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公开(公告)号：US20090210418A1
公开(公告)日：2009-08-20
申请号：US12031715
申请日：2008-02-15
IPC分类号： G06F17/30
CPC分类号： G06F17/30569 , G06F17/30675 , G06F17/30985
摘要： A transformation-based record matching technique. The technique provides a flexible way to account for synonyms and more general forms of string equivalences when performing record matching by taking as explicit input user-defined transformation rules (such as, for example, the fact that “Robert” and “Bob” that are synonymous). The input string and user-defined transformation rules are used to generate a larger set of strings which are used when performing record matching. Both the input string and data elements in a database can be transformed using the user-defined transformation rules in order to generate a larger set of potential record matches. These potential record matches can then be subjected to a threshold test in order to determine one or more best matches. Additionally, signature-based similarity functions are used to improve the computational efficiency of the technique.
摘要翻译： 基于变换的记录匹配技术。 当通过采用显式输入用户定义的转换规则（例如，“Robert”和“Bob”）这样的事实来执行记录匹配时，该技术提供了一种灵活的方式来解释同义词和更一般的字符串等同形式 同义词）。 输入字符串和用户定义的转换规则用于生成在执行记录匹配时使用的较大的一组字符串。 可以使用用户定义的变换规则来转换数据库中的输入字符串和数据元素，以便生成更大的潜在记录匹配集合。 然后可以对这些潜在的记录匹配进行阈值测试，以确定一个或多个最佳匹配。 另外，使用基于签名的相似度函数来提高该技术的计算效率。
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公开(公告)号：US08249336B2
公开(公告)日：2012-08-21
申请号：US12492311
申请日：2009-08-14
IPC分类号： G06K9/00
CPC分类号： G06F17/2765
摘要： Techniques are described to leverage a set of sample or example matched pairs of strings to learn string transformation rules, which may be used to match data records that are semantically equivalent. In one embodiment, matched pairs of input strings are accessed. For a set of matched pairs, a set of one or more string transformation rules are learned. A transformation rule may include two strings determined to be semantically equivalent. The transformation rules are used to determine whether a first and second string match each other.
摘要翻译： 描述技术来利用一组样本或示例匹配的字符串对来学习字符串转换规则，其可以用于匹配语义等同的数据记录。 在一个实施例中，访问匹配的输入串对。 对于一组匹配的对，学习一组或多个字符串转换规则。 转换规则可以包括确定为在语义上相等的两个字符串。 变换规则用于确定第一个和第二个字符串是否彼此匹配。
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公开(公告)号：US20110038531A1
公开(公告)日：2011-02-17
申请号：US12492311
申请日：2009-08-14
IPC分类号： G06K9/62
CPC分类号： G06F17/2765
摘要： Techniques are described to leverage a set of sample or example matched pairs of strings to learn string transformation rules, which may be used to match data records that are semantically equivalent. In one embodiment, matched pairs of input strings are accessed. For a set of matched pairs, a set of one or more string transformation rules are learned. A transformation rule may include two strings determined to be semantically equivalent. The transformation rules are used to determine whether a first and second string match each other.
摘要翻译： 描述技术来利用一组样本或示例匹配的字符串对来学习字符串转换规则，其可以用于匹配语义等同的数据记录。 在一个实施例中，访问匹配的输入串对。 对于一组匹配的对，学习一组或多个字符串转换规则。 转换规则可以包括确定为在语义上相等的两个字符串。 变换规则用于确定第一个和第二个字符串是否彼此匹配。
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公开(公告)号：US07610283B2
公开(公告)日：2009-10-27
申请号：US11761425
申请日：2007-06-12
CPC分类号： G06F17/30312 , Y10S707/99931 , Y10S707/99932 , Y10S707/99933 , Y10S707/99935 , Y10S707/99937
摘要： Input set indexing for set-similarity lookups. The architecture provides input to an indexing process that enables more efficient lookups for large data sets (e.g., disk-based) without requiring a full scan of the input. A new index structure is provided, the output of which is exact, rather than approximate. The similarity of two sets is specified using a similarity function that maps two sets to a numeric value that represents similarity of the two sets. Threshold-based lookups are addressed where two sets are considered similar if the numeric similarity score is above a threshold. The structure efficiently identifies all input sets within a distance k (e.g., a hamming distance) of the query set. Additional information in the form of frequency of elements (the number of input sets in which an element occurs) is used to improve index performance.
摘要翻译： 用于集合相似性查找的输入集索引。 该体系结构为索引过程提供输入，可以对大数据集（例如，基于磁盘）进行更有效的查找，而无需对输入进行全面扫描。 提供了一个新的索引结构，其输出是精确的，而不是近似的。 使用将两组映射到表示两组相似度的数值的相似度函数来指定两组的相似度。 如果数字相似性分数高于阈值，则基于阈值的查找被解决为其中两个集合被认为是相似的。 该结构有效地识别查询集合的距离k（例如，汉明距离）内的所有输入集合。 使用元素频率（元素发生的输入集合的数量）的形式的附加信息用于提高索引性能。
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公开(公告)号：US09081817B2
公开(公告)日：2015-07-14
申请号：US13084527
申请日：2011-04-11
申请人： Arvind Arasu , Michaela Götz , Shriraghav Kaushik
发明人： Arvind Arasu , Michaela Götz , Shriraghav Kaushik
CPC分类号： G06F17/30507 , G06N99/005
摘要： An active learning record matching system and method for producing a record matching package that is used to identify pairs of duplicate records. Embodiments of the system and method allow a precision threshold to be specified and then generate a learned record matching package having precision greater than this threshold and a recall close to the best possible recall. Embodiments of the system and method use a blocking technique to restrict the space of record matching packages considered and scale to large inputs. The learning method considers several record matching packages, estimates the precision and recall of the packages, and identifies the package with maximum recall having precision greater than equal to the given precision threshold. A human domain expert labels a sample of record pairs in the output of the package as matches or non-matches and this labeling is used to estimate the precision of the package.
摘要翻译： 用于产生用于识别重复记录对的记录匹配包的主动学习记录匹配系统和方法。 系统和方法的实施例允许指定精度阈值，然后产生具有大于该阈值的精度的学习记录匹配包以及接近最佳可能召回的召回。 系统和方法的实施例使用阻塞技术来限制所考虑的记录匹配包的空间并将其缩放到大的输入。 学习方法考虑了几个记录匹配包，估计包的精度和调用，并且识别具有大于等于给定精度阈值的精度的最大召回的包。 人类领域专家将包的输出中的记录对的样本标记为匹配或不匹配，并且该标签用于估计包的精度。
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公开(公告)号：US20120259802A1
公开(公告)日：2012-10-11
申请号：US13084527
申请日：2011-04-11
申请人： Arvind Arasu , Michaela Götz , Shriraghav Kaushik
发明人： Arvind Arasu , Michaela Götz , Shriraghav Kaushik
IPC分类号： G06F15/18
CPC分类号： G06F17/30507 , G06N99/005
摘要： An active learning record matching system and method for producing a record matching package that is used to identify pairs of duplicate records. Embodiments of the system and method allow a precision threshold to be specified and then generate a learned record matching package having precision greater than this threshold and a recall close to the best possible recall. Embodiments of the system and method use a blocking technique to restrict the space of record matching packages considered and scale to large inputs. The learning method considers several record matching packages, estimates the precision and recall of the packages, and identifies the package with maximum recall having precision greater than equal to the given precision threshold. A human domain expert labels a sample of record pairs in the output of the package as matches or non-matches and this labeling is used to estimate the precision of the package.
摘要翻译： 用于产生用于识别重复记录对的记录匹配包的主动学习记录匹配系统和方法。 系统和方法的实施例允许指定精度阈值，然后产生具有大于该阈值的精度的学习记录匹配包以及接近最佳可能召回的召回。 系统和方法的实施例使用阻塞技术来限制所考虑的记录匹配包的空间并将其缩放到大的输入。 学习方法考虑了几个记录匹配包，估计包的精度和调用，并且识别具有大于等于给定精度阈值的精度的最大召回的包。 人类领域专家将包的输出中的记录对的样本标记为匹配或不匹配，并且该标签用于估计包的精度。
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公开(公告)号：US20080313128A1
公开(公告)日：2008-12-18
申请号：US11761425
申请日：2007-06-12
CPC分类号： G06F17/30312 , Y10S707/99931 , Y10S707/99932 , Y10S707/99933 , Y10S707/99935 , Y10S707/99937
摘要： Input set indexing for set-similarity lookups. The architecture provides input to an indexing process that enables more efficient lookups for large data sets (e.g., disk-based) without requiring a full scan of the input. A new index structure is provided, the output of which is exact, rather than approximate. The similarity of two sets is specified using a similarity function that maps two sets to a numeric value that represents similarity of the two sets. Threshold-based lookups are addressed where two sets are considered similar if the numeric similarity score is above a threshold. The structure efficiently identifies all input sets within a distance k (e.g., a hamming distance) of the query set. Additional information in the form of frequency of elements (the number of input sets in which an element occurs) is used to improve index performance.
摘要翻译： 用于集合相似性查找的输入集索引。 该体系结构为索引过程提供输入，可以对大数据集（例如，基于磁盘）进行更有效的查找，而无需对输入进行全面扫描。 提供了一个新的索引结构，其输出是精确的，而不是近似的。 使用将两组映射到表示两组相似度的数值的相似度函数来指定两组的相似度。 如果数字相似性分数高于阈值，则基于阈值的查找被解决为其中两个集合被认为是相似的。 该结构有效地识别查询集合的距离k（例如，汉明距离）内的所有输入集合。 使用元素频率（元素发生的输入集合的数量）的形式的附加信息用于提高索引性能。
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公开(公告)号：US09317544B2
公开(公告)日：2016-04-19
申请号：US13253315
申请日：2011-10-05
CPC分类号： G06F17/30303 , G06F17/30533
摘要： A fuzzy joins system that is integrated in a database system generates fuzzy joins between records from two datasets. The fuzzy joins system includes a tokenizer to generate tokens for data records and a transformer to find transforms for the tokens. The fuzzy joins system invokes a signature generator, running within a runtime layer of the database system, to generate signatures for data records based on the tokens and their transforms. Subsequently, an equi-join operation joins the records from the two datasets with at least one equal signature. A similarity calculator, running within a runtime layer of the database system, computes a similarity measure using the token information of the joined records. If the similarity measure for any two records is above a threshold, the fuzzy joins system generates a fuzzy join between such two records.
摘要翻译： 集成在数据库系统中的模糊连接系统在两个数据集的记录之间生成模糊连接。 模糊连接系统包括一个用于生成数据记录令牌的标记器和一个用于为令牌找到变换的变压器。 模糊连接系统调用在数据库系统的运行时层内运行的签名生成器，以基于令牌及其变换生成用于数据记录的签名。 随后，等连接操作将来自两个数据集的记录与至少一个相等的签名相连。 在数据库系统的运行时层内运行的相似度计算器使用所连接的记录的令牌信息来计算相似性度量。 如果任何两个记录的相似性度量高于阈值，则模糊连接系统在这两个记录之间生成模糊连接。
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公开(公告)号：US20130091120A1
公开(公告)日：2013-04-11
申请号：US13253315
申请日：2011-10-05
IPC分类号： G06F17/30
CPC分类号： G06F17/30303 , G06F17/30533
摘要： A fuzzy joins system that is integrated in a database system generates fuzzy joins between records from two datasets. The fuzzy joins system includes a tokenizer to generate tokens for data records and a transformer to find transforms for the tokens. The fuzzy joins system invokes a signature generator, running within a runtime layer of the database system, to generate signatures for data records based on the tokens and their transforms. Subsequently, an equi-join operation joins the records from the two datasets with at least one equal signature. A similarity calculator, running within a runtime layer of the database system, computes a similarity measure using the token information of the joined records. If the similarity measure for any two records is above a threshold, the fuzzy joins system generates a fuzzy join between such two records.
摘要翻译： 集成在数据库系统中的模糊连接系统在两个数据集的记录之间生成模糊连接。 模糊连接系统包括一个用于生成数据记录令牌的标记器和一个用于为令牌找到变换的变压器。 模糊连接系统调用在数据库系统的运行时层内运行的签名生成器，以基于令牌及其转换生成用于数据记录的签名。 随后，等连接操作将来自两个数据集的记录与至少一个相等的签名相连。 在数据库系统的运行时层内运行的相似度计算器使用所连接的记录的令牌信息来计算相似性度量。 如果任何两个记录的相似性度量高于阈值，则模糊连接系统在这两个记录之间生成模糊连接。
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公开(公告)号：US08032546B2
公开(公告)日：2011-10-04
申请号：US12031715
申请日：2008-02-15
申请人： Arvind Arasu , Surajit Chaudhuri
发明人： Arvind Arasu , Surajit Chaudhuri
CPC分类号： G06F17/30569 , G06F17/30675 , G06F17/30985
摘要： A transformation-based record matching technique. The technique provides a flexible way to account for synonyms and more general forms of string equivalences when performing record matching by taking as explicit input user-defined transformation rules (such as, for example, the fact that “Robert” and “Bob” that are synonymous). The input string and user-defined transformation rules are used to generate a larger set of strings which are used when performing record matching. Both the input string and data elements in a database can be transformed using the user-defined transformation rules in order to generate a larger set of potential record matches. These potential record matches can then be subjected to a threshold test in order to determine one or more best matches. Additionally, signature-based similarity functions are used to improve the computational efficiency of the technique.
摘要翻译： 基于变换的记录匹配技术。 当通过采用显式输入用户定义的转换规则（例如，“Robert”和“Bob”）这样的事实来执行记录匹配时，该技术提供了一种灵活的方式来解释同义词和更一般的字符串等同形式 同义词）。 输入字符串和用户定义的转换规则用于生成在执行记录匹配时使用的较大的一组字符串。 可以使用用户定义的变换规则来转换数据库中的输入字符串和数据元素，以便生成更大的潜在记录匹配集合。 然后可以对这些潜在的记录匹配进行阈值测试，以确定一个或多个最佳匹配。 另外，使用基于签名的相似度函数来提高该技术的计算效率。
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