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    Multi objective optimization of applications
    24.
    发明授权
    Multi objective optimization of applications 有权

    公开(公告)号：US11237806B2

    公开(公告)日：2022-02-01

    申请号：US16863001

    申请日：2020-04-30

    Applicant: International Business Machines Corporation

    Inventor： Srinivasan Parthasarathy ,  Fabio A. Oliveira ,  Sushma Ravichandran

    IPC: G06F8/41 ,  G06F11/34 ,  G06N5/04 ,  G06N20/00 ,  G06F11/30

    Abstract: A system, computer program product, and method are provided for orchestrating a multi objective optimization of an application. A set of two or more key performance indicators (KPIs) and one or more parameters associated with the application are received. A machine learning (ML) based surrogate function learning model in combination with an acquisition function is leveraged to conduct one or more adaptive trials. Each trial consists of a specific configuration of the one or more parameters. A pareto surface of the KPIs of the application is computed based on the observations of KPI values from each adaptive trial. The pareto surface is explored and an optimal operating point is selected for the application. The application is then executed at the selected operating point.

    Schema discovery through statistical transduction
    28.
    发明授权
    Schema discovery through statistical transduction 有权

    公开(公告)号：US10331633B2

    公开(公告)日：2019-06-25

    申请号：US14730287

    申请日：2015-06-04

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Srinivasan Parthasarathy ,  Venkata N. Pavuluri ,  Deepak S. Turaga

    IPC: G06F16/21 ,  G06F17/27

    Abstract: A method, system, and computer program product derive data schema for application to a data set. One or more processors generate a directed acyclic weighted graph that encodes data types and semantic types used by a data set. One or more processors assign estimated frequencies for each component of the directed acyclic weighted graph, where the estimated frequencies predict a likelihood of a particular data schema element being used by any data set. One or more processors traverse through paths in the directed acyclic weighted graph with a predetermined portion of the data set to determine a data schema that correctly defines data from the data set and identifies any errors in the data set, and then apply the data schema to the data set to generate clean data that is properly formatted.

    Schema Discovery Through Statistical Transduction
    29.
    发明申请
    Schema Discovery Through Statistical Transduction 审中-公开
    通过统计转化的模式发现

    公开(公告)号：US20160357747A1

    公开(公告)日：2016-12-08

    申请号：US14730287

    申请日：2015-06-04

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Srinivasan Parthasarathy ,  Venkata N. Pavuluri ,  Deepak S. Turaga

    IPC: G06F17/30 ,  G06F17/27

    CPC classification number: G06F16/211 ,  G06F17/2705 ,  G06F17/2785

    Abstract: A method, system, and computer program product derive data schema for application to a data set. One or more processors generate a directed acyclic weighted graph that encodes data types and semantic types used by a data set. One or more processors assign estimated frequencies for each component of the directed acyclic weighted graph, where the estimated frequencies predict a likelihood of a particular data schema element being used by any data set. One or more processors traverse through paths in the directed acyclic weighted graph with a predetermined portion of the data set to determine a data schema that correctly defines data from the data set and identifies any errors in the data set, and then apply the data schema to the data set to generate clean data that is properly formatted.

    Abstract translation: 方法，系统和计算机程序产品导出用于应用于数据集的数据模式。 一个或多个处理器生成定向非循环加权图，其对数据集使用的数据类型和语义类型进行编码。 一个或多个处理器为有向非循环加权图的每个分量分配估计的频率，其中估计的频率预测特定数据模式元素被任何数据集使用的可能性。 一个或多个处理器通过具有数据集的预定部分的有向非循环加权图中的路径遍历，以确定从数据集中正确定义数据的数据模式，并识别数据集中的任何错误，然后将数据模式应用于 数据集将生成正确格式的干净数据。

    Optimal test flow scheduling within automated test equipment for minimized mean time to detect failure
    30.
    发明授权
    Optimal test flow scheduling within automated test equipment for minimized mean time to detect failure 有权
    自动测试设备内的最佳测试流程调度，以最小化平均时间来检测故障

    公开(公告)号：US09342424B2

    公开(公告)日：2016-05-17

    申请号：US13972566

    申请日：2013-08-21

    Applicant: International Business Machines Corporation

    Inventor： Wei Fan ,  Nagui Halim ,  Mark C. Johnson ,  Srinivasan Parthasarathy ,  Deepak S. Turaga ,  Olivier Verscheure

    IPC: G06F11/00 ,  G06F11/27 ,  G01R31/28

    CPC classification number: G06F11/27 ,  G01R31/2894

    Abstract: The present invention describes a method and system for optimizing a test flow within each ATE (Automated Test Equipment) station. The test flow includes a plurality of test blocks. A test block includes a plurality of individual tests. A computing system schedule the test flow based one or more of: a test failure model, test block duration and a yield model. The failure model determines an order or sequence of the test blocks. There are at least two failure models: independent failure model and dependant failure model. The yield model describes whether a semiconductor chip is defective or not. Upon completing the scheduling, the ATE station conducts tests according to the scheduled test flow. The present invention can also be applied to software testing.

    Abstract translation: 本发明描述了用于优化每个ATE（自动测试设备）站内的测试流程的方法和系统。 测试流程包括多个测试块。 测试块包括多个单独测试。 计算系统基于以下一个或多个来计划测试流程：测试失败模型，测试块持续时间和产量模型。 故障模型确定测试块的顺序或顺序。 至少有两种故障模型：独立故障模型和依赖故障模型。 产量模型描述了半导体芯片是否有缺陷。 完成调度后，ATE站根据预定的测试流程进行测试。 本发明也可以应用于软件测试。

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