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1. 发明授权

US08621424B2 Compiler based code modification for use in document ranking 有权
标题翻译：基于编译器的代码修改用于文档排名
公开(公告)号：US08621424B2
公开(公告)日：2013-12-31
申请号：US12165457
申请日：2008-06-30
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F9/44 , G06F9/45 , G06F13/00
CPC分类号： G06F8/41 , G06F9/3004 , G06F9/30072 , G06F9/30087 , G06F17/30864
摘要： The subject matter disclosed herein relates to alter an expression of executable instructions via a compiler component for use in ranking of electronic documents.
摘要翻译：本文公开的主题涉及通过用于电子文档排名的编译器组件来改变可执行指令的表达。

2. 发明申请

US20100023474A1 Decision Tree Coalescing for Document Ranking 有权
标题翻译：决策树合并文件排名
公开(公告)号：US20100023474A1
公开(公告)日：2010-01-28
申请号：US12178483
申请日：2008-07-23
申请人： Arun Kejariwal , Sapan Panigrahi , Girish Vaitheeswaran
发明人： Arun Kejariwal , Sapan Panigrahi , Girish Vaitheeswaran
IPC分类号： G06N5/02
CPC分类号： G06F17/30864
摘要： Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.
摘要翻译：机器学习排名算法，例如为了排序搜索结果，经常使用涉及基于特征阈值的决策节点的决策树序列。优化这种算法的模块，系统和方法涉及分析阈值特征值以根据根决策节点中使用的特征来确定每个特征的阈值间隔和分组决策树。然后将每个组中的决策树合并，以形成每个组的合并组树，并最终合并聚合组树以形成实现该算法的合并树。

3. 发明授权

US08458170B2 Prefetching data for document ranking 有权
标题翻译：预取文档排名数据
公开(公告)号：US08458170B2
公开(公告)日：2013-06-04
申请号：US12165307
申请日：2008-06-30
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F17/30
CPC分类号： G06F17/30864
摘要： The subject matter disclosed herein relates to prefetching data for use in ranking of electronic documents via a document ranking component.
摘要翻译：本文公开的主题涉及经由文档排序组件预取用于电子文档排名的数据。

4. 发明申请

US20100070457A1 Efficient Data Layout Techniques for Fast Machine Learning-Based Document Ranking 有权
标题翻译：基于快速机器学习的文档排序的高效数据布局技术
公开(公告)号：US20100070457A1
公开(公告)日：2010-03-18
申请号：US12211636
申请日：2008-09-16
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06N5/02
CPC分类号： G06N99/005
摘要： A computer readable medium stores a program for optimization for a search, and has sets of instructions for receiving a first decision tree. The first decision tree includes several nodes, and each node is for comparing a feature value to a threshold value. The instructions are for weighting the nodes within the first decision tree, determining the weighted frequency of a first feature within the first decision tree, and determining the weighted frequency of a second feature within the first decision tree. The instructions order the features based on the determined weighted frequencies, and store the ordering such that values of features having higher weighted frequencies are retrieved more often than values of features having lower weighted frequencies within the first decision tree.
摘要翻译：计算机可读介质存储用于搜索的优化的程序，并且具有用于接收第一决策树的指令集。第一决策树包括几个节点，每个节点用于将特征值与阈值进行比较。所述指令用于对第一决策树内的节点进行加权，确定第一决策树内的第一特征的加权频率，以及确定第一决策树内的第二特征的加权频率。指令基于确定的加权频率对特征进行排序，并存储排序，使得具有较高加权频率的特征值比第一决策树中具有较低加权频率的特征的值更频繁地检索。

5. 发明授权

US08533129B2 Efficient data layout techniques for fast machine learning-based document ranking 有权
标题翻译：高效的数据布局技术，用于快速的基于机器学习的文档排序
公开(公告)号：US08533129B2
公开(公告)日：2013-09-10
申请号：US12211636
申请日：2008-09-16
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： A computer readable medium stores a program for optimization for a search, and has sets of instructions for receiving a first decision tree. The first decision tree includes several nodes, and each node is for comparing a feature value to a threshold value. The instructions are for weighting the nodes within the first decision tree, determining the weighted frequency of a first feature within the first decision tree, and determining the weighted frequency of a second feature within the first decision tree. The instructions order the features based on the determined weighted frequencies, and store the ordering such that values of features having higher weighted frequencies are retrieved more often than values of features having lower weighted frequencies within the first decision tree.
摘要翻译：计算机可读介质存储用于搜索的优化的程序，并且具有用于接收第一决策树的指令集。第一决策树包括几个节点，每个节点用于将特征值与阈值进行比较。所述指令用于对第一决策树内的节点进行加权，确定第一决策树内的第一特征的加权频率，以及确定第一决策树内的第二特征的加权频率。指令基于确定的加权频率对特征进行排序，并存储排序，使得具有较高加权频率的特征值比第一决策树中具有较低加权频率的特征的值更频繁地检索。

6. 发明授权

US08065242B2 Decision tree coalescing for document ranking 有权
标题翻译：决策树合并文档排名
公开(公告)号：US08065242B2
公开(公告)日：2011-11-22
申请号：US12178483
申请日：2008-07-23
申请人： Arun Kejariwal , Sapan Panigrahi , Girish Vaitheeswaran
发明人： Arun Kejariwal , Sapan Panigrahi , Girish Vaitheeswaran
IPC分类号： G06F15/18
CPC分类号： G06F17/30864
摘要： Machine-learned ranking algorithms, e.g. for ranking search results, often use a sequence of decision trees involving decision nodes based on threshold values of features. Modules, systems and methods of optimizing such algorithms involve analyzing threshold feature values to determine threshold intervals for each feature and grouping decision trees according to the feature used in a root decision node. Then coalescing the decision trees within each group to form a coalesced group tree for each group and finally coalescing the coalesced group trees to form a coalesced tree that implements the algorithm.
摘要翻译：机器学习排名算法，例如为了排序搜索结果，经常使用涉及基于特征阈值的决策节点的决策树序列。优化这种算法的模块，系统和方法涉及分析阈值特征值以根据根决策节点中使用的特征来确定每个特征的阈值间隔和分组决策树。然后将每个组中的决策树合并，以形成每个组的合并组树，并最终合并聚合组树以形成实现该算法的合并树。

7. 发明申请

US20090328014A1 COMPILER BASED CODE MODIFICATION FOR USE IN DOCUMENT RANKING 有权
标题翻译：基于编译器的代码修改用于文档排序
公开(公告)号：US20090328014A1
公开(公告)日：2009-12-31
申请号：US12165457
申请日：2008-06-30
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F9/45
CPC分类号： G06F8/41 , G06F9/3004 , G06F9/30072 , G06F9/30087 , G06F17/30864
摘要： The subject matter disclosed herein relates to alter an expression of executable instructions via a compiler component for use in ranking of electronic documents.
摘要翻译：本文公开的主题涉及通过用于电子文档排名的编译器组件来改变可执行指令的表达。

8. 发明申请

US20090327274A1 PREFETCHING DATA FOR DOCUMENT RANKING 有权
标题翻译：用于文件排名的前提数据
公开(公告)号：US20090327274A1
公开(公告)日：2009-12-31
申请号：US12165307
申请日：2008-06-30
申请人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
发明人： Arun Kejariwal , Girish Vaitheeswaran , Sapan Panigrahi
IPC分类号： G06F7/06 , G06F17/30
CPC分类号： G06F17/30864
摘要： The subject matter disclosed herein relates to prefetching data for use in ranking of electronic documents via a document ranking component.
摘要翻译：本文公开的主题涉及经由文档排序组件预取用于电子文档排名的数据。

9. 发明授权

US08818787B2 Method and system for predicting performance of software applications on prospective hardware architecture 有权
标题翻译：用于预测软件应用程序在预期硬件架构上的性能的方法和系统
公开(公告)号：US08818787B2
公开(公告)日：2014-08-26
申请号：US13018243
申请日：2011-01-31
申请人： Arun Kejariwal , Paolo D'Alberto , Sapan Panigrahi , Rosario Cammarota
发明人： Arun Kejariwal , Paolo D'Alberto , Sapan Panigrahi , Rosario Cammarota
IPC分类号： G06F9/45 , G06F9/44
CPC分类号： G06F11/3447 , G06F11/3409 , G06F11/3419 , G06F11/3442 , G06F11/3466 , G06F2201/81 , G06F2201/83 , G06F2201/86 , G06F2201/865 , G06F2201/88
摘要： A system and method for identifying optimal system architectures for a reference application are provided. The system and method comprise executing a reference application and a plurality of test applications on a current system architecture and sampling performance data for each of the applications. The performance data is used to compute an application signature for each application. A similarity element is derived from the application signatures that illustrates the similarity between each application and every other application. Using a similarity threshold and an algorithm, a subset of test applications that are similar to the reference application are derived.
摘要翻译：提供了一种用于识别参考应用的最佳系统架构的系统和方法。该系统和方法包括在当前系统架构上执行参考应用和多个测试应用，并针对每个应用采样性能数据。性能数据用于计算每个应用程序的应用程序签名。相似性元素从应用程序签名中得出，该签名说明了每个应用程序和每个其他应用程序之间的相似性。使用相似性阈值和算法，推导出与参考应用程序相似的测试应用程序子集。

10. 发明申请

US20090199047A1 EXECUTING SOFTWARE PERFORMANCE TEST JOBS IN A CLUSTERED SYSTEM 审中-公开
标题翻译：执行集群系统中的软件性能测试工作
公开(公告)号：US20090199047A1
公开(公告)日：2009-08-06
申请号：US12023608
申请日：2008-01-31
申请人： Girish Vaitheeswaran , Sapan Panigrahi , Daniel Bretoi , Stephen Nelson , George Wu
发明人： Girish Vaitheeswaran , Sapan Panigrahi , Daniel Bretoi , Stephen Nelson , George Wu
IPC分类号： G06F11/30
CPC分类号： G06F11/3616 , G06F11/3409 , G06F11/3452 , G06F11/3466 , G06F11/3476 , G06F11/3672 , G06F2201/865 , G06F2201/875
摘要： Using a testing framework, developers may create a test module to centralize resources and results for a software test plan amongst a plurality of systems. With assistance from the testing framework, the test module may facilitate the creation of test cases, the execution of a test job for each test case, the collection of performance statistics during each test job, and the aggregation of collected statistics into organized reports for easier analysis. The test module may track test results for easy comparison of performance metrics in response to various conditions and environments over the history of the development process. The testing framework may also schedule a test job for execution when the various systems and resources required by the test job are free. The testing framework may be operating system independent, so that a single test job may test software concurrently on a variety of systems.
摘要翻译：使用测试框架，开发人员可以创建一个测试模块，以集中多个系统中的软件测试计划的资源和结果。在测试框架的帮助下，测试模块可能有助于创建测试用例，为每个测试用例执行测试作业，在每个测试作业期间收集性能统计信息，以及将收集的统计信息聚合到有组织的报告中以更容易分析。测试模块可以跟踪测试结果，以便在开发过程的历史上响应于各种条件和环境的性能指标进行比较。当测试作业所需的各种系统和资源是免费的时，测试框架还可以安排测试作业执行。测试框架可能与操作系统无关，因此单个测试作业可以在各种系统上同时测试软件。

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