公开(公告)号：US07346601B2

公开(公告)日：2008-03-18

申请号：US10161308

申请日：2002-06-03

申请人： Surajit Chaudhuri ,  Vivek Narasayya ,  Sunita Sarawagi

发明人： Surajit Chaudhuri ,  Vivek Narasayya ,  Sunita Sarawagi

IPC分类号： G06F17/30

CPC分类号： G06F17/30598 ,  G06F2216/03 ,  Y10S707/99932 ,  Y10S707/99934

摘要： A method for evaluating a user query on a database having a mining model that classifies records contained in the database into classes when the query comprises at least one mining predicate that refers to a class of database records. An upper envelope is derived for the class referred to by the mining predicate corresponding to a query that returns a set of database records that includes all of the database records belonging to the class. The upper envelope is included in the user query for query evaluation. The method may be practiced during a preprocessing phase by evaluating the mining model to extract a set of classes of the database records and deriving an upper envelope for each class. These upper envelopes are stored for access during user query evaluation.

摘要翻译： 一种用于评估具有挖掘模型的数据库上的用户查询的方法，所述挖掘模型将所述数据库中包含的记录分类为类，所述查询包括至少一个引用数据库记录类的挖掘谓词。 对于与返回一组包含属于该类的所有数据库记录的数据库记录的查询相对应的挖掘谓词引用的类，派生上层信封。 用于查询评估的用户查询中包含上部信封。 该方法可以在预处理阶段期间通过评估挖掘模型来提取数据库记录的一组类别并为每个类别导出上部包络来实现。 这些上部信封在用户查询评估期间被存储以供访问。

公开(公告)号：US06691098B1

公开(公告)日：2004-02-10

申请号：US09500263

申请日：2000-02-08

申请人： Rakesh Agrawal ,  Sunita Sarawagi

发明人： Rakesh Agrawal ,  Sunita Sarawagi

IPC分类号： G06F1730

CPC分类号： G06F17/30592 ,  G06F17/30572 ,  Y10S707/99931 ,  Y10S707/99945

摘要： A system and method for explaining why an exceptional element in a multidimensional database is exceptional by presenting the element using at least two of the dimensions responsible for the exception. Maximal terms are identified in the monolithic equation that is used to identify exceptions, and based on the maximal terms the dimensions that are to be displayed are selected as a visual indication of why a displayed element is exceptional.

摘要翻译： 一种用于解释为什么通过使用负责异常的维度中的至少两个来呈现元素来为多维数据库中的特殊元素而异常的系统和方法。 最大项在用于识别异常的单片方程中被识别，并且基于最大项，将选择要显示的尺寸作为显示元素为例的可视指示。

公开(公告)号：US5832475A

公开(公告)日：1998-11-03

申请号：US624283

申请日：1996-03-29

申请人： Rakesh Agrawal ,  Ashish Gupta ,  Sunita Sarawagi

发明人： Rakesh Agrawal ,  Ashish Gupta ,  Sunita Sarawagi

IPC分类号： G06F17/30

CPC分类号： G06F17/30489 ,  Y10S707/99931 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99934 ,  Y10S707/99937

摘要： Disclosed is a system and method for performing database queries including GROUP-BY operations, in which aggregate values for attributes are desired for distinct, partitioned subsets of tuples satisfying a query. A special case of the aggregation problem is addressed, employing a structure, called the data cube operator, which provides information useful for expediting execution of GROUP-BY operations in queries. Algorithms are provided for constructing the data cube by efficiently computing a collection of GROUP-BYs on the attributes of the relation. Decision support systems often require computation of multiple GROUP-BY operations on a given set of attributes, the GROUP-BYs being related in the sense that their attributes are subsets or supersets of each other. The invention extends hash-based and sort-based grouping methods with optimizations, including combining common operations across multiple GROUP-BYs and using pre-computed GROUP-BYs for computing other GROUP-BYs. An extension of the cube algorithms handles any given collection of aggregates.

摘要翻译： 公开了一种用于执行包括GROUP-BY操作的数据库查询的系统和方法，其中期望满足查询的元组的不同分配子集的属性的聚合值。 解决聚合问题的一个特殊情况，采用称为数据多维数据集运算符的结构，该结构提供了有助于在查询中加速执行GROUP-BY操作的信息。 提供了通过有效地计算关系的属性的GROUP-BY集合来构造数据立方体的算法。 决策支持系统通常需要对给定的一组属性计算多个GROUP-BY操作，GROUP-BY在其属性是彼此的子集或超集的意义上相关。 本发明通过优化来扩展基于散列和分类的分组方法，包括组合跨多个GROUP-BY的常规操作，并使用预先计算的GROUP-BY来计算其他GROUP-BY。 多维数据集算法的扩展可处理任何给定的聚合集合。

公开(公告)号：US06592627B1

公开(公告)日：2003-07-15

申请号：US09329684

申请日：1999-06-10

申请人： Rakesh Agrawal ,  Roberto Javier Bayardo ,  Dimitrios Gunopulos ,  Ching-Tien Howard Ho ,  Sunita Sarawagi ,  John Christopher Shafer ,  Ramakrishnan Srikant

发明人： Rakesh Agrawal ,  Roberto Javier Bayardo ,  Dimitrios Gunopulos ,  Ching-Tien Howard Ho ,  Sunita Sarawagi ,  John Christopher Shafer ,  Ramakrishnan Srikant

IPC分类号： G06F1730

CPC分类号： G06F17/30011

摘要： A user can easily organize computerized document folders by associating a few sample documents in the document database with each folder. The present invention learns folder profiles based on the sample documents and moves the remaining documents into the folders accordingly. In this way, the user can construct new folders, or rearrange existing folders, or cause the computer to automatically rearrange and maintain the folders. This is particularly useful for managing a database of perhaps thousands of emails.

摘要翻译： 用户可以通过将文档数据库中的几个示例文档与每个文件夹相关联来轻松组织计算机化的文档文件夹。 本发明基于样本文件学习文件夹简档，并将剩余的文档相应地移动到文件夹中。 这样，用户可以构建新的文件夹，或重新排列现有的文件夹，或使计算机自动重新排列和维护文件夹。 这对于管理数以千计的电子邮件的数据库特别有用。

公开(公告)号：US06189005B1

公开(公告)日：2001-02-13

申请号：US09138062

申请日：1998-08-21

申请人： Soumen Chakrabarti ,  Byron Edward Dom ,  Sunita Sarawagi

发明人： Soumen Chakrabarti ,  Byron Edward Dom ,  Sunita Sarawagi

IPC分类号： G06F1730

CPC分类号： G06F17/30551 ,  Y10S707/99931 ,  Y10S707/99936 ,  Y10S707/99937 ,  Y10S707/99942 ,  Y10S707/99952

摘要： A system and method for data mining is provided in which temporal patterns of itemsets in transactions having unexpected support values are identified. A surprising temporal pattern is an itemset whose support changes over time. The method may use a minimum description length formulation to discover these surprising temporal patterns.

摘要翻译： 提供了一种用于数据挖掘的系统和方法，其中识别具有意外支持值的事务中的项目集的时间模式。 令人惊讶的时间模式是一个项目集，其支持随着时间的推移而改变。 该方法可以使用最小描述长度公式来发现这些令人惊讶的时间模式。

公开(公告)号：US06324533B1

公开(公告)日：2001-11-27

申请号：US09087561

申请日：1998-05-29

申请人： Rakesh Agrawal ,  Sunita Sarawagi ,  Shiby Thomas

发明人： Rakesh Agrawal ,  Sunita Sarawagi ,  Shiby Thomas

IPC分类号： G06F1730

CPC分类号： G06F17/30539 ,  G06F2216/03 ,  Y10S707/99933

摘要： A method and apparatus for mining data relationships from an integrated database and data-mining system are disclosed. A set of frequent 1-itemsets is generated using a group-by query on data transactions. From these frequent 1-itemsets and the transactions, frequent 2-itemsets are determined. A candidate set of (n+2)-itemsets are generated from the frequent 2-itemsets, where n=1. Frequent (n+2)-itemsets are determined from candidate set and the transaction table using a query operation. The candidate set and frequent (n+2)-itemset are generated for (n+1) until the candidate set is empty. Rules are then extracted from the union of the determined frequent itemsets.

摘要翻译： 公开了一种用于从综合数据库和数据挖掘系统挖掘数据关系的方法和装置。 使用对数据事务的分组查询生成一组频繁的1项集。 从这些频繁的1项目集和事务中，确定频繁的2项集。 从n = 1的频繁2项集中生成（n + 2）个候选集的候选集。 使用查询操作从候选集和事务表确定频繁（n + 2）个事件。 为（n + 1）生成候选集和频繁（n + 2）个目录，直到候选集为空。 然后从确定的频繁项集的并集中提取规则。

公开(公告)号：US6094651A

公开(公告)日：2000-07-25

申请号：US916346

申请日：1997-08-22

申请人： Rakesh Agrawal ,  Sunita Sarawagi

发明人： Rakesh Agrawal ,  Sunita Sarawagi

IPC分类号： G06F17/30

CPC分类号： G06F17/30592 ,  Y10S707/99932 ,  Y10S707/99935

摘要： A method for locating data anomalies in a k dimensional data cube that includes the steps of associating a surprise value with each cell of a data cube, and indicating a data anomaly when the surprise value associated with a cell exceeds a predetermined exception threshold. According to one aspect of the invention, the surprise value associated with each cell is a composite value that is based on at least one of a Self-Exp value for the cell, an In-Exp value for the cell and a Path-Exp value for the cell. Preferably, the step of associating the surprise value with each cell includes the steps of determining a Self-Exp value for the cell, determining an In-Exp value for the cell, determining a Path-Exp value for the cell, and then generating the surprise value for the cell based on the Self-Exp value, the In-Exp value and the Path-value.

摘要翻译： 一种用于定位k维数据立方体中的数据异常的方法，其包括将惊奇值与数据立方体的每个单元相关联的步骤，以及当与单元相关联的突发值超过预定的异常阈值时指示数据异常的步骤。 根据本发明的一个方面，与每个小区相关联的惊喜值是基于小区的Self-Exp值，小区的In-Exp值和Path-Exp值中的至少一个的复合值 对于细胞 优选地，将惊喜值与每个小区相关联的步骤包括以下步骤：确定小区的Self-Exp值，确定小区的In-Exp值，确定小区的Path-Exp值，然后生成 基于Self-Exp值，In-Exp值和Path-value的小区的惊喜值。