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    DATABASE TABLE FORMAT CONVERSION BASED ON USER DATA ACCESS PATTERNS IN A NETWORKED COMPUTING ENVIRONMENT
    11.
    发明申请
    DATABASE TABLE FORMAT CONVERSION BASED ON USER DATA ACCESS PATTERNS IN A NETWORKED COMPUTING ENVIRONMENT 审中-公开

    公开(公告)号：US20180095961A1

    公开(公告)日：2018-04-05

    申请号：US15809004

    申请日：2017-11-10

    Applicant: International Business Machines Corporation

    Inventor： Lisa Seacat DeLuca ,  Yu Deng ,  Jenny S. Li ,  Liangzhao Zeng

    IPC: G06F17/30

    Abstract: An approach for conversion between database formats (e.g., from a relational database format to a hash table or a “big table” database format) based on user data access patterns in a networked computing environment is provided. A first set of database tables having a first format is identified based on a set of access patterns stored in a computer storage device. A second set of database tables having a second database format corresponding to the first set of database tables may then be provided (e.g., accessed, augmented, and/or generated). A mapping between the first set of database tables and the second set of database tables may then be created. A column set may then be generated based on at least one condition of the set of queries. The column set may then be used as a key for the second set of database tables.

    IMAGE VULNERABILITY REPAIR IN A NETWORKED COMPUTING ENVIRONMENT
    14.
    发明申请
    IMAGE VULNERABILITY REPAIR IN A NETWORKED COMPUTING ENVIRONMENT 有权
    网络计算环境中的图像易损性维护

    公开(公告)号：US20170006056A1

    公开(公告)日：2017-01-05

    申请号：US15268966

    申请日：2016-09-19

    Applicant: International Business Machines Corporation

    Inventor： Al Chakra ,  Christopher J. Dawson ,  Yu Deng ,  Rick A. Hamilton, II ,  Jenny S. Li ,  Liangzhao Zeng

    IPC: H04L29/06 ,  G06F21/57 ,  G06F21/55

    CPC classification number: H04L63/1433 ,  G06F21/55 ,  G06F21/577 ,  H04L63/02

    Abstract: Embodiments of the present invention provide an approach to repair vulnerabilities (e.g., security vulnerabilities) in images (e.g., application images) in a networked computing environment (e.g., a cloud computing environment). Specifically, an image is checked for vulnerabilities using a database of known images and/or vulnerabilities. If a vulnerability is found, a flexible/elastic firewall is established around the image so as to isolate the vulnerability. Once the firewall has been put in place, the vulnerability can be repaired by a variety of means such as upgrading the image, quarantining the image, discarding the image, and/or generating a new image. Once the image has been repaired, the firewall can be removed.

    Abstract translation: 本发明的实施例提供了在网络计算环境（例如，云计算环境）中修复图像（例如，应用图像）中的漏洞（例如，安全漏洞）的方法。 具体来说，使用已知图像和/或漏洞的数据库检查图像的漏洞。 如果发现一个漏洞，则会围绕图像建立一个灵活/有弹性的防火墙，从而隔离漏洞。 一旦防火墙已经到位，该漏洞可以通过各种手段进行修复，例如升级图像，隔离图像，丢弃图像和/或生成新图像。 一旦图像被修复，防火墙可以被删除。

    Search engine analytics and optimization for media content in social networks
    15.
    发明授权
    Search engine analytics and optimization for media content in social networks 有权
    针对社交网络中媒体内容的搜索引擎分析和优化

    公开(公告)号：US09529893B1

    公开(公告)日：2016-12-27

    申请号：US15063676

    申请日：2016-03-08

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Yu Deng ,  Jenny S. Li ,  Liangzhao Zeng

    IPC: G06F17/30

    CPC classification number: G06F17/30867 ,  G06F17/30442 ,  G06F17/3053 ,  G06F17/30873 ,  G06Q50/01

    Abstract: Embodiments include method, systems and computer program products for searching a social network for media content. Aspects include receiving a query for media content related to a topic from a user, identifying a plurality of content items associated with the topic and determining a quality score for each of the plurality of content items. Aspects also include providing a subset of the plurality of content items to the user, wherein the subset is selected based on the quality score and updating the quality score of one or more content items in the subset based on a behavior of the user in viewing the subset of the plurality of content items.

    Abstract translation: 实施例包括用于搜索社交网络以进行媒体内容的方法，系统和计算机程序产品。 方面包括从用户接收与主题相关的媒体内容的查询，识别与该主题相关联的多个内容项目并且确定多个内容项目中的每一个的质量分数。 方面还包括向用户提供多个内容项目的子集，其中，基于质量分数选择子集，并且基于用户在查看该节目中的行为来更新该子集中的一个或多个内容项目的质量分数 多个内容项目的子集。

    SECURITY POLICY GENERATION BASED ON SNAPSHOTS OF SIMILAR VIRTUAL MACHINES
    16.
    发明申请
    SECURITY POLICY GENERATION BASED ON SNAPSHOTS OF SIMILAR VIRTUAL MACHINES 有权
    基于类似虚拟机的攻击的安全策略生成

    公开(公告)号：US20160321455A1

    公开(公告)日：2016-11-03

    申请号：US14699251

    申请日：2015-04-29

    Applicant: International Business Machines Corporation

    Inventor： Yu Deng ,  Ruchi Mahindru ,  HariGovind V. Ramasamy ,  Lakshminarayanan Renganarayana ,  Soumitra Sarkar ,  Long Wang

    IPC: G06F21/57 ,  G06F9/455

    CPC classification number: G06F21/577 ,  G06F3/0619 ,  G06F3/065 ,  G06F3/0685 ,  G06F9/45558 ,  G06F2009/45562 ,  G06F2009/45575 ,  G06F2009/45587 ,  G06F2221/034

    Abstract: Determining which snapshot deltas tend to occur in: (i) healthy virtual machines (VMs) that have been subject to an attack yet remained healthy, and/or (ii) unhealthy VMs that have apparently been adversely affected by an attack. Snapshot deltas that occur in at least some (or more preferably all) of the healthy VM subset provide information about software changes (for example, updates, configuration changes) that may be helpful. Snapshot deltas that occur in at least some (or more preferably all) of the unhealthy VM subsets provide information about software changes (for example, updates, configuration changes) that may be unhelpful.

    Abstract translation: 确定哪些快照增量趋于发生在：（i）已经受到攻击的健康虚拟机（VM）保持健康，和/或（ii）显然受到攻击不利影响的不健康的虚拟机。 发生在健康VM子集的至少一些（或更优选全部））的快照增量提供关于可能有帮助的软件改变（例如，更新，配置改变）的信息。 发生在至少一些（或更优选全部）不健康的VM子集中的快照三角形提供关于可能无益的软件改变（例如，更新，配置改变）的信息。

    INTRODUCING USER TRUSTWORTHINESS IN IMPLICIT FEEDBACK BASED SEARCH RESULT RANKING
    17.
    发明申请
    INTRODUCING USER TRUSTWORTHINESS IN IMPLICIT FEEDBACK BASED SEARCH RESULT RANKING 审中-公开
    基于隐私反馈的搜索结果排名引入用户信任

    公开(公告)号：US20150332169A1

    公开(公告)日：2015-11-19

    申请号：US14278754

    申请日：2014-05-15

    Applicant: International Business Machines Corporation

    Inventor： John A. Bivens ,  Yu Deng ,  Peter X. O'Bryan ,  Jonathan J. Puryear ,  Harigovind V. Ramasamy ,  Soumitra Sarkar ,  Zaman Valli-Hasham ,  Kevin D. Wahlmeier ,  Yinan Zhang

    IPC: G06N99/00

    CPC classification number: G06N20/00

    Abstract: User trustworthiness may be introduced in implicit feedback based supervised machine learning systems. A set of training data examples may be scored based on the trustworthiness of users associated respectively with the training data examples. The training data examples may be sampled into a plurality of training data sets based on a weighted bootstrap sampling technique, where each weight is a probability proportional to trustworthiness score associated with an example. A machine learning algorithm takes the plurality of the training data sets as input and generates a plurality of trained models. Outputs from the plurality of trained models may be ensembled by computing a weighted average of the outputs of the plurality of trained models.

    Abstract translation: 用户可信度可以在基于隐式反馈的监督机器学习系统中引入。 可以基于分别与训练数据示例相关联的用户的可信度来对一组训练数据示例进行评分。 训练数据示例可以基于加权引导抽样技术被采样到多个训练数据集中，其中每个权重是与与示例相关联的可信度得分成比例的概率。 机器学习算法将多个训练数据集作为输入，并产生多个训练模型。 可以通过计算多个训练模型的输出的加权平均来合并来自多个训练模型的输出。

    IMAGE VULNERABILITY REPAIR IN A NETWORKED COMPUTING ENVIRONMENT
    19.
    发明申请
    IMAGE VULNERABILITY REPAIR IN A NETWORKED COMPUTING ENVIRONMENT 有权
    网络计算环境中的图像易损性维护

    公开(公告)号：US20140137258A1

    公开(公告)日：2014-05-15

    申请号：US14156665

    申请日：2014-01-16

    Applicant: International Business Machines Corporation

    Inventor： Al Chakra ,  Christopher J. Dawson ,  Yu Deng ,  Rick A. Hamilton, II ,  Jenny S. Li ,  Liangzhao Zeng

    IPC: H04L29/06

    CPC classification number: H04L63/1433 ,  G06F21/55 ,  G06F21/577 ,  H04L63/02

    Abstract: Embodiments of the present invention provide an approach to repair vulnerabilities (e.g., security vulnerabilities) in images (e.g., application images) in a networked computing environment (e.g., a cloud computing environment). Specifically, an image is checked for vulnerabilities using a database of known images and/or vulnerabilities. If a vulnerability is found, a flexible/elastic firewall is established around the image so as to isolate the vulnerability. Once the firewall has been put in place, the vulnerability can be repaired by a variety of means such as upgrading the image, quarantining the image, discarding the image, and/or generating a new image. Once the image has been repaired, the firewall can be removed.

    Abstract translation: 本发明的实施例提供了在网络计算环境（例如，云计算环境）中修复图像（例如，应用图像）中的漏洞（例如，安全漏洞）的方法。 具体来说，使用已知图像和/或漏洞的数据库检查图像的漏洞。 如果发现一个漏洞，则会围绕图像建立一个灵活/有弹性的防火墙，从而隔离漏洞。 一旦防火墙已经到位，该漏洞可以通过各种手段进行修复，例如升级图像，隔离图像，丢弃图像和/或生成新图像。 一旦图像被修复，防火墙可以被删除。

    DATA SELECTION FOR MACHINE LEARNING MODELS BASED ON DATA PROFILING
    20.
    发明公开
    DATA SELECTION FOR MACHINE LEARNING MODELS BASED ON DATA PROFILING 审中-公开

    公开(公告)号：US20230385706A1

    公开(公告)日：2023-11-30

    申请号：US17804218

    申请日：2022-05-26

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Paulina Toro Isaza ,  Yu Deng ,  Michael Elton Nidd ,  Harshit Kumar ,  Larisa Shwartz

    IPC: G06N20/20 ,  G06K9/62

    CPC classification number: G06N20/20 ,  G06K9/6228 ,  G06K9/6262

    Abstract: A method, computer system, and a computer program for data selection is provided. The present invention may include generating a first model associated with a dataset. The present invention may further include determining a first model performance level associated with the first model based on a plurality of dataset metric values of the dataset. The present invention may further include a plurality of data subsets of a dataset based on the first model performance level failing to exceed a performance threshold and calculating a plurality of subset metric values associated with the plurality of data subsets. The present invention may further include generating a second model associated with at least one data subset based on the plurality of subset metric values and determining an optimization associated with the first model based on a second model performance level associated with the second model exceeding the performance threshold.

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