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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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公开(公告)号：US20060053129A1
公开(公告)日：2006-03-09
申请号：US10929514
申请日：2004-08-30
IPC分类号： G06F7/00
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.
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公开(公告)号：US07296011B2
公开(公告)日：2007-11-13
申请号：US10600083
申请日：2003-06-20
CPC分类号： G06F17/30542 , G06F17/30303 , Y10S707/99933
摘要： To help ensure high data quality, data warehouses validate and clean, if needed incoming data tuples from external sources. In many situations, input tuples or portions of input tuples must match acceptable tuples in a reference table. For example, product name and description fields in a sales record from a distributor must match the pre-recorded name and description fields in a product reference relation. A disclosed system implements an efficient and accurate approximate or fuzzy match operation that can effectively clean an incoming tuple if it fails to match exactly with any of the multiple tuples in the reference relation. A disclosed similarity function that utilizes token substrings referred to as q-grams overcomes limitations of prior art similarity functions while efficiently performing a fuzzy match process.
摘要翻译： 为了帮助确保高数据质量，数据仓库验证和清理，如果需要外部来源的传入数据元组。 在许多情况下，输入元组或输入元组的一部分必须匹配参考表中可接受的元组。 例如，分销商的销售记录中的产品名称和描述字段必须与产品参考关系中的预先记录的名称和描述字段相匹配。 所公开的系统实现有效和准确的近似或模糊匹配操作，其可以有效地清除传入元组，如果它不能与参考关系中的任何多个元组完全匹配。 使用称为q-gram的令牌子串的公开的相似度函数克服了现有技术相似度功能的限制，同时有效地执行模糊匹配过程。
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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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5.发明申请公开(公告)号：US20090319500A1
公开(公告)日：2009-12-24
申请号：US12144675
申请日：2008-06-24
CPC分类号： G06F17/30011 , G06F17/278
摘要： A set of documents is filtered for entity extraction. A list of entity strings is received. A set of token sets that covers the entity strings in the list is determined. An inverted index generated on a first set of documents is queried using the set of token sets to determine a set of document identifiers for a subset of the documents in the first set. A second set of documents identified by the set of document identifiers is retrieved from the first set of documents. The second set of documents is filtered to include one or more documents of the second set that each includes a match with at least one entity string of the list of entity strings. Entity recognition may be performed on the filtered second set of documents.
摘要翻译： 过滤一组文档进行实体提取。 接收到实体字符串的列表。 确定一组涵盖列表中的实体字符串的令牌集。 使用该组令牌查询在第一组文档上生成的反向索引，以确定第一组中的文档的子集的一组文档标识符。 从第一组文档中检索由该组文档标识符标识的第二组文档。 第二组文档被过滤以包括第二组的一个或多个文档，每个文档包括与实体字符串列表的至少一个实体字符串的匹配。 可以对经过滤的第二组文件执行实体识别。
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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：US07406479B2
公开(公告)日：2008-07-29
申请号：US11352141
申请日：2006-02-10
IPC分类号： G06F17/00
CPC分类号： G06F17/30442 , Y10S707/99942 , Y10S707/99943
摘要： A set similarity join system and method are provided. The system can be employed to facilitate data cleaning based on similarities through the identification of “close” tuples (e.g., records and/or rows). “Closeness” can be is evaluated using a similarity function(s) chosen to suit the domain and/or application. Thus, the system facilitates generic domain-independent data cleansing.The system can be employed with a foundational primitive, the set similarity join (SSJoin) operator, which can be used as a building block to implement a broad variety of notions of similarity (e.g., edit similarity, Jaccard similarity, generalized edit similarity, hamming distance, soundex, etc.) as well as similarity based on co-occurrences. The SSJoin operator can exploit the observation that set overlap can be used effectively to support a variety of similarity functions. The SSJoin operator compares values based on “sets” associated with (or explicitly constructed for) each one of them.
摘要翻译： 提供了一种集合相似性连接系统和方法。 可以通过识别“关闭”元组（例如，记录和/或行）来基于相似性来促进系统的数据清理。 可以使用选择适合域和/或应用程序的相似性函数来评估“接近度”。 因此，该系统便于通用的域无关数据清理。 该系统可以与基本原语，即相似性连接（SSJoin）运算符一起使用，其可以用作构建块来实现各种各样的相似性概念（例如，编辑相似性，Jaccard相似性，广义编辑相似性，汉明 距离，声音等）以及基于共同出现的相似性。 SSJoin算子可以利用设置重叠的观察结果有效地用于支持各种相似度函数。 SSJoin操作符根据与其中每一个相关联（或明确构建的）的“集合”来比较值。
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公开(公告)号：US07287019B2
公开(公告)日：2007-10-23
申请号：US10453992
申请日：2003-06-04
申请人： Rahul Kapoor , Venkatesh Ganti , Surajit Chaudhuri
发明人： Rahul Kapoor , Venkatesh Ganti , Surajit Chaudhuri
IPC分类号： G06F17/30
CPC分类号： G06F17/30303 , G06F2216/03 , Y10S707/99932 , Y10S707/99933 , Y10S707/99934 , Y10S707/99935 , Y10S707/99936
摘要： A process for finding a similar data records from a set of data records. A database table or tables provide a number of data records from which one or more canonical data records are identified. Tokens are identified within the data records and classified according to attribute field. A similarity score is assigned to data records in relation to other data records based on a similarity between tokens of the data records. Data records whose similarity score with respect to each other is greater than a threshold form one or more groups of data records. The records or tuples form nodes of a graph wherein edges between nodes represent a similarity score between records of a group. Within each group a canonical record is identified based on the similarity of data records to each other within the group.
摘要翻译： 从一组数据记录中查找类似数据记录的过程。 数据库表或表提供了一些数据记录，从中可以识别一个或多个规范数据记录。 在数据记录中识别令牌，并根据属性字段进行分类。 基于数据记录的令牌之间的相似度，将相似性得分分配给与其他数据记录有关的数据记录。 其相似度相对于彼此的数据记录大于阈值形成一组或多组数据记录。 记录或元组形成图的节点，其中节点之间的边表示组之间的相似性得分。 在每个组内，基于数据记录在组内的彼此的相似性来识别规范记录。
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公开(公告)号：US20060282436A1
公开(公告)日：2006-12-14
申请号：US11123901
申请日：2005-05-06
IPC分类号： G06F7/00
CPC分类号： G06F17/30536 , Y10S707/99932
摘要： A system that facilitates estimating functional relationships associated with one or more columns in a database comprises a sampling component that receives a random sample of records within the database. An estimate generator component calculates an estimate of strength of functional relationships based at least in part upon the received samples. For example, the estimate generator component can calculate an estimate of strength of a column as a key column based at least in part upon the received samples.
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公开(公告)号：US07149735B2
公开(公告)日：2006-12-12
申请号：US10603035
申请日：2003-06-24
申请人： Surajit Chaudhuri , Venkatesh Ganti , Luis Gravano
发明人： Surajit Chaudhuri , Venkatesh Ganti , Luis Gravano
IPC分类号： G06F17/30
CPC分类号： G06F17/30985 , Y10S707/99936
摘要： A method of estimating selectivity of a given string predicate in a database query. In the method selectivities of substrings of various substring lengths are estimated. For example, the selectivity of substrings between length l (or some constant q) to the length of the given string predicate may be estimated. The method then selects a candidate sub string for each sub string length based on estimated selectivities of the substrings. The estimated selectivities of the candidate substrings are combined. The combined estimated selectivity of the candidate substrings is returned as the estimated selectivity of the given string predicate.
摘要翻译： 在数据库查询中估计给定字符串谓词的选择性的方法。 在方法中，估计各种子串长度的子串的选择性。 例如，可以估计长度l（或一些常数q）与给定字符串谓词的长度之间的子串的选择性。 然后，该方法基于所估计的子串的选择性来选择每个子串长度的候选子串。 合并候选子串的估计选择性。 候选子串的组合估计选择性作为给定字符串谓词的估计选择性返回。
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