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    • 1. 发明授权
    • Importance of semantic web resources and semantic associations between two resources
    • 语义网页资源的重要性和两个资源之间的语义关联
    • US07490094B2
    • 2009-02-10
    • US10840175
    • 2004-05-06
    • Bhuvan BambaSougata Mukherjea
    • Bhuvan BambaSougata Mukherjea
    • G06F17/30
    • G06F17/30882G06F17/30864Y10S707/99933Y10S707/99953
    • The importance of semantic web resources is determined. Some resources are classes (of the type RDFS:Class); others are non-class resources. Non-class resources belong to one or more classes while class resources are subclasses of one or more parent classes. A subjectivity score is determined for each resource of a set of resources based on the number of Resource Description Format (RDF) triples of which the resource is the subject and predefined weights of properties of the triples. An objectivity score is determined for each resource based on the number of RDF triples of which the resource is the object and predefined weights of the properties of the triples. The importance of a class is determined from the respective subjectivity score and objectivity score, and a factor relating to the importance of the class's parents.
    • 确定语义Web资源的重要性。 一些资源是类(类型为RDFS:Class); 其他是非课程资源。 非类资源属于一个或多个类,而类资源是一个或多个父类的子类。 基于资源是主题的资源描述格式(RDF)三元组的数量和三元组的属性的预定权重,确定一组资源的每个资源的主观评分。 基于资源是对象的RDF三元组的数量和三元组的属性的预定权重,为每个资源确定客观评分。 课程的重要性取决于各自的主观评分和客观评分,以及与班级父母重要性有关的因素。
    • 2. 发明申请
    • Importance of semantic web resources and semantic associations between two resources
    • 语义网页资源的重要性和两个资源之间的语义关联
    • US20050251805A1
    • 2005-11-10
    • US10840175
    • 2004-05-06
    • Bhuvan BambaSougata Mukherjea
    • Bhuvan BambaSougata Mukherjea
    • G06F9/46G06F17/30
    • G06F17/30882G06F17/30864Y10S707/99933Y10S707/99953
    • The importance of semantic web resources is determined. Some resources are classes (of the type RDFS:Class); others are non-class resources. Non-class resources belong to one or more classes while class resources are subclasses of one or more parent classes. A subjectivity score is determined for each resource of a set of resources based on the number of Resource Description Format (RDF) triples of which the resource is the subject and predefined weights of properties of the triples. An objectivity score is determined for each resource based on the number of RDF triples of which the resource is the object and predefined weights of the properties of the triples. The importance of a class is determined from the respective subjectivity score and objectivity score, and a factor relating to the importance of the class's parents.
    • 确定语义Web资源的重要性。 一些资源是类(类型为RDFS:Class); 其他是非课程资源。 非类资源属于一个或多个类,而类资源是一个或多个父类的子类。 基于资源是主题的资源描述格式(RDF)三元组的数量和三元组的属性的预定权重,确定一组资源的每个资源的主观评分。 基于资源是对象的RDF三元组的数量和三元组的属性的预定权重,为每个资源确定客观评分。 课程的重要性取决于各自的主观评分和客观评分,以及与班级父母重要性有关的因素。
    • 3. 发明授权
    • Scalable performance-based volume allocation in large storage controller collections
    • 大型存储控制器集合中可扩展的基于性能的卷分配
    • US08412890B2
    • 2013-04-02
    • US13043247
    • 2011-03-08
    • Bhuvan BambaMadhukar R. Korupolu
    • Bhuvan BambaMadhukar R. Korupolu
    • G06F12/00
    • G06F9/5083G06F9/5016
    • A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system in a plurality of component layers is analyzed to yield gap values for each node (e.g., a bottom-up estimation). The gap value for each node is an estimate of the amount in GB of the new workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system (e.g., a top-down selection). The volumes may be applied to a storage workload in the order of the allocation list and the gap values and list are updated.
    • 公开了可应用于大型存储控制器集合中的可扩展的基于性能的卷分配技术。 分析表示多个组件层中的存储系统的互连组件的多个节点的全局资源树,以产生每个节点的间隙值(例如,自底向上估计)。 每个节点的间隙值是可以在该节点的子树中分配的新工作负载的GB量的估计,而不超过该子树中任何节点的性能和空间界限。 进一步分析全局资源树的间隙值以生成存储系统的卷的有序分配列表(例如,自上而下的选择)。 卷可以按照分配列表的顺序应用于存储工作负载,并且间隙值和列表被更新。
    • 4. 发明申请
    • INTEGRATED PLACEMENT PLANNING FOR HETEROGENOUS STORAGE AREA NETWORK DATA CENTERS
    • 用于异构存储区域网络数据中心的集成放置规划
    • US20080295094A1
    • 2008-11-27
    • US11752292
    • 2007-05-22
    • Madhukar R. KorupoluAameek SinghBhuvan Bamba
    • Madhukar R. KorupoluAameek SinghBhuvan Bamba
    • G06F9/455
    • G06F9/5066G06F9/5077H04L67/1002H04L67/1008H04L67/101H04L67/1021H04L67/1023H04L67/1097
    • A program, method and system are disclosed for planning the placement of a collection of applications in a heterogeneous storage area network data center. The program, method, and system disclosed deal with the coupled placement of virtual machine applications within a resource graph, with each application requiring a certain amount of CPU resources and a certain amount of storage resources from the connected resource node pairs within the resource graph. The resource nodes in the graph provide either storage resources, CPU resources, or both and can have differing degrees of affinity between different node pairs. Various placement algorithms may be used to optimize placement of the applications such as an individual-greedy, pair-greedy or stable marriage algorithm. One placement objective may be to place the programs among nodes of the resource graph without exceeding the storage and CPU capacities at nodes while keeping the total cost over all applications small.
    • 公开了一种用于规划在异构存储区域网络数据中心中应用集合的放置的程序,方法和系统。 所公开的程序,方法和系统处理虚拟机应用在资源图中的耦合放置,每个应用需要来自资源图中连接的资源节点对的一定量的CPU资源和一定量的存储资源。 图中的资源节点提供存储资源,CPU资源或两者,并且可以在不同节点对之间具有不同程度的亲和度。 可以使用各种放置算法来优化诸如个人贪心,对贪婪或稳定的结婚算法的应用的放置。 一个放置目标可以是在节点之间放置节目,而不超过节点处的存储和CPU容量,同时保持所有应用程序的总成本较小。
    • 5. 发明申请
    • SCALABLE PERFORMANCE-BASED VOLUME ALLOCATION IN LARGE STORAGE CONTROLLER COLLECTIONS
    • 基于性能的大容量分配在大容量存储控制器集合
    • US20110161617A1
    • 2011-06-30
    • US13043247
    • 2011-03-08
    • Bhuvan BambaMadhukar R. Korupolu
    • Bhuvan BambaMadhukar R. Korupolu
    • G06F12/02
    • G06F9/5083G06F9/5016
    • A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system in a plurality of component layers is analyzed to yield gap values for each node (e.g., a bottom-up estimation). The gap value for each node is an estimate of the amount in GB of the new workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system (e.g., a top-down selection). The volumes may be applied to a storage workload in the order of the allocation list and the gap values and list are updated.
    • 公开了可应用于大型存储控制器集合中的可扩展的基于性能的卷分配技术。 分析表示多个组件层中的存储系统的互连组件的多个节点的全局资源树,以产生每个节点的间隙值(例如,自底向上估计)。 每个节点的间隙值是可以在该节点的子树中分配的新工作负载的GB量的估计,而不超过该子树中任何节点的性能和空间界限。 进一步分析全局资源树的间隙值以生成存储系统的卷的有序分配列表(例如,自上而下的选择)。 卷可以按照分配列表的顺序应用于存储工作负载,并且间隙值和列表被更新。
    • 6. 发明申请
    • SCALABLE PERFORMANCE-BASED VOLUME ALLOCATION IN LARGE STORAGE CONTROLLER COLLECTIONS
    • 基于性能的大容量分配在大容量存储控制器集合
    • US20080288739A1
    • 2008-11-20
    • US11750076
    • 2007-05-17
    • Bhuvan BambaMadhukar R. Korupolu
    • Bhuvan BambaMadhukar R. Korupolu
    • G06F12/00
    • G06F9/5083G06F9/5016
    • A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system is analyzed to yield gap values for each node (e.g., a bottom-up estimation). The gap value for each node is an estimate of the amount in GB of the new workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system (e.g., a top-down selection). The volumes may be applied to a storage workload in the order of the allocation list and the gap values and list are updated.
    • 公开了可应用于大型存储控制器集合中的可扩展的基于性能的卷分配技术。 分析表示存储系统的互连组件的多个节点的全局资源树,以产生每个节点的间隙值(例如,自底向上估计)。 每个节点的间隙值是可以在该节点的子树中分配的新工作负载的GB量的估计,而不超过该子树中任何节点的性能和空间界限。 进一步分析全局资源树的间隙值以生成存储系统的卷的有序分配列表(例如,自上而下的选择)。 卷可以按照分配列表的顺序应用于存储工作负载,并且间隙值和列表被更新。
    • 7. 发明申请
    • CONSTRUCTING MULTIDIMENSIONAL HISTOGRAMS FOR COMPLEX SPATIAL GEOMETRY OBJECTS
    • 构造复杂空间几何对象的多维组态
    • US20140052711A1
    • 2014-02-20
    • US13587897
    • 2012-08-16
    • Bhuvan BambaRichard J. AndersonYing HuSiva Ravada
    • Bhuvan BambaRichard J. AndersonYing HuSiva Ravada
    • G06F17/30
    • G06F17/30241
    • Techniques are described for generating histograms for a multidimensional space. In the presence of large spatial objects, fuzzy splitting techniques are utilized to recursively divide the multidimensional space into partitions, where a single spatial object may belong to multiple partitions. Large spatial objects are essentially broken down into smaller objects that may allow for more efficient partitioning of the multidimensional space. A count of spatial objects in each partition yields a spatial histogram. A spatial object that belongs to multiple partitions may have a weighted count for each of the multiple partitions, based on the extent to which the spatial object overlaps with each partition. Thus, an object that is split among a handful of partitions will only contribute a fraction of a count to each partition. Small partitions having relatively few objects are avoided by refusing to subdivide a partition whose members drop below a threshold number.
    • 描述了用于生成多维空间的直方图的技术。 在存在大空间物体的情况下,使用模糊分割技术将多维空间递归地划分成分区,其中单个空间对象可能属于多个分区。 大的空间对象基本上被分解成更小的对象,这可以允许对多维空间进行更有效的划分。 每个分区中的空间对象的计数产生空间直方图。 基于空间对象与每个分区重叠的程度,属于多个分区的空间对象可以具有针对每个多个分区的加权计数。 因此,在少数分区之间分割的对象只会为每个分区贡献一部分计数。 通过拒绝细分成员下降到阈值以下的分区来避免具有相对较少对象的小分区。
    • 9. 发明授权
    • Scalable performance-based volume allocation in large storage controller collections
    • 大型存储控制器集合中可扩展的基于性能的卷分配
    • US07917705B2
    • 2011-03-29
    • US11750076
    • 2007-05-17
    • Bhuvan BambaMadhukar R. Korupolu
    • Bhuvan BambaMadhukar R. Korupolu
    • G06F12/00
    • G06F9/5083G06F9/5016
    • A scalable, performance-based, volume allocation technique that can be applied in large storage controller collections is disclosed. A global resource tree of multiple nodes representing interconnected components of a storage system is analyzed to yield gap values for each node (e.g., a bottom-up estimation). The gap value for each node is an estimate of the amount in GB of the new workload that can be allocated in the subtree of that node without exceeding the performance and space bounds at any of the nodes in that subtree. The gap values of the global resource tree are further analyzed to generate an ordered allocation list of the volumes of the storage system (e.g., a top-down selection). The volumes may be applied to a storage workload in the order of the allocation list and the gap values and list are updated.
    • 公开了可应用于大型存储控制器集合中的可扩展的基于性能的卷分配技术。 分析表示存储系统的互连组件的多个节点的全局资源树,以产生每个节点的间隙值(例如,自底向上估计)。 每个节点的间隙值是可以在该节点的子树中分配的新工作负载的GB量的估计,而不超过该子树中任何节点的性能和空间界限。 进一步分析全局资源树的间隙值以生成存储系统的卷的有序分配列表(例如,自上而下的选择)。 卷可以按照分配列表的顺序应用于存储工作负载,并且间隙值和列表被更新。
    • 10. 发明授权
    • Constructing multidimensional histograms for complex spatial geometry objects
    • 为复杂的空间几何对象构建多维直方图
    • US08812488B2
    • 2014-08-19
    • US13587897
    • 2012-08-16
    • Bhuvan BambaRichard J. AndersonYing HuSiva Ravada
    • Bhuvan BambaRichard J. AndersonYing HuSiva Ravada
    • G06F17/30
    • G06F17/30241
    • Techniques are described for generating histograms for a multidimensional space. In the presence of large spatial objects, fuzzy splitting techniques are utilized to recursively divide the multidimensional space into partitions, where a single spatial object may belong to multiple partitions. Large spatial objects are essentially broken down into smaller objects that may allow for more efficient partitioning of the multidimensional space. A count of spatial objects in each partition yields a spatial histogram. A spatial object that belongs to multiple partitions may have a weighted count for each of the multiple partitions, based on the extent to which the spatial object overlaps with each partition. Thus, an object that is split among a handful of partitions will only contribute a fraction of a count to each partition. Small partitions having relatively few objects are avoided by refusing to subdivide a partition whose members drop below a threshold number.
    • 描述了用于生成多维空间的直方图的技术。 在存在大空间物体的情况下,使用模糊分割技术将多维空间递归地划分成分区,其中单个空间对象可能属于多个分区。 大的空间对象基本上被分解成更小的对象,这可以允许对多维空间进行更有效的划分。 每个分区中的空间对象的计数产生空间直方图。 基于空间对象与每个分区重叠的程度,属于多个分区的空间对象可以具有针对多个分区中的每一个的加权计数。 因此,在少数分区之间分割的对象只会为每个分区贡献一部分计数。 通过拒绝细分成员下降到阈值以下的分区来避免具有相对较少对象的小分区。