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31. 发明授权

US07496592B2 Systems and methods for maintaining closed frequent itemsets over a data stream sliding window 失效
标题翻译：在数据流滑动窗口上维护关闭频繁项目集的系统和方法
公开(公告)号：US07496592B2
公开(公告)日：2009-02-24
申请号：US11046926
申请日：2005-01-31
申请人： Yun Chi , Haixun Wang , Philip S. Yu
发明人： Yun Chi , Haixun Wang , Philip S. Yu
IPC分类号： G06F17/00
CPC分类号： G06F17/30516 , G06F17/30539 , Y10S707/99943
摘要： Towards mining closed frequent itemsets over a sliding window using limited memory space, a synopsis data structure to monitor transactions in the sliding window so that one can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets, but monitoring only frequent itemsets makes it difficult to detect new itemsets when they become frequent. Herein, there is introduced a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of itemsets over a sliding-window. The selected itemsets include a boundary between closed frequent itemsets and the rest of the itemsets Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET.
摘要翻译：通过使用有限的存储空间的滑动窗口挖掘封闭的频繁项集，用于监视滑动窗口中的事务的概要数据结构，以便可以随时输出当前关闭的频繁项集。由于时间和内存限制，概要数据结构不能监视所有可能的项集，而只监视频繁项集，使得当它们变得频繁时很难检测新的项集。在这里，引入了一种紧凑的数据结构，封闭的枚举树（CET），以便在滑动窗口上维护动态选择的一组项集。所选择的项目集包括封闭频繁项集和其余项目集之间的边界由于边界相对稳定，在滑动窗口中挖掘封闭频繁项集的成本大大降低到可能导致边界移动的采矿交易的成本 CET。

32. 发明申请

US20080215542A1 Method For Supporting Ontology-Related Semantic Queries in DBMSs with XML Support 失效
标题翻译：支持XML支持的DBMS中与本体相关的语义查询的方法
公开(公告)号：US20080215542A1
公开(公告)日：2008-09-04
申请号：US11681319
申请日：2007-03-02
申请人： Lipyeow Lim , Haixun Wang , Min Wang
发明人： Lipyeow Lim , Haixun Wang , Min Wang
IPC分类号： G06F17/30
CPC分类号： G06F17/30734 , G06F17/30404
摘要： A method for supporting semantic matching queries in a database management system (DBMS) by extracting and storing the transitive/subsumption relationships from a given ontology data in a DBMS with native XML support. These transitive relationships are transformed into a set of XML documents that are natural mappings of the hierarchical structure of the transitive relationships. A table function construct expresses semantic matching queries in a declarative manner. The semantic matching queried are automatically rewritten or translated into standard SQL/XML search operators such as XQuery, XPath and XMLExists, and executed by the SQL/XML DBMS on the given instance data and the extracted transitive relationships data.
摘要翻译：一种通过从具有本地XML支持的DBMS中的给定本体数据中提取和存储传递/包含关系来在数据库管理系统（DBMS）中支持语义匹配查询的方法。这些传递关系被转换成一组XML文档，它们是传递关系的层次结构的自然映射。表函数构造以声明方式表达语义匹配查询。查询的语义匹配自动重写或转换为标准SQL / XML搜索运算符，如XQuery，XPath和XMLExists，并由SQL / XML DBMS在给定的实例数据和提取的传递关系数据上执行。

33. 发明申请

US20080183656A1 Query integrity assurance in database outsourcing 有权
标题翻译：查询数据库外包的完整性保证
公开(公告)号：US20080183656A1
公开(公告)日：2008-07-31
申请号：US11626847
申请日：2007-01-25
申请人： Chang-Shing Perng , Haixun Wang , Jian Yin , Philip S. Yu
发明人： Chang-Shing Perng , Haixun Wang , Jian Yin , Philip S. Yu
IPC分类号： G06F17/30
CPC分类号： G06F21/64 , G06F17/30286 , G06F21/6245 , G06F2221/2115
摘要： A method, system and computer program product for confirming the validity of data returned from a data store. A data store contains a primary data set encrypted using a first encryption and a secondary data set using a second encryption. The secondary data set is a subset of the primary data set. A client issues a substantive query against the data store to retrieve a primary data result belonging to the primary data set. A query interface issues at least one validating query against the data store. Each validating query returns a secondary data result belonging to the secondary data set. The query interface receives the secondary data result and provides a data invalid notification if data satisfying the substantive query included in an unencrypted form of the secondary data result is not contained in an unencrypted form of the primary data result.
摘要翻译：一种用于确认从数据存储返回的数据的有效性的方法，系统和计算机程序产品。数据存储包含使用第一加密加密的主数据集和使用第二加密的辅数据集。辅助数据集是主数据集的子集。客户端对数据存储器发出实质性查询以检索属于主数据集的主数据结果。查询界面对数据存储区发出至少一个验证查询。每个验证查询返回属于辅助数据集的辅助数据结果。如果满足辅助数据结果的未加密形式的实质性查询的数据未包含在主数据结果的未加密形式中，则查询接口接收辅助数据结果并提供数据无效通知。

34. 发明申请

US20080177813A1 METHOD FOR FAST RELEVANCE DISCOVERY IN TIME SERIES 有权
标题翻译：时间序列中快速相关发现的方法
公开(公告)号：US20080177813A1
公开(公告)日：2008-07-24
申请号：US11563900
申请日：2006-11-28
申请人： Haixun Wang , Chang-Shing Perng
发明人： Haixun Wang , Chang-Shing Perng
IPC分类号： G06F17/15
CPC分类号： G06K9/00536
摘要： A method for measuring time series relevance using state transition points, including inputting time series data and relevance threshold data. Then convert all time series values to ranks within [0,1] interval. Calculate the valid range of the transition point in [0,1]. Afterwards, a verification occurs that a time series Z exists for each pair of time series Z and Y, such that the relevances between X and Z, and between Y and Z are known. Then deduce the relevance of X and Y. The relevance of X and Y must be at least one of, (i) higher, and (ii) lower than, the given threshold. Provided Z is found terminate all remaining calculations for X and Y. Otherwise, segment the time series if no Z time series exists, use the segmented time series to estimate the relevance. Apply a hill climbing algorithm in the valid range to find the true relevance.
摘要翻译：一种使用状态转换点来测量时间序列相关性的方法，包括输入时间序列数据和相关阈值数据。然后将所有时间序列值转换为[0,1]间隔内的等级。计算[0,1]中转换点的有效范围。之后，对于每对时间序列Z和Y存在时间序列Z的验证，使得X和Z之间，以及Y和Z之间的相关性是已知的。然后推导X和Y的相关性.X和Y的相关性必须至少为（i）较高和（ii）低于给定阈值中的一个。如果Z被找到终止X和Y的所有剩余计算。否则，如果没有Z时间序列，则分段时间序列，使用分段时间序列来估计相关性。在有效范围内应用爬山算法来找到真正的相关性。

35. 发明申请

US20080133438A1 SYSTEM AND METHOD FOR FEATURE BASED LOAD SHEDDING IN CLASSIFICATION 审中-公开
标题翻译：基于特征的负载分类的系统和方法
公开(公告)号：US20080133438A1
公开(公告)日：2008-06-05
申请号：US11564885
申请日：2006-11-30
申请人： Charu C. Aggarwal , Haixun Wang
发明人： Charu C. Aggarwal , Haixun Wang
IPC分类号： G06N5/00
CPC分类号： G06N20/00
摘要： A system and method for feature based load shedding in classification. The system includes a plurality of data sources. The plurality of data sources being configured to render independent streams of input data, such data being selectively grouped together to form a particular classification task. The system further includes a central classification server configured to analyze and execute multiple tasks, each task consisting of multiple input data. The central classification server further configured to analyze the data for knowledge-based decision-making. The central classification server being communicatively engaged via a network to the plurality of data sources. The method includes rendering independent streams of input data, such data being selectively grouped together to form a particular task. The method further includes analyzing and handling multiple tasks, each task consisting of multiple input data. The method also includes analyzing the data for knowledge-based decision-making.
摘要翻译：一种分类中基于特征的负载脱落的系统和方法。该系统包括多个数据源。多个数据源被配置为呈现独立的输入数据流，这样的数据被选择性地分组在一起以形成特定的分类任务。该系统还包括配置成分析和执行多个任务的中央分类服务器，每个任务由多个输入数据组成。中央分类服务器还被配置为分析用于基于知识的决策的数据。中央分类服务器经由网络被通信地接合到多个数据源。该方法包括呈现独立的输入数据流，这样的数据被选择性地分组在一起以形成特定的任务。该方法还包括分析和处理多个任务，每个任务由多个输入数据组成。该方法还包括分析基于知识的决策的数据。

36. 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。

37. 发明申请

US20070230488A1 Space and time efficient XML graph labeling 失效
标题翻译：空间和时间有效的XML图形标注
公开(公告)号：US20070230488A1
公开(公告)日：2007-10-04
申请号：US11396502
申请日：2006-03-31
申请人： Philip Yu , Haixun Wang , Hao He
发明人： Philip Yu , Haixun Wang , Hao He
IPC分类号： H04L12/56
CPC分类号： H04L45/48 , H04L45/02
摘要： There is provided a method for determining reachability between any two nodes within a graph. The inventive method utilizes a dual-labeling scheme. Initially, a spanning tree is defined for a group of nodes within a graph. Each node in the spanning tree is assigned a unique interval-based label, that describes its dependency from an ancestor node. Non-tree labels are then assigned to each node in the spanning tree that is connected to another node in the spanning tree by a non-tree link. From these labels, reachability of any two nodes in the spanning tree is determined by using only the interval-based labels and the non-tree labels.
摘要翻译：提供了一种用于确定图中任何两个节点之间的可达性的方法。本发明的方法利用双标记方案。最初，为图中的一组节点定义了生成树。生成树中的每个节点都被分配一个唯一的基于间隔的标签，它描述了从祖先节点的依赖关系。然后，非树标签被分配给生成树中通过非树形链接连接到生成树中的另一个节点的每个节点。从这些标签中，生成树中任何两个节点的可达性通过仅使用基于间隔的标签和非树标签来确定。

38. 发明申请

US20060184527A1 System and method for load shedding in data mining and knowledge discovery from stream data 有权
公开(公告)号：US20060184527A1
公开(公告)日：2006-08-17
申请号：US11058944
申请日：2005-02-16
申请人： Yun Chi , Haixun Wang , Philip Yu
发明人： Yun Chi , Haixun Wang , Philip Yu
IPC分类号： H04L27/28
CPC分类号： G06K9/6297 , H04L43/028
摘要： Load shedding schemes for mining data streams. A scoring function is used to rank the importance of stream elements, and those elements with high importance are investigated. In the context of not knowing the exact feature values of a data stream, the use of a Markov model is proposed herein for predicting the feature distribution of a data stream. Based on the predicted feature distribution, one can make classification decisions to maximize the expected benefits. In addition, there is proposed herein the employment of a quality of decision (QoD) metric to measure the level of uncertainty in decisions and to guide load shedding. A load shedding scheme such as presented herein assigns available resources to multiple data streams to maximize the quality of classification decisions. Furthermore, such a load shedding scheme is able to learn and adapt to changing data characteristics in the data streams.

39. 发明申请

US20060010093A1 System and method for continuous diagnosis of data streams 失效
标题翻译：用于连续诊断数据流的系统和方法
公开(公告)号：US20060010093A1
公开(公告)日：2006-01-12
申请号：US10880913
申请日：2004-06-30
申请人： Wei Fan , Haixun Wang , Philip Yu
发明人： Wei Fan , Haixun Wang , Philip Yu
IPC分类号： G06F17/30
CPC分类号： G06F17/30017 , G06F2216/03 , Y10S707/99931 , Y10S707/99935
摘要： In connection with the mining of time-evolving data streams, a general framework that mines changes and reconstructs models from a data stream with unlabeled instances or a limited number of labeled instances. In particular, there are defined herein statistical profiling methods that extend a classification tree in order to guess the percentage of drifts in the data stream without any labelled data. Exact error can be estimated by actively sampling a small number of true labels. If the estimated error is significantly higher than empirical expectations, there preferably re-sampled a small number of true labels to reconstruct the decision tree from the leaf node level.
摘要翻译：与挖掘时间不断变化的数据流有关的一般框架，即从具有未标记实例的数据流或有限数量的标记实例中挖掘变更和重建模型。特别地，这里定义了扩展分类树的统计分析方法，以便在没有任何标记数据的情况下猜测数据流中漂移的百分比。可以通过主动抽取少量真实标签来估计精确误差。如果估计的误差明显高于经验期望值，则最好重新采样少量的真实标签，以从叶节点级别重建决策树。

40. 发明授权

US10176245B2 Semantic query by example 有权
公开(公告)号：US10176245B2
公开(公告)日：2019-01-08
申请号：US12566882
申请日：2009-09-25
申请人： Lipyeow Lim , Haixun Wang , Min Wang
发明人： Lipyeow Lim , Haixun Wang , Min Wang
IPC分类号： G06F17/00 , G06F17/30
摘要： A computer-implemented method, system, and computer program product for producing a semantic query by example are provided. The method includes receiving examples of potential results from querying a database table with an associated ontology, and extracting features from the database table and the examples based on the associated ontology. The method further includes training a classifier based on the examples and the extracted features, and applying the classifier to the database table to obtain a semantic query result. The method also includes outputting the semantic query result to a user interface, and requesting user feedback of satisfaction with the semantic query result. The method additionally includes updating the classifier and the semantic query result iteratively in response to the user feedback.

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