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1. 发明申请

US20090287721A1 Generating conditional functional dependencies 失效
标题翻译：生成条件函数依赖
公开(公告)号：US20090287721A1
公开(公告)日：2009-11-19
申请号：US12380858
申请日：2009-03-03
申请人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Bei Yu
发明人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Bei Yu
IPC分类号： G06F17/30
CPC分类号： G06F17/30604
摘要： Techniques are disclosed for generating conditional functional dependency (CFD) pattern tableaux having the desirable properties of support, confidence and parsimony. These techniques include both a greedy algorithm for generating a tableau and, for large data sets, an “on-demand” algorithm that outperforms the basic greedy algorithm in running time by an order of magnitude. In addition, a range tableau, as a generalization of a pattern tableau, can achieve even more parsimony.
摘要翻译：公开了用于产生具有所需性质的支持，置信和简约的条件功能依赖（CFD）模式表的技术。这些技术既包括用于生成表格的贪心算法，又包括用于大数据集的“按需”算法，其在运行时间上超过基本贪心算法一个数量级。另外，作为模式表格的泛化，范围表可以实现更加简单。

2. 发明授权

US08639667B2 Generating conditional functional dependencies 失效
标题翻译：生成条件函数依赖
公开(公告)号：US08639667B2
公开(公告)日：2014-01-28
申请号：US12380858
申请日：2009-03-03
申请人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Bei Yu
发明人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Bei Yu
IPC分类号： G06F7/00 , G06F17/00 , G06F17/30
CPC分类号： G06F17/30604
摘要： Techniques are disclosed for generating conditional functional dependency (CFD) pattern tableaux having the desirable properties of support, confidence and parsimony. These techniques include both a greedy algorithm for generating a tableau and, for large data sets, an “on-demand” algorithm that outperforms the basic greedy algorithm in running time by an order of magnitude. In addition, a range tableau, as a generalization of a pattern tableau, can achieve even more parsimony.
摘要翻译：公开了用于产生具有所需性质的支持，置信和简约的条件功能依赖（CFD）模式表的技术。这些技术既包括用于生成表格的贪心算法，又包括用于大数据集的“按需”算法，其在运行时间上超过基本贪心算法一个数量级。另外，作为模式表格的泛化，范围表可以实现更加简单。

3. 发明授权

US08645309B2 Processing data using sequential dependencies 有权
标题翻译：使用顺序依赖来处理数据
公开(公告)号：US08645309B2
公开(公告)日：2014-02-04
申请号：US12592586
申请日：2009-11-30
申请人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Avishek Saha
发明人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Avishek Saha
IPC分类号： G06F17/00 , G06N7/04 , G06N5/02 , G06N7/00 , G06N7/08 , G06F11/00 , G06F11/30 , G06C25/00 , H03M13/00 , H04L1/00
CPC分类号： G06N7/00 , G06N5/00
摘要： The specification describes data processes for analyzing large data steams for target anomalies. “Sequential dependencies” (SDs) are chosen for ordered data and present a framework for discovering which subsets of the data obey a given sequential dependency. Given an interval G, an SD on attributes X and Y, written as X→G Y, denotes that the distance between the Y-values of any two consecutive records, when sorted on X, are within G. SDs may be extended to Conditional Sequential Dependencies (CSDs), consisting of an underlying SD plus a representation of the subsets of the data that satisfy the SD. The conditional approximate sequential dependencies may be expressed as pattern tableaux, i.e., compact representations of the subsets of the data that satisfy the underlying dependency.
摘要翻译：该规范描述了用于分析目标异常的大型数据流的数据处理。为有序数据选择“顺序依赖”（SDs），并提供一个框架，用于发现数据的哪些子集服从给定的顺序依赖。给定一个间隔G，写入X> GY的属性X和Y上的SD表示当在X上排序时，任何两个连续记录的Y值之间的距离在G之内可以扩展到条件顺序依赖性（CSD）由基础SD加上满足SD的数据子集的表示组成。条件近似顺序依赖性可以表示为模式表，即满足基础依赖性的数据子集的紧凑表示。

4. 发明申请

US20120130935A1 Conservation dependencies 有权
标题翻译：保护依赖
公开(公告)号：US20120130935A1
公开(公告)日：2012-05-24
申请号：US12927770
申请日：2010-11-23
申请人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Barna Saha
发明人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Barna Saha
IPC分类号： G06N5/02
CPC分类号： G06Q40/025 , G06N5/02
摘要： Given a set of data for which a conservation law is an appropriate characterization, “hold” and/or “fail” tableaux are provided for the underlying conservation law, thereby providing a conservation dependency whereby portions of the data for which the law approximately holds or fails can be discovered and summarized in a semantically meaningful way.
摘要翻译：给定一套保护法是适当表征的数据，为基础守恒定律提供“保持”和/或“失败”表，从而提供保护依赖性，依赖于法律约束的数据部分或可以以语义有意义的方式发现和总结失败。

5. 发明申请

US20110131170A1 Processing data using sequential dependencies 有权
标题翻译：使用顺序依赖来处理数据
公开(公告)号：US20110131170A1
公开(公告)日：2011-06-02
申请号：US12592586
申请日：2009-11-30
申请人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Avishek Saha
发明人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Avishek Saha
IPC分类号： G06N5/02
CPC分类号： G06N7/00 , G06N5/00
摘要： The specification describes data processes for analyzing large data steams for target anomalies. “Sequential dependencies” (SDs) are chosen for ordered data and present a framework for discovering which subsets of the data obey a given sequential dependency. Given an interval G, an SD on attributes X and Y, written as X→G Y, denotes that the distance between the Y-values of any two consecutive records, when sorted on X, are within G. SDs may be extended to Conditional Sequential Dependencies (CSDs), consisting of an underlying SD plus a representation of the subsets of the data that satisfy the SD. The conditional approximate sequential dependencies may be expressed as pattern tableaux, i.e., compact representations of the subsets of the data that satisfy the underlying dependency.
摘要翻译：该规范描述了用于分析目标异常的大型数据流的数据处理。为有序数据选择“顺序依赖”（SDs），并提供一个框架，用于发现数据的哪些子集服从给定的顺序依赖。给定间隔G，写入X→GY的属性X和Y上的SD表示当在X上排序时任何两个连续记录的Y值之间的距离在G内。可以扩展到条件序列依赖关系（CSDs）由基础SD加上满足SD的数据子集的表示组成。条件近似顺序依赖性可以表示为模式表，即满足基础依赖性的数据子集的紧凑表示。

6. 发明授权

US09177343B2 Conservation dependencies 有权
标题翻译：保护依赖
公开(公告)号：US09177343B2
公开(公告)日：2015-11-03
申请号：US12927770
申请日：2010-11-23
申请人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Barna Saha
发明人： Lukasz Golab , Howard Karloff , Philip Korn , Divesh Srivastava , Barna Saha
IPC分类号： G06N5/02 , G06Q40/02
CPC分类号： G06Q40/025 , G06N5/02
摘要： Given a set of data for which a conservation law is an appropriate characterization, “hold” and/or “fail” tableaux are provided for the underlying conservation law, thereby providing a conservation dependency whereby portions of the data for which the law approximately holds or fails can be discovered and summarized in a semantically meaningful way.
摘要翻译：给定一套保护法是适当表征的数据，为基础守恒定律提供“保持”和/或“失败”表，从而提供保护依赖性，依赖于法律约束的数据部分或可以以语义有意义的方式发现和总结失败。

7. 发明申请

US20050131946A1 Method and apparatus for identifying hierarchical heavy hitters in a data stream 审中-公开
标题翻译：用于识别数据流中的分级重击打者的方法和装置
公开(公告)号：US20050131946A1
公开(公告)日：2005-06-16
申请号：US10802605
申请日：2004-03-17
申请人： Philip Korn , Shanmugavelayutham Muthukrishnan , Divesh Srivastava , Graham Cormode
发明人： Philip Korn , Shanmugavelayutham Muthukrishnan , Divesh Srivastava , Graham Cormode
IPC分类号： G06F17/00 , H04L29/06
CPC分类号： H04L63/1458
摘要： A method, apparatus, and computer readable medium for processing a data stream is described. In one example, a set of elements of a data stream are received. The set of elements are stored in a memory as a hierarchy of nodes. Each of the nodes includes frequency data associated with either an element in the set of elements or a prefix of an element in the set of elements. A set of hierarchical heavy hitters is then identified among the nodes in the hierarchy. The frequency data of each of the hierarchical heavy hitter nodes, after discounting any portion thereof attributed to a descendent hierarchical heavy hitter node in said set of hierarchical heavy hitter nodes, being greater than or equal to a fraction of the number of elements in the set of elements.
摘要翻译：描述了一种用于处理数据流的方法，装置和计算机可读介质。在一个示例中，接收数据流的一组元素。该组元素作为节点的层次结构存储在存储器中。每个节点包括与元素集合中的元素相关联的频率数据或元素集合中的元素的前缀。然后在层次结构中的节点之间识别一组分层重击打者。每个分层重型打击者节点的频率数据在折叠其归因于所述一组分级重击打者节点中的后裔分级重击球员节点的任何部分之后大于或等于该组中的元素数量的一部分的元素。

8. 发明授权

US08160837B2 Methods and apparatus to determine statistical dominance point descriptors for multidimensional data 有权
标题翻译：确定多维数据统计优势点描述符的方法和装置
公开(公告)号：US08160837B2
公开(公告)日：2012-04-17
申请号：US12334252
申请日：2008-12-12
申请人： Graham Cormode , Philip Korn , Divesh Srivastava
发明人： Graham Cormode , Philip Korn , Divesh Srivastava
IPC分类号： G06F17/18
CPC分类号： G06F17/18 , Y10S707/99932 , Y10S707/99948
摘要： Methods and apparatus to determine statistical dominance point descriptors for multidimensional data are disclosed. An example method disclosed herein comprises determining a first joint dominance value for a first data point in a multidimensional data set, data points in the multidimensional data set comprising multidimensional values, each dimension corresponding to a different measurement of a physical event, the first joint dominance value corresponding to a number of data points in the multidimensional data set dominated by the first data point in every dimension, determining a first skewness value for the first data point, the first skewness value corresponding to a size of a first dimension of the first data point relative to a combined size of all dimensions of the first data point, and combining the first joint dominance and first skewness values to determine a first statistical dominance point descriptor associated with the first data point.
摘要翻译：公开了确定多维数据的统计优势点描述符的方法和装置。本文公开的示例性方法包括确定多维数据集中的第一数据点的第一联合优势值，所述多维数据集中的数据点包括多维值，每个维度对应于物理事件的不同测量，第一联合优势对应于由每个维度中的第一数据点主导的多维数据集中的多个数据点的数值，确定第一数据点的第一偏差值，第一偏差值对应于第一数据的第一维度的大小相对于第一数据点的所有维度的组合尺寸，以及组合第一联合优势和第一偏移值以确定与第一数据点相关联的第一统计优势点描述符。

9. 发明申请

US20060053122A1 Method for matching XML twigs using index structures and relational query processors 审中-公开
标题翻译：使用索引结构和关系查询处理器来匹配XML树枝的方法
公开(公告)号：US20060053122A1
公开(公告)日：2006-03-09
申请号：US10937641
申请日：2004-09-09
申请人： Philip Korn , Nikolaos Koudas , Divesh Srivastava , Zhiyuan Chen , Johannes Gehrke , Jayavel Shanmugasundaram
发明人： Philip Korn , Nikolaos Koudas , Divesh Srivastava , Zhiyuan Chen , Johannes Gehrke , Jayavel Shanmugasundaram
IPC分类号： G06F7/00
CPC分类号： G06F16/84
摘要： A framework defining a family of index structures useful in evaluating XML path expressions (i.e., twigs) in XML database is disclosed. Within this framework, two particular index structures with different space-time tradeoffs are presented that prove effective for the evaluation of twigs with value conditions. These index structures can be realized using access methods of an underlying relational database system. Experimental results show that the indices disclosed achieve significant improvement in performance for evaluating twig queries as compared with previously proposed XML path indices.
摘要翻译：公开了一种定义在XML数据库中评估XML路径表达式（即，树枝）中有用的索引结构族的框架。在这个框架内，提出了具有不同时空权衡的两个特定索引结构，这对于评价具有价值条件的枝条是有效的。这些索引结构可以使用底层关系数据库系统的访问方法来实现。实验结果表明，与以前提出的XML路径索引相比，所公开的索引在评估树枝查询方面的性能显着提高。

10. 发明申请

US20100153064A1 Methods and Apparatus to Determine Statistical Dominance Point Descriptors for Multidimensional Data 有权
标题翻译：确定多维数据的统计优势点描述符的方法和装置
公开(公告)号：US20100153064A1
公开(公告)日：2010-06-17
申请号：US12334252
申请日：2008-12-12
申请人： Graham Cormode , Philip Korn , Divesh Srivastava
发明人： Graham Cormode , Philip Korn , Divesh Srivastava
IPC分类号： G06F17/18 , G06F17/30 , G06F15/173
CPC分类号： G06F17/18 , Y10S707/99932 , Y10S707/99948
摘要： Methods and apparatus to determine statistical dominance point descriptors for multidimensional data are disclosed. An example method disclosed herein comprises determining a first joint dominance value for a first data point in a multidimensional data set, data points in the multidimensional data set comprising multidimensional values, each dimension corresponding to a different measurement of a physical event, the first joint dominance value corresponding to a number of data points in the multidimensional data set dominated by the first data point in every dimension, determining a first skewness value for the first data point, the first skewness value corresponding to a size of a first dimension of the first data point relative to a combined size of all dimensions of the first data point, and combining the first joint dominance and first skewness values to determine a first statistical dominance point descriptor associated with the first data point.
摘要翻译：公开了确定多维数据的统计优势点描述符的方法和装置。本文公开的示例性方法包括确定多维数据集中的第一数据点的第一联合优势值，所述多维数据集中的数据点包括多维值，每个维度对应于物理事件的不同测量，第一联合优势对应于由每个维度中的第一数据点主导的多维数据集中的多个数据点的数值，确定第一数据点的第一偏差值，第一偏差值对应于第一数据的第一维度的大小相对于第一数据点的所有维度的组合尺寸，以及组合第一联合优势和第一偏移值以确定与第一数据点相关联的第一统计优势点描述符。

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