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公开(公告)号：US20080306945A1
公开(公告)日：2008-12-11
申请号：US11758202
申请日：2007-06-05
IPC分类号： G06F17/30
CPC分类号： G06F17/30533 , G06F17/30495
摘要： Example-driven creation of record matching queries. The disclosed architecture employs techniques that exploit the availability of positive (or matching) and negative (non-matching) examples to search through this space and suggest an initial record matching query. The record matching task is modeled as that of designing an operator tree obtained by composing a few primitive operators. This ensures that record matching programs be executable efficiently and scalably over large input relations. The architecture joins records across multiple (e.g., two) relations (e.g., R and S). The architecture exploits the monotonicity property of similarity functions for record matching in the relations, in that, any pair of matching records have a higher similarity value than non-matching record pairs on at least one similarity function.
摘要翻译： 示例驱动创建记录匹配查询。 所公开的架构采用利用正（或匹配）和否定（不匹配）示例的可用性来搜索该空间并提出初始记录匹配查询的技术。 记录匹配任务被建模为设计通过组合几个原始算子获得的运算符树的记录匹配任务。 这确保了记录匹配程序可以在大的输入关系上有效和可扩展地执行。 该架构通过多个（例如，两个）关系（例如，R和S）连接记录。 该架构利用了关系中记录匹配的相似度函数的单调性，因为任何一对匹配记录具有比至少一个相似度函数上的非匹配记录对更高的相似度值。
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公开(公告)号：US08204866B2
公开(公告)日：2012-06-19
申请号：US11804400
申请日：2007-05-18
IPC分类号： G06F17/30
CPC分类号： G06F17/30489
摘要： A deduplication algorithm that provides improved accuracy in data deduplication by using aggregate and/or groupwise constraints. Deduplication is accomplished using only as many of these constraints that are satisfied rather than be imposed inflexibly as hard constraints. Additionally, textual similarity between tuples is leveraged to restrict the search space. The algorithm begins with a coarse initial partition of data records and continues by raising the similarity threshold until the threshold splits a given partition. This sequence of splits defines a rich space of alternatives. Over this space, an algorithm finds a partition of the input that maximizes constraint satisfaction. In the context of groupwise aggregation constraints for deduplication all SQL (structured query language) aggregates are allowed, including summation.
摘要翻译： 重复数据删除算法，通过使用聚合和/或分组约束来提高重复数据删除的精度。 重复数据删除使用只有这些约束满足的约束才能实现，而不是将其作为硬约束条件强制强加。 此外，利用元组之间的文本相似性来限制搜索空间。 该算法以数据记录的粗略初始分区开始，并通过提高相似性阈值继续，直到阈值分裂给定分区。 这个拆分序列定义了丰富的替代空间。 在这个空间上，一个算法找到了一个最大化约束满足度的输入分区。 在重复数据消除的分组聚合约束的上下文中，允许所有SQL（结构化查询语言）聚合，包括求和。
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公开(公告)号：US08046339B2
公开(公告)日：2011-10-25
申请号：US11758202
申请日：2007-06-05
IPC分类号： G06F17/30
CPC分类号： G06F17/30533 , G06F17/30495
摘要： Example-driven creation of record matching queries. The disclosed architecture employs techniques that exploit the availability of positive (or matching) and negative (non-matching) examples to search through this space and suggest an initial record matching query. The record matching task is modeled as that of designing an operator tree obtained by composing a few primitive operators. This ensures that record matching programs be executable efficiently and scalably over large input relations. The architecture joins records across multiple (e.g., two) relations (e.g., R and S). The architecture exploits the monotonicity property of similarity functions for record matching in the relations, in that, any pair of matching records have a higher similarity value than non-matching record pairs on at least one similarity function.
摘要翻译： 示例驱动创建记录匹配查询。 所公开的架构采用利用正（或匹配）和否定（不匹配）示例的可用性来搜索该空间并提出初始记录匹配查询的技术。 记录匹配任务被建模为设计通过组合几个原始算子获得的运算符树的记录匹配任务。 这确保了记录匹配程序可以在大的输入关系上有效和可扩展地执行。 该架构通过多个（例如，两个）关系（例如，R和S）连接记录。 该架构利用了关系中记录匹配的相似度函数的单调性，因为任何一对匹配记录具有比至少一个相似度函数上的非匹配记录对更高的相似度值。
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公开(公告)号：US20080288482A1
公开(公告)日：2008-11-20
申请号：US11804400
申请日：2007-05-18
IPC分类号： G06F17/30
CPC分类号： G06F17/30489
摘要： A deduplication algorithm that provides improved accuracy in data deduplication by using aggregate and/or groupwise constraints. Deduplication is accomplished using only as many of these constraints that are satisfied rather than be imposed inflexibly as hard constraints. Additionally, textual similarity between tuples is leveraged to restrict the search space. The algorithm begins with a coarse initial partition of data records and continues by raising the similarity threshold until the threshold splits a given partition. This sequence of splits defines a rich space of alternatives. Over this space, an algorithm finds a partition of the input that maximizes constraint satisfaction. In the context of groupwise aggregation constraints for deduplication all SQL (structured query language) aggregates are allowed, including summation.
摘要翻译： 重复数据删除算法，通过使用聚合和/或分组约束来提高重复数据删除的精度。 重复数据删除使用只有这些约束满足的约束才能实现，而不是将其作为硬约束条件强制强加。 此外，利用元组之间的文本相似性来限制搜索空间。 该算法以数据记录的粗略初始分区开始，并通过提高相似性阈值继续，直到阈值分裂给定分区。 这个拆分序列定义了丰富的替代空间。 在这个空间上，一个算法找到了一个最大化约束满足度的输入分区。 在重复数据消除的分组聚合约束的上下文中，允许所有SQL（结构化查询语言）聚合，包括求和。
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公开(公告)号：US20090006392A1
公开(公告)日：2009-01-01
申请号：US11769050
申请日：2007-06-27
CPC分类号： G06F17/30536
摘要： Architecture that provides a data profile computation technique which employs key profile computation and data pattern profile computation. Key profile computation in a data table includes both exact keys as well as approximate keys, and is based on key strengths. A key strength of 100% is an exact key, and any other percentage in an approximate key. The key strength is estimated based on the number of table rows that have duplicated attribute values. Only column sets that exceed a threshold value are returned. Pattern profiling identifies a small set of regular expression patterns which best describe the patterns within a given set of attribute values. Pattern profiling includes three phases: a first phases for determining token regular expressions, a second phase for determining candidate regular expressions, and a third phase for identifying the best regular expressions of the candidates that match the attribute values.
摘要翻译： 提供采用关键轮廓计算和数据模式轮廓计算的数据轮廓计算技术的架构。 数据表中的关键轮廓计算包括精密键和近似键，并且基于关键优点。 100％的关键优势是一个确切的关键，其中一个关键的任何其他百分比。 基于具有重复的属性值的表行的数量来估计关键强度。 只返回超过阈值的列集。 模式分析标识一组最佳描述一组给定属性值中的模式的正则表达式模式。 模式分析包括三个阶段：用于确定令牌正则表达式的第一阶段，用于确定候选正则表达式的第二阶段，以及用于识别与属性值匹配的候选的最佳正则表达式的第三阶段。
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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：US07720883B2
公开(公告)日：2010-05-18
申请号：US11769050
申请日：2007-06-27
CPC分类号： G06F17/30536
摘要： Architecture that provides a data profile computation technique which employs key profile computation and data pattern profile computation. Key profile computation in a data table includes both exact keys as well as approximate keys, and is based on key strengths. A key strength of 100% is an exact key, and any other percentage in an approximate key. The key strength is estimated based on the number of table rows that have duplicated attribute values. Only column sets that exceed a threshold value are returned. Pattern profiling identifies a small set of regular expression patterns which best describe the patterns within a given set of attribute values. Pattern profiling includes three phases: a first phases for determining token regular expressions, a second phase for determining candidate regular expressions, and a third phase for identifying the best regular expressions of the candidates that match the attribute values.
摘要翻译： 提供采用关键轮廓计算和数据模式轮廓计算的数据轮廓计算技术的架构。 数据表中的关键轮廓计算包括精密键和近似键，并且基于关键优点。 100％的关键优势是一个确切的关键，其中一个关键的任何其他百分比。 基于具有重复的属性值的表行的数量来估计关键强度。 只返回超过阈值的列集。 模式分析标识一组最佳描述一组给定属性值中的模式的正则表达式模式。 模式分析包括三个阶段：用于确定令牌正则表达式的第一阶段，用于确定候选正则表达式的第二阶段，以及用于识别与属性值匹配的候选的最佳正则表达式的第三阶段。
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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：US20100299367A1
公开(公告)日：2010-11-25
申请号：US12469399
申请日：2009-05-20
IPC分类号： G06F17/30
CPC分类号： G06F16/24578 , G06F16/245 , G06F16/24535 , G06F16/24539 , G06F16/248 , G06F16/43
摘要： A keyword search is executed on a view of a database based on a Boolean keyword query. The view includes multiple text columns, and the keyword search is executed on each of the multiple text columns in the view. The output results from the keyword search on each of the text columns include tuple identifiers of one or more relevant tuples and a relevancy score for ranking the results of the keyword query.
摘要翻译： 在基于布尔关键字查询的数据库视图上执行关键字搜索。 该视图包括多个文本列，并且在视图中的每个多个文本列上执行关键字搜索。 每个文本列上的关键字搜索的输出结果包括一个或多个相关元组的元组标识符和用于对关键字查询的结果进行排名的相关分数。
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公开(公告)号：US07516149B2
公开(公告)日：2009-04-07
申请号：US10929514
申请日：2004-08-30
CPC分类号： G06F17/30303 , Y10S707/99932 , Y10S707/99933 , Y10S707/99937 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： At least one implementation, described herein, detects fuzzy duplicates and eliminates such duplicates. Fuzzy duplicates are multiple, seemingly distinct tuples (i.e., records) in a database that represent the same real-world entity or phenomenon.
摘要翻译： 本文描述的至少一个实施例检测模糊重复并消除这种重复。 模糊重复是代表相同的真实世界实体或现象的数据库中的多个看似独特的元组（即，记录）。
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