prefix-sum array P of the same size as the data cube, all range queries for a given cube can be answered in constant time, irrespective of the size of the sub-cube circumscribed by a query, using the inverse binary operator of the SUM operator. Alternatively, only auxiliary information for any user-specified fraction of the size of the d-dimensional data cube is maintained, to minimize the required system storage. The answer to a range query may now require access to some cells of the data cube in addition to the auxiliary information, but the average time complexity is still reduced significantly.
    • 公开了一种在数据库中执行范围和查询的方法,其中数据被表示为多维数据立方体。 该方法包括以下步骤:选择数据立方体的维度的子集; 基于对应于查询范围的立方体中的聚合值来计算沿着所选维度的一组前缀和; 以及基于所计算的前缀和来生成范围和结果。 两个d维数组A和P分别用于表示数据立方体和数据立方体的前缀和。 通过保持与数据立方体相同大小的前缀和数组P,给定多维数据集的所有范围查询可以在常量时间内被应答,而不管查询所包围的子立方体的大小,使用反二进制运算符 的SUM操作员。 或者,仅维护d维数据立方体的大小的任何用户指定分数的辅助信息,以最小化所需的系统存储。 范围查询的答案现在除了辅助信息之外,还可能需要访问数据立方体的某些单元格,但平均时间复杂度仍然显着降低。