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1. 发明授权

US07490094B2 Importance of semantic web resources and semantic associations between two resources 失效
标题翻译：语义网页资源的重要性和两个资源之间的语义关联
公开(公告)号：US07490094B2
公开(公告)日：2009-02-10
申请号：US10840175
申请日：2004-05-06
申请人： Bhuvan Bamba , Sougata Mukherjea
发明人： Bhuvan Bamba , Sougata Mukherjea
IPC分类号： G06F17/30
CPC分类号： G06F17/30882 , G06F17/30864 , Y10S707/99933 , Y10S707/99953
摘要： 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.
摘要翻译：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

US20050251805A1 Importance of semantic web resources and semantic associations between two resources 失效
标题翻译：语义网页资源的重要性和两个资源之间的语义关联
公开(公告)号：US20050251805A1
公开(公告)日：2005-11-10
申请号：US10840175
申请日：2004-05-06
申请人： Bhuvan Bamba , Sougata Mukherjea
发明人： Bhuvan Bamba , Sougata Mukherjea
IPC分类号： G06F9/46 , G06F17/30
CPC分类号： G06F17/30882 , G06F17/30864 , Y10S707/99933 , Y10S707/99953
摘要： 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.
摘要翻译：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

US08412890B2 Scalable performance-based volume allocation in large storage controller collections 失效
标题翻译：大型存储控制器集合中可扩展的基于性能的卷分配
公开(公告)号：US08412890B2
公开(公告)日：2013-04-02
申请号：US13043247
申请日：2011-03-08
申请人： Bhuvan Bamba , Madhukar R. Korupolu
发明人： Bhuvan Bamba , Madhukar R. Korupolu
IPC分类号： G06F12/00
CPC分类号： G06F9/5083 , G06F9/5016
摘要： 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.
摘要翻译：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

US20080295094A1 INTEGRATED PLACEMENT PLANNING FOR HETEROGENOUS STORAGE AREA NETWORK DATA CENTERS 有权
标题翻译：用于异构存储区域网络数据中心的集成放置规划
公开(公告)号：US20080295094A1
公开(公告)日：2008-11-27
申请号：US11752292
申请日：2007-05-22
申请人： Madhukar R. Korupolu , Aameek Singh , Bhuvan Bamba
发明人： Madhukar R. Korupolu , Aameek Singh , Bhuvan Bamba
IPC分类号： G06F9/455
CPC分类号： G06F9/5066 , G06F9/5077 , H04L67/1002 , H04L67/1008 , H04L67/101 , H04L67/1021 , H04L67/1023 , H04L67/1097
摘要： 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.
摘要翻译：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

US20110161617A1 SCALABLE PERFORMANCE-BASED VOLUME ALLOCATION IN LARGE STORAGE CONTROLLER COLLECTIONS 失效
标题翻译：基于性能的大容量分配在大容量存储控制器集合
公开(公告)号：US20110161617A1
公开(公告)日：2011-06-30
申请号：US13043247
申请日：2011-03-08
申请人： Bhuvan Bamba , Madhukar R. Korupolu
发明人： Bhuvan Bamba , Madhukar R. Korupolu
IPC分类号： G06F12/02
CPC分类号： G06F9/5083 , G06F9/5016
摘要： 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.
摘要翻译：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

US20080288739A1 SCALABLE PERFORMANCE-BASED VOLUME ALLOCATION IN LARGE STORAGE CONTROLLER COLLECTIONS 失效
标题翻译：基于性能的大容量分配在大容量存储控制器集合
公开(公告)号：US20080288739A1
公开(公告)日：2008-11-20
申请号：US11750076
申请日：2007-05-17
申请人： Bhuvan Bamba , Madhukar R. Korupolu
发明人： Bhuvan Bamba , Madhukar R. Korupolu
IPC分类号： G06F12/00
CPC分类号： G06F9/5083 , G06F9/5016
摘要： 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.
摘要翻译：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

US20140052711A1 CONSTRUCTING MULTIDIMENSIONAL HISTOGRAMS FOR COMPLEX SPATIAL GEOMETRY OBJECTS 有权
标题翻译：构造复杂空间几何对象的多维组态
公开(公告)号：US20140052711A1
公开(公告)日：2014-02-20
申请号：US13587897
申请日：2012-08-16
申请人： Bhuvan Bamba , Richard J. Anderson , Ying Hu , Siva Ravada
发明人： Bhuvan Bamba , Richard J. Anderson , Ying Hu , Siva Ravada
IPC分类号： G06F17/30
CPC分类号： G06F17/30241
摘要： 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.
摘要翻译：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。

8. 发明授权

US08295856B2 Selective processing of location-sensitive data streams 有权
公开(公告)号：US08295856B2
公开(公告)日：2012-10-23
申请号：US12575371
申请日：2009-10-07
申请人： Bhuvan Bamba , Bugra Gedik , Kun-Lung Wu
发明人： Bhuvan Bamba , Bugra Gedik , Kun-Lung Wu
IPC分类号： H04W24/00
摘要： A method for processing a first data stream specifying locations of a user at different times and a second data stream specifying values of a monitored attribute at a location of interest at different times includes: receiving a location-centric trigger specifying at least one spatial predicate condition relative to the location of interest and at least one non-spatial predicate condition relevant to the location of interest, calculating a safe region that includes locations whose probability of satisfying the spatial predicate condition falls below a first threshold, calculating a safe value container that includes values whose probability of satisfying the non-spatial predicate condition falls below a second threshold, and processing the first data stream and the second data stream against the location-centric trigger, by considering only those locations that are not contained within the safe region and only those values that are not contained within the safe value container.

9. 发明授权

US07917705B2 Scalable performance-based volume allocation in large storage controller collections 失效
标题翻译：大型存储控制器集合中可扩展的基于性能的卷分配
公开(公告)号：US07917705B2
公开(公告)日：2011-03-29
申请号：US11750076
申请日：2007-05-17
申请人： Bhuvan Bamba , Madhukar R. Korupolu
发明人： Bhuvan Bamba , Madhukar R. Korupolu
IPC分类号： G06F12/00
CPC分类号： G06F9/5083 , G06F9/5016
摘要： 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.
摘要翻译：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

US08812488B2 Constructing multidimensional histograms for complex spatial geometry objects 有权
标题翻译：为复杂的空间几何对象构建多维直方图
公开(公告)号：US08812488B2
公开(公告)日：2014-08-19
申请号：US13587897
申请日：2012-08-16
申请人： Bhuvan Bamba , Richard J. Anderson , Ying Hu , Siva Ravada
发明人： Bhuvan Bamba , Richard J. Anderson , Ying Hu , Siva Ravada
IPC分类号： G06F17/30
CPC分类号： G06F17/30241
摘要： 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.
摘要翻译：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。

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