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1. 发明申请

US20080071721A1 System and method for learning models from scarce and skewed training data 失效
标题翻译：从稀缺和倾斜的训练数据中学习模型的系统和方法
公开(公告)号：US20080071721A1
公开(公告)日：2008-03-20
申请号：US11506226
申请日：2006-08-18
申请人： Haixun Wang , Jian Yin , Philip Shi-lung Yu
发明人： Haixun Wang , Jian Yin , Philip Shi-lung Yu
IPC分类号： G06N5/02
CPC分类号： G06N99/005
摘要： A system and method for learning models from scarce and/or skewed training data includes partitioning a data stream into a sequence of time windows. A most likely current class distribution to classify portions of the data stream is determined based on observing training data in a current time window and based on concept drift probability patterns using historical information.
摘要翻译：用于从稀缺和/或倾斜的训练数据学习模型的系统和方法包括将数据流划分成时间窗口序列。基于在当前时间窗口中观察训练数据并且基于使用历史信息的概念漂移概率模式来确定对数据流的部分进行分类的最可能的当前类别分布。

2. 发明授权

US07630950B2 System and method for learning models from scarce and skewed training data 失效
标题翻译：从稀缺和倾斜的训练数据中学习模型的系统和方法
公开(公告)号：US07630950B2
公开(公告)日：2009-12-08
申请号：US11506226
申请日：2006-08-18
申请人： Haixun Wang , Jian Yin , Philip Shi-lung Yu
发明人： Haixun Wang , Jian Yin , Philip Shi-lung Yu
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G06N99/005
摘要： A system and method for learning models from scarce and/or skewed training data includes partitioning a data stream into a sequence of time windows. A most likely current class distribution to classify portions of the data stream is determined based on observing training data in a current time window and based on concept drift probability patterns using historical information.
摘要翻译：用于从稀缺和/或倾斜的训练数据学习模型的系统和方法包括将数据流划分成时间窗口序列。基于在当前时间窗口中观察训练数据并且基于使用历史信息的概念漂移概率模式来确定对数据流的部分进行分类的最可能的当前类别分布。

3. 发明授权

US07890471B2 Index structure for supporting structural XML queries 失效
标题翻译：用于支持结构XML查询的索引结构
公开(公告)号：US07890471B2
公开(公告)日：2011-02-15
申请号：US11780095
申请日：2007-07-19
申请人： Wei Fan , Haixun Wang , Philip Shi-Lung Yu
发明人： Wei Fan , Haixun Wang , Philip Shi-Lung Yu
IPC分类号： G06F7/00
CPC分类号： G06F17/30911 , Y10S707/99933 , Y10S707/99943
摘要： The present invention provides a ViST (or “virtual suffix tree”), which is a novel index structure for searching XML documents. By representing both XML documents and XML queries in structure-encoded sequences, it is shown that querying XML data is equivalent to finding (non-contiguous) subsequence matches. A variety of XML queries, including those with branches, or wild-cards (‘*’ and ‘//’), can be expressed by structure-encoded sequences. Unlike index methods that disassemble a query into multiple sub-queries, and then join the results of these sub-queries to provide the final answers, ViST uses tree structures as the basic unit of query to avoid expensive join operations. Furthermore, ViST provides a unified index on both content and structure of the XML documents, hence it has a performance advantage over methods indexing either just content or structure. ViST supports dynamic index update, and it relies solely on B+Trees without using any specialized data structures that are not well supported by common database management systems (hereinafter referred to as “DBMSs”).
摘要翻译：本发明提供了一种ViST（或“虚拟后缀树”），其是用于搜索XML文档的新型索引结构。通过在结构编码序列中同时表示XML文档和XML查询，显示查询XML数据等同于查找（非连续）子序列匹配。各种XML查询（包括具有分支的查询）或通配符（'*'和'//'）可以由结构编码的序列表示。不同于将查询反汇编成多个子查询的索引方法，然后加入这些子查询的结果以提供最终答案，ViST使用树结构作为查询的基本单位，以避免昂贵的连接操作。此外，ViST为XML文档的内容和结构提供了一个统一的索引，因此与仅通过内容或结构索引方法相比，它具有性能优势。 ViST支持动态索引更新，它仅依赖于B + Tree，而不使用通用数据库管理系统（以下简称“DBMS”）不太支持的任何专门的数据结构。

4. 发明授权

US07243100B2 Methods and apparatus for mining attribute associations 失效
标题翻译：挖掘属性关联的方法和装置
公开(公告)号：US07243100B2
公开(公告)日：2007-07-10
申请号：US10630992
申请日：2003-07-30
申请人： Sheng Ma , Chang-shing Perng , Haixun Wang , Philip Shi-Lung Yu
发明人： Sheng Ma , Chang-shing Perng , Haixun Wang , Philip Shi-Lung Yu
IPC分类号： G06F17/30 , G06F17/00
CPC分类号： G06F17/18 , G06F17/30539 , Y10S707/99936 , Y10S707/99937 , Y10S707/99943
摘要： Attribute association discovery techniques that support relational-based data mining are disclosed. In one aspect of the invention, a technique for mining attribute associations in a relational data set comprises the following steps/operations. Multiple items are obtained from the relational data set. Then, attribute associations are discovered using: (i) multi-attribute mining templates formed from at least a portion of the multiple items; and (ii) one or more mining preferences specified by a user. The invention provides a novel architecture for the mining search space so as to exploit the inter-relationships among patterns of different templates. The framework is relational-sensitive and supports interactive and online mining.
摘要翻译：公开了支持基于关系的数据挖掘的属性关联发现技术。在本发明的一个方面，用于挖掘关系数据集中的属性关联的技术包括以下步骤/操作。从关系数据集获得多个项目。然后，使用以下方式发现属性关联：（i）由多个项目的至少一部分形成的多属性挖掘模板; 和（ii）用户指定的一个或多个挖掘偏好。本发明提供了一种用于挖掘搜索空间的新型架构，以便利用不同模板的模式之间的相互关系。该框架是关系敏感的，支持交互式和在线挖掘。

5. 发明授权

US07890649B2 System and method for scalable processing of multi-way data stream correlations 失效
标题翻译：用于多路数据流相关性的可扩展处理的系统和方法
公开(公告)号：US07890649B2
公开(公告)日：2011-02-15
申请号：US12478627
申请日：2009-06-04
申请人： Xiaohui Gu , Haixun Wang , Philip Shi-lung Yu
发明人： Xiaohui Gu , Haixun Wang , Philip Shi-lung Yu
IPC分类号： G06F7/00
CPC分类号： G06F17/30516 , G06F17/30985 , Y10S707/99945 , Y10S707/99948
摘要： A computer implemented method, apparatus, and computer usable program code for processing multi-way stream correlations. Stream data are received for correlation. A task is formed for continuously partitioning a multi-way stream correlation workload into smaller workload pieces. Each of the smaller workload pieces may be processed by a single host. The stream data are sent to different hosts for correlation processing.
摘要翻译：一种用于处理多路流相关性的计算机实现的方法，装置和计算机可用程序代码。接收流数据进行相关。形成一个任务，用于将多路流相关工作负载连续划分成较小的工作负载。每个较小的工作负载片段可以由单个主机处理。流数据被发送到不同的主机进行相关处理。

6. 发明授权

US07548937B2 System and method for scalable processing of multi-way data stream correlations 失效
标题翻译：用于多路数据流相关性的可扩展处理的系统和方法
公开(公告)号：US07548937B2
公开(公告)日：2009-06-16
申请号：US11417838
申请日：2006-05-04
申请人： Xiaohui Gu , Haixun Wang , Philip Shi-lung Yu
发明人： Xiaohui Gu , Haixun Wang , Philip Shi-lung Yu
IPC分类号： G06F7/00 , G06F15/16
CPC分类号： G06F17/30516 , G06F17/30985 , Y10S707/99945 , Y10S707/99948
摘要： A computer implemented method, apparatus, and computer usable program code for processing multi-way stream correlations. Stream data are received for correlation. A task is formed for continuously partitioning a multi-way stream correlation workload into smaller workload pieces. Each of the smaller workload pieces may be processed by a single host. The stream data are sent to different hosts for correlation processing.
摘要翻译：一种用于处理多路流相关性的计算机实现的方法，装置和计算机可用程序代码。接收流数据进行相关。形成一个任务，用于将多路流相关工作负载连续划分成较小的工作负载。每个较小的工作负载片段可以由单个主机处理。流数据被发送到不同的主机进行相关处理。

7. 发明授权

US09009176B2 System and method for indexing weighted-sequences in large databases 有权
标题翻译：用于索引大数据库中加权序列的系统和方法
公开(公告)号：US09009176B2
公开(公告)日：2015-04-14
申请号：US12198717
申请日：2008-08-26
申请人： Wei Fan , Chang-Shing Perng , Haixun Wang , Philip Shi-Lung Yu
发明人： Wei Fan , Chang-Shing Perng , Haixun Wang , Philip Shi-Lung Yu
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30327 , G06F17/30548 , Y10S707/99943
摘要： The present invention provides an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure in which each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence because each event is associated with a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed herein enables the efficient retrieval from the database of all subsequences (contiguous and non-contiguous) that match a given query sequence both by events and by weights. The index structure also takes into consideration the nonuniform frequency distribution of events in the sequence data.
摘要翻译：本发明提供了一种用于在大数据库中管理加权序列的索引结构。加权序列被定义为二维结构，其中序列中的每个元素与权重相关联。例如，一系列网络事件是加权序列，因为每个事件都与时间戳相关联。通过事件发生模式查询大序列数据库是了解事件之间的时间因果关系的第一步。这里提出的索引结构使得能够通过事件和权重从数据库有效地检索与给定查询序列匹配的所有子序列（连续的和不连续的）。索引结构还考虑了序列数据中事件的不均匀频率分布。

8. 发明申请

US20090248749A1 System and Method for Scalable Processing of Multi-Way Data Stream Correlations 失效
标题翻译：用于多路数据流相关性的可扩展处理的系统和方法
公开(公告)号：US20090248749A1
公开(公告)日：2009-10-01
申请号：US12478627
申请日：2009-06-04
申请人： Xiaohui Gu , Haixun Wang , Philip Shi-lung Yu
发明人： Xiaohui Gu , Haixun Wang , Philip Shi-lung Yu
IPC分类号： G06F17/30 , G06F15/16
CPC分类号： G06F17/30516 , G06F17/30985 , Y10S707/99945 , Y10S707/99948
摘要： A computer implemented method, apparatus, and computer usable program code for processing multi-way stream correlations. Stream data are received for correlation. A task is formed for continuously partitioning a multi-way stream correlation workload into smaller workload pieces. Each of the smaller workload pieces may be processed by a single host. The stream data are sent to different hosts for correlation processing.
摘要翻译：一种用于处理多路流相关性的计算机实现的方法，装置和计算机可用程序代码。接收流数据进行相关。形成一个任务，用于将多路流相关工作负载连续划分成较小的工作负载。每个较小的工作负载片段可以由单个主机处理。流数据被发送到不同的主机进行相关处理。

9. 发明授权

US07418455B2 System and method for indexing weighted-sequences in large databases 有权
标题翻译：用于索引大数据库中加权序列的系统和方法
公开(公告)号：US07418455B2
公开(公告)日：2008-08-26
申请号：US10723229
申请日：2003-11-26
申请人： Wei Fan , Chang-Shing Perng , Haixun Wang , Philip Shi-Lung Yu
发明人： Wei Fan , Chang-Shing Perng , Haixun Wang , Philip Shi-Lung Yu
IPC分类号： G06F7/00 , G06F17/00
CPC分类号： G06F17/30327 , G06F17/30548 , Y10S707/99943
摘要： The present invention provides an index structure for managing weighted-sequences in large databases. A weighted-sequence is defined as a two-dimensional structure in which each element in the sequence is associated with a weight. A series of network events, for instance, is a weighted-sequence because each event is associated with a timestamp. Querying a large sequence database by events' occurrence patterns is a first step towards understanding the temporal causal relationships among the events. The index structure proposed herein enables the efficient retrieval from the database of all subsequences (contiguous and non-contiguous) that match a given query sequence both by events and by weights. The index structure also takes into consideration the nonuniform frequency distribution of events in the sequence data.
摘要翻译：本发明提供了一种用于在大数据库中管理加权序列的索引结构。加权序列被定义为二维结构，其中序列中的每个元素与权重相关联。例如，一系列网络事件是加权序列，因为每个事件都与时间戳相关联。通过事件发生模式查询大序列数据库是了解事件之间的时间因果关系的第一步。这里提出的索引结构使得能够通过事件和权重从数据库有效地检索与给定查询序列匹配的所有子序列（连续的和不连续的）。索引结构还考虑了序列数据中事件的不均匀频率分布。

10. 发明授权

US07287023B2 Index structure for supporting structural XML queries 失效
标题翻译：用于支持结构XML查询的索引结构
公开(公告)号：US07287023B2
公开(公告)日：2007-10-23
申请号：US10723206
申请日：2003-11-26
申请人： Wei Fan , Haixun Wang , Philip Shi-Lung Yu
发明人： Wei Fan , Haixun Wang , Philip Shi-Lung Yu
IPC分类号： G06F17/30
CPC分类号： G06F17/30911 , Y10S707/99933 , Y10S707/99943
摘要： The present invention provides a ViST (or “virtual suffix tree”), which is a novel index structure for searching XML documents. By representing both XML documents and XML queries in structure-encoded sequences, it is shown that querying XML data is equivalent to finding (non-contiguous) subsequence matches. A variety of XML queries, including those with branches, or wild-cards (‘*’ and ‘//’), can be expressed by structure-encoded sequences. Unlike index methods that disassemble a query into multiple sub-queries, and then join the results of these sub-queries to provide the final answers, ViST uses tree structures as the basic unit of query to avoid expensive join operations. Furthermore, ViST provides a unified index on both content and structure of the XML documents, hence it has a performance advantage over methods indexing either just content or structure. ViST supports dynamic index update, and it relies solely on B+Trees without using any specialized data structures that are not well supported by common database management systems (hereinafter referred to as “DBMSs”).
摘要翻译：本发明提供了一种ViST（或“虚拟后缀树”），其是用于搜索XML文档的新型索引结构。通过在结构编码序列中同时表示XML文档和XML查询，显示查询XML数据等同于查找（非连续）子序列匹配。各种XML查询（包括具有分支的查询）或通配符（'*'和'//'）可以由结构编码的序列表示。不同于将查询反汇编成多个子查询的索引方法，然后加入这些子查询的结果以提供最终答案，ViST使用树结构作为查询的基本单位，以避免昂贵的连接操作。此外，ViST为XML文档的内容和结构提供了一个统一的索引，因此与仅通过内容或结构索引方法相比，它具有性能优势。 ViST支持动态索引更新，它仅仅依赖于B＆lt; +＆gt;树，而不使用通用数据库管理系统（以下简称“DBMS”）不能很好地支持的任何专门的数据结构。

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