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41. 发明授权

US07281013B2 Workload analysis tool for relational databases 有权
标题翻译：关系数据库的工作负载分析工具
公开(公告)号：US07281013B2
公开(公告)日：2007-10-09
申请号：US10161397
申请日：2002-06-03
申请人： Surajit Chaudhuri , Vivek Narasayya , Omer Zaki
发明人： Surajit Chaudhuri , Vivek Narasayya , Omer Zaki
IPC分类号： G06F17/30
CPC分类号： G06F17/30595 , Y10S707/958 , Y10S707/99932 , Y10S707/99942
摘要： A method for providing workload information in a structured workload information data structure format that is organized according to a workload schema to be conducive to a given end usage of the information. The structured workload information can be made accessible using standard database analytical server applications to facilitate ad-hoc querying of the structured workload information to summarize and analyze the database workload or to facilitate exchange of workload information. A structured workload information (SWI) is constructed according to a SWI schema to facilitate a desired end usage of the workload information. The query information is extracted from the workload and stored in a structured workload information (SWI) data structure according to the schema based on the desired end usage of the information such as ad hoc querying or information exchange. The query information may be stored in a relational database having query information organized as a central fact table and a collection of hierarchical dimension tables or as an OLAP cube featuring hierarchical dimensions that arrange the query information in dimensions having objects ordered as a function of granularity or the information may be stored according to an XML schema wherein units of query information are separated by XML tags that identify a type of workload information.
摘要翻译：一种用于以结构化工作负载信息数据结构格式提供工作负载信息的方法，所述结构化工作负载信息数据结构格式根据工作负载模式被组织以有利于信息的给定最终使用。结构化工作负载信息可以使用标准数据库分析服务器应用程序进行访问，以便于对结构化工作负载信息进行即席查询，以总结和分析数据库工作负载或促进工作负载信息的交换。根据SWI模式构建结构化工作负载信息（SWI），以便于工作负载信息的期望的最终使用。从工作负载中提取查询信息，并根据信息的期望最终使用情况，根据模式，将其存储在结构化工作负载信息（SWI）数据结构中，例如即席查询或信息交换。查询信息可以存储在具有被组织为中心事实表和分层维度表的集合的查询信息的关系数据库中，或者作为具有分层维度的OLAP多维数据集，OLAP多维数据集将维度中的查询信息排列成具有作为粒度的函数排列的对象，或者可以根据XML模式存储信息，其中查询信息的单位由标识工作负载信息类型的XML标签分隔。

42. 发明授权

US07139778B2 Linear programming approach to assigning benefit to database physical design structures 有权
标题翻译：线性规划方法为数据库物理设计结构分配利益
公开(公告)号：US07139778B2
公开(公告)日：2006-11-21
申请号：US10186821
申请日：2002-06-28
申请人： Surajit Chaudhuri , Vivek Narasayya , Mayur Datar
发明人： Surajit Chaudhuri , Vivek Narasayya , Mayur Datar
IPC分类号： G06F17/30
CPC分类号： G06F17/30595 , Y10S707/954 , Y10S707/99931 , Y10S707/99932
摘要： In a relational database system, a set of physical design structures is enumerated that optimizes database performance over a given workload consisting of workload entries that include queries and updates that have been executed against the database. An individual benefit is calculated for each candidate structure relevant to a given workload entry and these individual benefits are summed over the entries in the workload examined thus far. A workload entry is selected from the workload and a set of candidate structures relevant to the workload entry is identified. A benefit is assigned to each of the candidate structures by 1) evaluating the workload entry in the presence of subsets of candidate structures to obtain an actual benefit for each subset of candidate structures; 2) forming a set of constraints on the structure benefits of candidate structures based on the actual benefits determined for each subset of candidate structures; and 3) determining the individual benefit of each candidate structure by resolving the constraints. The set of physical design structures is enumerated based on the determined benefit for each candidate structure.
摘要翻译：在关系数据库系统中，枚举了一组物理设计结构，其优化了数据库性能，该特定工作负载包括工作负载条目，其中包括针对数据库执行的查询和更新。对于与给定工作量条目相关的每个候选结构计算个人福利，并且将这些个人福利归结于迄今为止审查的工作量中的条目。从工作负载中选择工作负载条目，并识别与工作负载条目相关的一组候选结构。通过以下方式分配每个候选结构的好处：1）在存在候选结构子集的情况下评估工作量项，以获得候选结构的每个子集的实际利益; 2）根据为候选结构的每个子集确定的实际收益，形成一组对候选结构的结构利益的约束; 和3）通过解决约束来确定每个候选结构的个人利益。基于每个候选结构的确定好处列举了一套物理设计结构。

43. 发明授权

US07120624B2 Optimization based method for estimating the results of aggregate queries 失效
标题翻译：用于估计聚合查询结果的基于优化的方法
公开(公告)号：US07120624B2
公开(公告)日：2006-10-10
申请号：US09861960
申请日：2001-05-21
申请人： Surajit Chaudhuri , Vivek Narasayya , Guatam Das
发明人： Surajit Chaudhuri , Vivek Narasayya , Guatam Das
IPC分类号： G06F17/30
CPC分类号： G06F17/30536 , G06F17/30489 , Y10S707/99933 , Y10S707/99934 , Y10S707/99936 , Y10S707/99937 , Y10S707/99943 , Y10S707/99945
摘要： A method for estimating the result of a query on a database having data records arranged in tables. The database has an expected workload that includes a set of queries that can be executed on the database. A sample is constructed by selecting data records for inclusion in the sample in a manner that minimizes an estimation error when the data records are acted upon by a query in the expected workload to provide an estimated result. The query accesses the sample and is executed on the sample, returning an estimated query result. The expected workload can be constructed by specifying a degree of overlap between records selected by queries in the given workload and records selected by queries in the expected workload.
摘要翻译：一种用于估计具有以表格排列的数据记录的数据库的查询结果的方法。数据库具有预期的工作负载，其中包括可在数据库上执行的一组查询。通过以下方式选择数据记录来构建一个样本：当数据记录在期望的工作负载中由查询作用时，使估计误差最小化的方式，以提供估计结果。查询访问样本并在样本上执行，返回估计的查询结果。可以通过指定给定工作负载中的查询选择的记录与预期工作负载中的查询所选择的记录之间的重叠程度来构建预期的工作负载。

44. 发明申请

US20060053103A1 Database aggregation query result estimator 有权
标题翻译：数据库聚合查询结果估计器
公开(公告)号：US20060053103A1
公开(公告)日：2006-03-09
申请号：US11246354
申请日：2005-10-07
申请人： Surajit Chaudhuri , Vivek Narasayya , Rajeev Motwani , Mayur Datar
发明人： Surajit Chaudhuri , Vivek Narasayya , Rajeev Motwani , Mayur Datar
IPC分类号： G06F17/30
CPC分类号： G06F17/30489 , G06F17/30536 , G06F2216/03 , Y10S707/957 , Y10S707/99932 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99943
摘要： Aggregation queries are performed by first identifying outlier values, aggregating the outlier values, and sampling the remaining data after pruning the outlier values. The sampled data is extrapolated and added to the aggregated outlier values to provide an estimate for each aggregation query. Outlier values are identified by selecting values outside of a selected sliding window of data having the lowest variance. An index is created for the outlier values. The outlier data is removed from the window of data, and separately aggregated. The remaining data without the outliers is then sampled to provide a statistically relevant sample that is then aggregated and extrapolated to provide an estimate for the remaining data. This sampled estimate is combined with the outlier aggregate to form an estimate for the entire set of data.
摘要翻译：通过首先识别异常值，聚合异常值和在修剪异常值之后对剩余数据进行采样来执行聚合查询。采样数据被外推并加到聚合异常值中，以提供每个聚合查询的估计。异常值通过选择具有最小方差的数据的所选滑动窗口之外的值来识别。为异常值创建索引。离群数据从数据窗口中移除，并单独汇总。然后对没有异常值的剩余数据进行采样，以提供统计学上相关的样本，然后对其进行聚合和外插，以提供剩余数据的估计。该采样估计与异常值聚合组合以形成整套数据的估计。

45. 发明申请

US20050102305A1 Compressing database workloads 有权
公开(公告)号：US20050102305A1
公开(公告)日：2005-05-12
申请号：US11008335
申请日：2004-12-08
申请人： Surajit Chaudhuri , Ashish Gupta , Vivek Narasayya , Sanjay Agrawal
发明人： Surajit Chaudhuri , Ashish Gupta , Vivek Narasayya , Sanjay Agrawal
IPC分类号： G06F17/30 , G06F17/00
CPC分类号： G06F17/30536 , G06F17/30306 , G06F17/30312 , Y10S706/917 , Y10S707/99932 , Y10S707/99942 , Y10S707/99945
摘要： Relational database applications such as index selection, histogram tuning, approximate query processing, and statistics selection have recognized the importance of leveraging workloads. Often these applications are presented with large workloads, i.e., a set of SQL DML statements, as input. A key factor affecting the scalability of such applications is the size of the workload. The invention concerns workload compression which helps improve the scalability of such applications. The exemplary embodiment is broadly applicable to a variety of workload-driven applications, while allowing for incorporation of application specific knowledge. The process is described in detail in the context of two workload-driven applications: index selection and approximate query processing.

46. 发明授权

US06169983A Index merging for database systems 失效
标题翻译：索引合并数据库系统
公开(公告)号：US06169983A
公开(公告)日：2001-01-02
申请号：US09087617
申请日：1998-05-30
申请人： Surajit Chaudhuri , Vivek Narasayya
发明人： Surajit Chaudhuri , Vivek Narasayya
IPC分类号： G06F1730
CPC分类号： G06F17/30336 , Y10S707/99932 , Y10S707/99933 , Y10S707/99935
摘要： An index merge tool helps form, for use by a database server in accessing a database in accordance with a workload of queries, an index configuration or set of indexes that consumes relatively less storage space. The index merge tool identifies from an initial set of indexes one or more combinations of two or more indexes on the same table of the database and merges each identified combination of indexes to form a merged set of indexes. The index merge tool identifies and merges each combination of indexes by identifying and merging one pair of indexes at a time. The index merge tool uses the merged set of indexes as the index configuration for use in executing queries against the database so long as the storage saved by the merged set of indexes exceeds a threshold amount and so long as any increase in the cost to execute queries against the database using the merged set of indexes is limited. Otherwise, the index merge tool uses the initial set of indexes as the index configuration.
摘要翻译：索引合并工具有助于形成数据库服务器根据查询的工作负载访问数据库，使用索引配置或索引集合来消耗相对较少的存储空间。索引合并工具从初始索引集中识别数据库同一表上的两个或多个索引的一个或多个组合，并合并每个已标识的索引组合以形成一组合并的索引。索引合并工具通过一次识别和合并一对索引来识别和合并索引的每个组合。索引合并工具使用合并的索引集作为用于对数据库执行查询的索引配置，只要合并的索引集合保存的存储超过阈值量，并且只要执行查询的成本增加使用合并的索引集对数据库进行限制。否则，索引合并工具将使用初始索引集作为索引配置。

47. 发明授权

US5960423A Database system index selection using candidate index selection for a workload 失效
标题翻译：数据库系统索引选择使用工作负载的候选索引选择
公开(公告)号：US5960423A
公开(公告)日：1999-09-28
申请号：US980830
申请日：1997-12-01
申请人： Surajit Chaudhuri , Vivek Narasayya
发明人： Surajit Chaudhuri , Vivek Narasayya
IPC分类号： G06F17/30
CPC分类号： G06F17/30312 , Y10S707/99931 , Y10S707/99932 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99953
摘要： An index selection tool helps reduce costs in time and memory in selecting an index configuration or set of indexes for use by a database server in accessing a database in accordance with a workload of queries. The index selection tool attempts to reduce the number of indexes to be considered, the number of index configurations to be enumerated, and the number of invocations of a query optimizer in selecting an index configuration for the workload.
摘要翻译：索引选择工具有助于在选择索引配置或索引集时，在数据库服务器根据查询的工作量访问数据库时，减少时间和内存中的成本。索引选择工具尝试减少要考虑的索引数量，要枚举的索引配置数量以及查询优化器在为工作负载选择索引配置时调用的次数。

48. 发明申请

US20120323921A1 DICTIONARY FOR HIERARCHICAL ATTRIBUTES FROM CATALOG ITEMS 有权
标题翻译：来自目录项目的分类属性的词典
公开(公告)号：US20120323921A1
公开(公告)日：2012-12-20
申请号：US13160532
申请日：2011-06-15
申请人： Zhimin Chen , Eduardo Laureano , Renfei Luo , Tsheko Mutungu , Vivek Narasayya , David Talby
发明人： Zhimin Chen , Eduardo Laureano , Renfei Luo , Tsheko Mutungu , Vivek Narasayya , David Talby
IPC分类号： G06F17/30
CPC分类号： G06F17/30616
摘要： A plurality of items included in a catalog may be obtained, each item associated with an item category. Brand indicators may be obtained, each brand indicator associated with the item category. Brand indicators associated with each of the items may be determined, and the each item may be assigned to a partition group associated with the brand indicator that is associated with the each item. Correlated string tokens that are correlated, greater than a predetermined correlation threshold value, with the brand indicator associated with the partition group that is associated with the each one of the items, the correlated string tokens associated with the each one of the plurality of items, may be determined. A dictionary hierarchy may be generated based on the one or more correlated string tokens.
摘要翻译：可以获得包括在目录中的多个项目，每个项目与项目类别相关联。可以获得品牌指标，每个品牌指标与项目类别相关联。可以确定与每个项目相关联的品牌指示符，并且可以将每个项目分配给与与每个项目相关联的品牌指示符相关联的分区组。与相关联的字符串令牌，大于预定的相关阈值，与与与每个项目相关联的分区组相关联的品牌指示符，与多个项目中的每一个相关联的相关联的字符串令牌，可以确定。可以基于一个或多个相关串令牌来生成词典层次。

49. 发明授权

US07720883B2 Key profile computation and data pattern profile computation 有权
标题翻译：关键轮廓计算和数据模式轮廓计算
公开(公告)号：US07720883B2
公开(公告)日：2010-05-18
申请号：US11769050
申请日：2007-06-27
申请人： Zhimin Chen , Venkatesh Ganti , Gunjan Jha , Shriraghav Kaushik , Vivek Narasayya
发明人： Zhimin Chen , Venkatesh Ganti , Gunjan Jha , Shriraghav Kaushik , Vivek Narasayya
IPC分类号： G06F7/00 , G06F17/30
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。

50. 发明申请

US20090006392A1 DATA PROFILE COMPUTATION 有权
标题翻译：数据配置文件计算
公开(公告)号：US20090006392A1
公开(公告)日：2009-01-01
申请号：US11769050
申请日：2007-06-27
申请人： Zhimin Chen , Venkatesh Ganti , Gunjan Jha , Shriraghav Kaushik , Vivek Narasayya
发明人： Zhimin Chen , Venkatesh Ganti , Gunjan Jha , Shriraghav Kaushik , Vivek Narasayya
IPC分类号： G06F7/06 , G06F17/30
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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