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    Pluggable web-based visualizations for applications
    1.
    发明授权
    Pluggable web-based visualizations for applications 有权
    可插入的基于Web的可视化应用程序

    公开(公告)号：US08890890B2

    公开(公告)日：2014-11-18

    申请号：US12788222

    申请日：2010-05-26

    申请人： Danyel Fisher ,  Roland Fernandez ,  Steven Mark Drucker

    发明人： Danyel Fisher ,  Roland Fernandez ,  Steven Mark Drucker

    IPC分类号： G06T11/20 ,  G06T11/60 ,  G06F17/30

    CPC分类号： G06F17/30905 ,  G06T11/206 ,  G06T11/60

    摘要： The pluggable web-based visualization technique described herein pertains to a method for providing pluggable web-based visualizations for applications. The method selects visualizations from the web to be embedded into a host application on a desktop computing device. The visualizations can be plugged in to a variety of host applications. This empowers end-users, application designers, and visualization designers by allowing greater reuse of existing code. Additionally, end-users do not have to wait for new revisions of existing applications to use the latest techniques. Designers of domain specific visualizations can work on just the visualization and have them incorporated into a variety of different host applications. Users can perform local processing and visualizations on their own machine, yet obtain new visualizations from the web where they can be updated more frequently and where special purpose visualizations are available.

    摘要翻译： 本文描述的可插入的基于网络的可视化技术涉及用于为应用提供可插入的基于web的可视化的方法。 该方法选择来自web的可视化来嵌入在桌面计算设备上的主机应用中。 可视化可以插入到各种主机应用程序中。 这允许最终用户，应用程序设计人员和可视化设计人员通过允许更多地重用现有代码。 此外，最终用户不必等待现有应用程序的新修订来使用最新技术。 域特定可视化的设计者可以仅在可视化中工作，并将它们并入到各种不同的主机应用程序中。 用户可以在自己的机器上执行本地处理和可视化，还可以从网络获取新的可视化信息，从而可以更频繁地进行更新，并提供特殊目的可视化。

    Pluggable Web-Based Visualizations for Applications
    4.
    发明申请
    Pluggable Web-Based Visualizations for Applications 有权
    可插入的基于Web的可视化应用程序

    公开(公告)号：US20110292072A1

    公开(公告)日：2011-12-01

    申请号：US12788222

    申请日：2010-05-26

    申请人： Danyel Fisher ,  Roland Fernandez ,  Steven Mark Drucker

    发明人： Danyel Fisher ,  Roland Fernandez ,  Steven Mark Drucker

    IPC分类号： G09G5/00

    CPC分类号： G06F17/30905 ,  G06T11/206 ,  G06T11/60

    摘要： The pluggable web-based visualization technique described herein pertains to a method for providing pluggable web-based visualizations for applications. The method selects visualizations from the web to be embedded into a host application on a desktop computing device. The visualizations can be plugged in to a variety of host applications. This empowers end-users, application designers, and visualization designers by allowing greater reuse of existing code. Additionally, end-users do not have to wait for new revisions of existing applications to use the latest techniques. Designers of domain specific visualizations can work on just the visualization and have them incorporated into a variety of different host applications. Users can perform local processing and visualizations on their own machine, yet obtain new visualizations from the web where they can be updated more frequently and where special purpose visualizations are available.

    摘要翻译： 本文描述的可插入的基于网络的可视化技术涉及用于为应用提供可插入的基于web的可视化的方法。 该方法选择来自web的可视化来嵌入在桌面计算设备上的主机应用中。 可视化可以插入到各种主机应用程序中。 这允许最终用户，应用程序设计人员和可视化设计人员通过允许更多地重用现有代码。 此外，最终用户不必等待现有应用程序的新修订来使用最新技术。 域特定可视化的设计者可以仅在可视化中工作，并将它们并入到各种不同的主机应用程序中。 用户可以在自己的机器上执行本地处理和可视化，还可以从网络获取新的可视化信息，从而可以更频繁地进行更新，并提供特殊目的可视化。

    Exploring data using multiple machine-learning models
    5.
    发明授权
    Exploring data using multiple machine-learning models 有权
    使用多机器学习模型探索数据

    公开(公告)号：US08595153B2

    公开(公告)日：2013-11-26

    申请号：US12797395

    申请日：2010-06-09

    申请人： Steven Mark Drucker ,  Kayur Dushyant Patel ,  Desney S. Tan ,  Ashish Kapoor ,  James Anthony Fogarty

    发明人： Steven Mark Drucker ,  Kayur Dushyant Patel ,  Desney S. Tan ,  Ashish Kapoor ,  James Anthony Fogarty

    IPC分类号： G06N5/00

    CPC分类号： G06N99/005

    摘要： A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data.

    摘要翻译： 一种用于同时运行多个机器学习模型的多模型数据挖掘系统和方法，用于理解和探索数据。 该系统和方法的实施例允许用户更好地理解数据并获得对其数据的新见解。 系统和方法的实施例还允许用户交互地探索问题并导航数据的不同视图。 同时运行许多不同的分类器训练和评估实验，得到结果。 结果在每个实验中进行聚合和可视化，以确定和理解每个示例如何分类给每个不同的分类器。 然后以各种方式总结这些结果，以允许用户在各个示例本身和与数据相关的特征方面获得对数据的更多了解。

    TEXT MINING OF MICROBLOGS USING LATENT TOPIC LABELS
    6.
    发明申请
    TEXT MINING OF MICROBLOGS USING LATENT TOPIC LABELS 审中-公开
    使用专利主题标签的文本采集文本

    公开(公告)号：US20120041953A1

    公开(公告)日：2012-02-16

    申请号：US12857518

    申请日：2010-08-16

    申请人： Susan Theresa Dumais ,  Daniel Ramage ,  Daniel John Liebling ,  Steven Mark Drucker

    发明人： Susan Theresa Dumais ,  Daniel Ramage ,  Daniel John Liebling ,  Steven Mark Drucker

    IPC分类号： G06F17/30

    CPC分类号： G06F16/353

    摘要： A latent topic labels text mining system and method to mine and analyze the content of textual data. Embodiments of the system and method are particularly well suited for use on microblog data to help people identify posts they want to read and to find people that they want to follow. Embodiments of the system and method use a modified Labeled LDA technique (called an L+LDA technique) that analyzes content using a combination of labeled and latent topics. The resultant data is assigned labels one of four labels to generate a lower-dimensional representation of the data that the individual words in a microblog post. This learned topic representation is used to characterize, summarize, filter, find, suggest, and compare the content of microblog posts. Embodiments of the system and method also include visualization techniques such as a tag cloud visualization that is used to visualize microblogging data.

    摘要翻译： 潜在主题标签文本挖掘系统和方法来挖掘和分析文本数据的内容。 系统和方法的实施例特别适用于微博数据，以帮助人们识别他们想要阅读的帖子，并找到他们想要遵循的人。 该系统和方法的实施例使用经修改的标记LDA技术（称为L + LDA技术），其使用标记和潜在主题的组合来分析内容。 为四个标签中的一个标签分配标签，以生成微博文档中各个单词的数据的低维表示。 这个学习的主题表示用于表征，总结，过滤，查找，建议和比较微博帖子的内容。 系统和方法的实施例还包括可视化技术，例如用于可视化微博数据的标签云可视化。

    EXPLORING DATA USING MULTIPLE MACHINE-LEARNING MODELS
    7.
    发明申请
    EXPLORING DATA USING MULTIPLE MACHINE-LEARNING MODELS 有权
    使用多种机器学习模型探索数据

    公开(公告)号：US20110307422A1

    公开(公告)日：2011-12-15

    申请号：US12797395

    申请日：2010-06-09

    申请人： Steven Mark Drucker ,  Kayur Dushyant Patel ,  Desney S. Tan ,  Ashish Kapoor ,  James Anthony Fogarty

    发明人： Steven Mark Drucker ,  Kayur Dushyant Patel ,  Desney S. Tan ,  Ashish Kapoor ,  James Anthony Fogarty

    IPC分类号： G06F15/18

    CPC分类号： G06N99/005

    摘要： A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data.

    摘要翻译： 一种用于同时运行多个机器学习模型的多模型数据挖掘系统和方法，用于理解和探索数据。 该系统和方法的实施例允许用户更好地理解数据并获得对其数据的新见解。 系统和方法的实施例还允许用户交互地探索问题并导航数据的不同视图。 同时运行许多不同的分类器训练和评估实验，得到结果。 结果在每个实验中进行聚合和可视化，以确定和理解每个示例如何分类给每个不同的分类器。 然后以各种方式总结这些结果，以允许用户在各个示例本身和与数据相关的特征方面获得对数据的更多了解。