专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明授权

US07840516B2 Preserving privacy of one-dimensional data streams by perturbing data with noise and using dynamic autocorrelation 失效
标题翻译：通过噪声干扰数据并使用动态自相关来保护一维数据流的隐私
公开(公告)号：US07840516B2
公开(公告)日：2010-11-23
申请号：US11678808
申请日：2007-02-26
申请人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
发明人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
IPC分类号： G06F17/00
CPC分类号： G06F21/755
摘要： A method, information processing system, and computer readable medium are provided for preserving privacy of one-dimensional nonstationary data streams. The method includes receiving a one-dimensional nonstationary data stream. A set of first-moment statistical values are calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.
摘要翻译：提供了一种方法，信息处理系统和计算机可读介质，用于保持一维非平稳数据流的隐私。该方法包括接收一维非平稳数据流。对于数据的子时间空间的给定时刻，计算一组一阶统计值。第一时刻统计值包括时间子空间的主成分。数据按照与主要分量成比例的噪声与第一时刻的统计值相互扰动，使得数据的至少一部分二阶统计值仅在预定方差内被噪声扰动。

2. 发明申请

US20080209568A1 PRESERVING PRIVACY OF DATA STREAMS USING DYNAMIC CORRELATIONS 失效
标题翻译：使用动态关联保护数据流的隐私
公开(公告)号：US20080209568A1
公开(公告)日：2008-08-28
申请号：US11678786
申请日：2007-02-26
申请人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
发明人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
IPC分类号： G06F21/06
CPC分类号： H04L63/0407 , G06F21/6245 , G06F2207/7219
摘要： Disclosed is a method, information processing system, and computer readable medium for preserving privacy of nonstationary data streams. The method includes receiving at least one nonstationary data stream with time dependent data. Calculating, for a given instant of sub-space of time, A set of first-moment statistical values is calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.
摘要翻译：公开了一种用于保持非平稳数据流的隐私的方法，信息处理系统和计算机可读介质。该方法包括接收具有时间相关数据的至少一个非平稳数据流。对于给定时间子空间的计算，对于数据的子时间空间的给定时刻，计算一组一阶统计值。第一时刻统计值包括时间子空间的主成分。数据按照与主要分量成比例的噪声与第一时刻的统计值相互扰动，使得数据的至少一部分二阶统计值仅在预定方差内被噪声扰动。

3. 发明授权

US07853545B2 Preserving privacy of one-dimensional data streams using dynamic correlations 失效
标题翻译：使用动态相关性保护一维数据流的隐私
公开(公告)号：US07853545B2
公开(公告)日：2010-12-14
申请号：US11678786
申请日：2007-02-26
申请人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
发明人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
IPC分类号： G06F17/00
CPC分类号： H04L63/0407 , G06F21/6245 , G06F2207/7219
摘要： Disclosed is a method, information processing system, and computer readable medium for preserving privacy of nonstationary data streams. The method includes receiving at least one nonstationary data stream with time dependent data. Calculating, for a given instant of sub-space of time, A set of first-moment statistical values is calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.
摘要翻译：公开了一种用于保持非平稳数据流的隐私的方法，信息处理系统和计算机可读介质。该方法包括接收具有时间相关数据的至少一个非平稳数据流。对于给定时间子空间的计算，对于数据的子时间空间的给定时刻，计算一组一阶统计值。第一时刻统计值包括时间子空间的主成分。数据按照与主要分量成比例的噪声与第一时刻的统计值相互扰动，使得数据的至少一部分二阶统计值仅在预定方差内被噪声扰动。

4. 发明申请

US20080205641A1 PRESERVING PRIVACY OF ONE-DIMENSIONAL DATA STREAMS USING DYNAMIC AUTOCORRELATION 失效
标题翻译：使用动态自动保存保护一维数据流的隐私
公开(公告)号：US20080205641A1
公开(公告)日：2008-08-28
申请号：US11678808
申请日：2007-02-26
申请人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
发明人： Yuan-Chi Chang , Feifei Li , Spyridon Papadimitriou , George A. Mihaila , Ioana Stanoi , Jimeng Sun , Philip S. Yu
IPC分类号： H04L9/18
CPC分类号： G06F21/755
摘要： A method, information processing system, and computer readable medium are provided for preserving privacy of one-dimensional nonstationary data streams. The method includes receiving a one-dimensional nonstationary data stream. A set of first-moment statistical values are calculated, for a given instant of sub-space of time, for the data. The first moment statistical values include a principal component for the sub-space of time. The data is perturbed with noise along the principal component in proportion to the first-moment of statistical values so that at least part of a set of second-moment statistical values for the data is perturbed by the noise only within a predetermined variance.
摘要翻译：提供了一种方法，信息处理系统和计算机可读介质，用于保持一维非平稳数据流的隐私。该方法包括接收一维非平稳数据流。对于数据的子时间空间的给定时刻，计算一组一阶统计值。第一时刻统计值包括时间子空间的主成分。数据按照与主要分量成比例的噪声与第一时刻的统计值相互扰动，使得数据的至少一部分二维统计值仅在预定方差内被噪声扰动。

5. 发明授权

US07505876B2 Systems and methods for simultaneous summarization of data cube streams 失效
标题翻译：同时汇总数据立方体流的系统和方法
公开(公告)号：US07505876B2
公开(公告)日：2009-03-17
申请号：US11620679
申请日：2007-01-07
申请人： Spyridon Papadimitriou , Jimeng Sun , Philip S. Yu
发明人： Spyridon Papadimitriou , Jimeng Sun , Philip S. Yu
IPC分类号： G06F15/00 , G06F17/30
CPC分类号： G06F17/30516 , G06F17/30241 , G06F17/30489 , G06F17/30551 , Y10S707/99936
摘要： In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.
摘要翻译：在一个示例性实施例中，本发明的一些主要方面如下：（i）数据模型：我们引入张量流以处理多方面流的大集合; 和（ii）算法框架：我们提出基于窗口的张量分析（WTA）来有效地从张量流中提取核心模式。张量表示与在线分析处理（OLAP）中的数据立方体相关。然而，我们的本发明专注于为每个窗口构造简单的摘要，而不仅仅是组织数据以沿着每个方面或方面的组合来产生简单的聚合。

6. 发明申请

US20080168375A1 SYSTEMS AND METHODS FOR SIMULTANEOUS SUMMARIZATION OF DATA CUBE STREAMS 失效
标题翻译：数据库流程同步总结的系统和方法
公开(公告)号：US20080168375A1
公开(公告)日：2008-07-10
申请号：US11620679
申请日：2007-01-07
申请人： Spyridon Papadimitriou , Jimeng Sun , Philip S. Yu
发明人： Spyridon Papadimitriou , Jimeng Sun , Philip S. Yu
IPC分类号： G06F3/048
CPC分类号： G06F17/30516 , G06F17/30241 , G06F17/30489 , G06F17/30551 , Y10S707/99936
摘要： In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.
摘要翻译：在一个示例性实施例中，本发明的一些主要方面如下：（i）数据模型：我们引入张量流以处理多方面流的大集合; 和（ii）算法框架：我们提出基于窗口的张量分析（WTA）来有效地从张量流中提取核心模式。张量表示与在线分析处理（OLAP）中的数据立方体相关。然而，我们的本发明专注于为每个窗口构造简单的摘要，而不仅仅是组织数据以沿着每个方面或方面的组合来产生简单的聚合。

7. 发明申请

US20110055379A1 CONTENT-BASED AND TIME-EVOLVING SOCIAL NETWORK ANALYSIS 有权
标题翻译：基于内容和时间演化的社会网络分析
公开(公告)号：US20110055379A1
公开(公告)日：2011-03-03
申请号：US12552812
申请日：2009-09-02
申请人： Ching-Yung Lin , Spyridon Papadimitriou , Jimeng Sun , Kun-Lung Wu
发明人： Ching-Yung Lin , Spyridon Papadimitriou , Jimeng Sun , Kun-Lung Wu
IPC分类号： G06F17/10 , G06F15/173
CPC分类号： G06K9/00677 , G06K9/6224 , G06Q30/02
摘要： System and method for modeling a content-based network. The method includes finding single mode clusters from among network (sender and recipient) and content dimensions represented as a tensor data structure. The method allows for derivation of useful cross-mode clusters (interpretable patterns) that reveal key relationships among user communities and keyword concepts for presentation to users in a meaningful and intuitive way. Additionally, the derivation of useful cross-mode clusters is facilitated by constructing a reduced low-dimensional representation of the content-based network. Moreover, the invention may be enhanced for modeling and analyzing the time evolution of social communication networks and the content related to such networks. To this end, a set of non-overlapping or possibly overlapping time-based windows is constructed and the analysis performed at each successive time interval.
摘要翻译：用于建模基于内容的网络的系统和方法。该方法包括从网络（发送者和接收者）和表示为张量数据结构的内容维度中找到单一模式集群。该方法允许推导出有用的交叉模式集群（可解释模式），其显示用户社区之间的关键关系和关键字概念，以有意义和直观的方式呈现给用户。另外，通过构建基于内容的网络的减少的低维表示来促进有用的交叉模式集群的推导。此外，本发明可以被增强用于建模和分析社交通信网络的时间演进和与这样的网络有关的内容。为此，构建一组不重叠或可能重叠的基于时间的窗口，并且在每个连续的时间间隔执行分析。

8. 发明授权

US08818918B2 Determining the importance of data items and their characteristics using centrality measures 有权
标题翻译：使用中心性措施确定数据项的重要性及其特征
公开(公告)号：US08818918B2
公开(公告)日：2014-08-26
申请号：US13096220
申请日：2011-04-28
申请人： Ching-Yung Lin , Hanghang Tong , Jimeng Sun , Spyridon Papadimitriou , U Kang
发明人： Ching-Yung Lin , Hanghang Tong , Jimeng Sun , Spyridon Papadimitriou , U Kang
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G06N5/003
摘要： Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.
摘要翻译：用于确定数据项的重要性的计算机实现的方法，系统和制造。一种方法包括：（a）接收节点图; （b）近似一个节点的邻居节点数; 和（c）使用近似步骤计算节点与剩余节点的平均最短路径长度，其中该计算表明由节点表示的数据项的重要性。另一种方法包括：（a）接收节点图; （b）构建节点图的分解线图; （c）计算分解线图中节点图形节点的入射边缘的固定概率，以及（d）计算与节点相关联的入射边缘的固定概率的总和，其中求和表示数据项的重要性由节点表示。两种方法都使用计算机设备进行至少一个步骤。

9. 发明申请

US20120278261A1 DETERMINING THE IMPORTANCE OF DATA ITEMS AND THEIR CHARACTERISTICS USING CENTRALITY MEASURES 有权
标题翻译：确定数据项的重要性及其使用中心度量的特性
公开(公告)号：US20120278261A1
公开(公告)日：2012-11-01
申请号：US13096220
申请日：2011-04-28
申请人： Ching-Yung Lin , Hanghang Tong , Jimeng Sun , Spyridon Papadimitriou , U Kang
发明人： Ching-Yung Lin , Hanghang Tong , Jimeng Sun , Spyridon Papadimitriou , U Kang
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G06N5/003
摘要： Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.
摘要翻译：用于确定数据项的重要性的计算机实现的方法，系统和制造。一种方法包括：（a）接收节点图; （b）近似一个节点的邻居节点数; 和（c）使用近似步骤计算节点与剩余节点的平均最短路径长度，其中该计算表明由节点表示的数据项的重要性。另一种方法包括：（a）接收节点图; （b）构建节点图的分解线图; （c）计算分解线图中节点图形节点的入射边缘的固定概率，以及（d）计算与节点相关联的入射边缘的固定概率的总和，其中求和表示数据项的重要性由节点表示。两种方法都使用计算机设备进行至少一个步骤。

10. 发明申请

US20070294247A1 Identifying optimal multi-scale patterns in time-series streams 审中-公开
标题翻译：确定时间序列流中的最优多尺度模式
公开(公告)号：US20070294247A1
公开(公告)日：2007-12-20
申请号：US11471002
申请日：2006-06-20
申请人： Spyridon Papadimitriou , Philip S. Yu
发明人： Spyridon Papadimitriou , Philip S. Yu
IPC分类号： G06F17/30
CPC分类号： G06K9/00496
摘要： A method, system, and computer readable medium for identifying local patterns in at least one time series data stream are disclosed. The method comprises generating multiple ordered levels of hierarchal approximation functions. The multiple ordered levels are generated directly from at least one given time series data stream including at least one set of time series data. The hierarchical approximation functions for each level of the multiple levels is based upon creating a set of approximating functions. The hierarchical approximation functions are also based upon selecting a current window with a current window length from a set of varying window lengths. The current window is selected for a current level of the multiple levels.
摘要翻译：公开了一种用于识别至少一个时间序列数据流中的局部模式的方法，系统和计算机可读介质。该方法包括生成层次近似函数的多个有序级别。多个有序级别直接从包括至少一组时间序列数据的至少一个给定时间序列数据流生成。多层次的每个级别的层次近似函数基于创建一组近似函数。层次近似函数还基于从一组变化的窗口长度中选择具有当前窗口长度的当前窗口。为当前级别选择当前窗口。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式