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    • 53. 发明授权
    • Tagging XML query results over relational DBMSs
    • 在关系数据库管理系统上标记XML查询结果
    • US06934712B2
    • 2005-08-23
    • US09810167
    • 2001-03-19
    • Gerald George KiernanJayavel ShanmugasundaramEugene Jon Shekita
    • Gerald George KiernanJayavel ShanmugasundaramEugene Jon Shekita
    • G06F17/30
    • G06F17/30917G06F17/30595G06F17/30932G06F17/30941Y10S707/99932Y10S707/99933Y10S707/99934Y10S707/99935Y10S707/99943
    • A method for publishing relational data as XML by translating XML queries into queries against a relational database. Conversion of the relational database into an XML database is not required. Each relational table is mapped to a virtual XML document, and XML queries are issued over these virtual documents. An XML query is parsed and transformed into a language-neutral intermediate representation, which is a sequence of operations describing how the output document is derived from the underlying relational tables. The intermediate representation is then translated into an SQL query over the underlying relational tables. The intermediate representation is also used to generate a tagger graph, which the tagger runtime ‘walks’ to generate the tagged, structured XML output. Each of the nodes of the tagger graph are operators which perform processing on the results of the SQL query. The SQL query is executed, and the SQL query results are then provided to the tagger. The tagger runtime applies the operators of each node to the inputs at that node to produce the structured XML document as a query result, guided by the structure of the tagger graph.
    • 通过将XML查询转换为关系数据库的查询来将关系数据发布为XML的方法。 不需要将关系数据库转换为XML数据库。 每个关系表映射到一个虚拟XML文档,并通过这些虚拟文档发出XML查询。 解析XML查询并将其转换为语言中立的中间表示,它是描述输出文档如何从底层关系表导出的操作序列。 然后将中间表示形式转换为基础关系表中的SQL查询。 中间表示也用于生成标记图,标记运行时“走”来生成标记的结构化XML输出。 标记图中的每个节点都是对SQL查询结果执行处理的操作符。 执行SQL查询,然后将SQL查询结果提供给标记器。 标记运行时将每个节点的运算符应用于该节点的输入,以便通过标记器图的结构来指导结构化的XML文档作为查询结果。
    • 54. 发明授权
    • Left outer join elimination on key
    • 关键的左外连接消除
    • US06721730B2
    • 2004-04-13
    • US09887582
    • 2001-06-21
    • Gerald George KiernanJayavel Shanmugasundaram
    • Gerald George KiernanJayavel Shanmugasundaram
    • G06F1730
    • G06F17/30454Y10S707/99933Y10S707/99934
    • A query optimization technique that determines whether any column from a right quantifier of a left outer join query is projected out of the query. If not, then all predicates in an ON clause are removed from the left outer join query, the right quantifier is removed from the left outer join query, and the left outer join query is converted to a simple select query. A number of steps are performed to determine whether any of the columns quantified by the right quantifier participate in a projection list of the query. A set of equivalence class columns is computed for the query, wherein the equivalence classes are derived from equi-join predicates in the query. A set of columns quantified by the right quantifier across the computed set of equivalence classes is computed. A determination is made whether a subset of the set of columns form a key for the right quantifier. If the right quantifier columns are not projected out of the query, then the optimization may be performed.
    • 一种查询优化技术,用于确定来自左侧外部连接查询的右侧定量器的任何列是否从该查询中投射出来。 如果没有,那么ON子句中的所有谓词将从左外连接查询中删除,右侧的量词会从左外连接查询中删除,左外连接查询将转换为简单的选择查询。 执行多个步骤以确定由右侧量词库量化的任何列是否参与查询的投影列表。 为查询计算一组等价类列,其中等价类是从查询中的等连接谓词导出的。 计算通过所计算的等价类集合的正确量词量化的一组列。 确定一组列的一个子集是否形成正确的量词的键。 如果正确的量词列不会从查询中投射出来,则可以执行优化。
    • 55. 发明授权
    • Multi-dimensional database record compression utilizing optimized cluster models
    • 利用优化的集群模型进行多维数据库记录压缩
    • US06633882B1
    • 2003-10-14
    • US09606964
    • 2000-06-29
    • Usama FayyadJayavel Shanmugasundaram
    • Usama FayyadJayavel Shanmugasundaram
    • G06F1730
    • G06F17/30592Y10S707/99936Y10S707/99942
    • Apparatus and method for use in querying a database containing data records. The database is characterized by a compression scheme to provide data clustering information. In accordance with a exemplary embodiment of the invention a functional representation of data clustering is a Gaussian and the queries are performing by integrating the Gaussian corresponding to each of the data clusters over the ranges to determine the sum or the count of data records from the database that fall within the selected ranges. The process chooses a value for the cluster number K. The cluster model is next broken up into areas (tiles) based on user defined parameters. Data from the database is then classified based on the tiling information. A sorted version of the classified data, ordered by cluster number and then by the tile number within the cluster is generated. This data is then evaluated to test the sufficiency of the model created during the clustering.
    • 用于查询包含数据记录的数据库的装置和方法。 数据库的特征在于提供数据聚类信息的压缩方案。 根据本发明的示例性实施例,数据聚类的功能表示是高斯,并且通过在范围上对与每个数据集合相对应的高斯进行积分来执行查询,以确定来自数据库的数据记录的和或计数 在所选范围内。 该过程选择簇号K的值。根据用户定义的参数,将聚类模型分解为区域(瓦片)。 然后根据平铺信息对来自数据库的数据进行分类。 生成分类数据的排序版本,按簇号排序,然后由群集中的瓦片号排序。 然后评估该数据以测试在聚类期间创建的模型的充分性。
    • 56. 发明授权
    • Multi-dimensional database and data cube compression for aggregate query support on numeric dimensions
    • 多维数据库和数据立方体压缩,用于在数字维度上的聚合查询支持
    • US06549907B1
    • 2003-04-15
    • US09296831
    • 1999-04-22
    • Usama FayyadJayavel Shanmugasundaram
    • Usama FayyadJayavel Shanmugasundaram
    • G06F1730
    • G06F17/30489G06F17/30324G06F17/30592G06F17/30598Y10S707/99933Y10S707/99942Y10S707/99953
    • An apparatus and method for efficiently compressing contents of a database system to support ad hoc querying and OLAP type aggregation queries. This invention consists of a new compressed representation of the data cube that (a) drastically reduces storage requirements, (b) does not require the discretization hierarchy along each query dimension to be fixed beforehand and (c) treats each dimension as a potential target measure and supports multiple aggregation functions without additional storage costs. The tradeoff is approximate, yet relatively accurate, answers to queries. We outline mechanisms to reduce the error in the approximation. Our performance evaluation indicates that our compression technique effectively addresses the limitation of existing approaches. The basic method relies on representing the contents of the database by a probability distribution consisting of a mixture of Gaussians. Aggregation queries, be they multi-dimensional, conjunctive, or disjunctive, can be answered by performing integration over the probability distribution.
    • 一种用于高效地压缩数据库系统的内容以支持即席查询和OLAP类型聚合查询的装置和方法。 本发明由数据立方体的新的压缩表示形式组成,(a)大大减少存储需求,(b)不需要预先固定每个查询维度的离散化层级,并且(c)将每个维度视为潜在的目标度量 并支持多个聚合功能,无需额外的存储成本。 权衡是大概的,但相对准确的是查询的答案。 我们概述了减少近似误差的机制。 我们的绩效评估表明,我们的压缩技术有效地解决了现有方法的局限性。 基本方法依赖于通过由高斯混合组成的概率分布来表示数据库的内容。 可以通过对概率分布进行集成来回答聚合查询(无论是多维的,连接的还是分离的)。
    • 57. 发明申请
    • NETWORK BASED ADVERTISEMENT SYSTEM
    • 基于网络的广告系统
    • US20120054027A1
    • 2012-03-01
    • US12871240
    • 2010-08-30
    • Randolph Preston McAfeeVijay Krishna NarayananJayavel ShanmugasundaramRajesh G. Parekh
    • Randolph Preston McAfeeVijay Krishna NarayananJayavel ShanmugasundaramRajesh G. Parekh
    • G06Q30/00
    • G06Q30/0251
    • A network based advertisement system includes an optimizer configured to forecast a supply of opportunities, forecast a supply of guaranteed contracts, and forecast a supply of non-guaranteed contracts. Each opportunity represents a user visiting a webpage. Each guaranteed contract guarantees the matching of an advertisement to a number of opportunities. Each non-guaranteed contract guarantees a user event associated with an advertisement. The optimizer then generates a plan for matching contracts to opportunities based on the forecasted supply of opportunities, the forecasted supply of guaranteed contracts, the forecasted supply of non-guaranteed contracts, and an objective function that balances a group of parameters that define the representativeness of contracts, a cost associated with not serving non-guaranteed contracts, and performance objectives associated with contracts.
    • 基于网络的广告系统包括优化器,其被配置为预测机会供应,预测保证合同的供应以及预测非保证合同的供应。 每个机会代表访问网页的用户。 每个保证合同保证广告与许多机会的匹配。 每个非保证合同保证与广告相关联的用户事件。 然后,优化者根据预测的机会供应,预期的保证合同供应,预期的无担保合同供应,以及平衡一组定义代表性的参数的目标函数,生成一个将契约与机会相匹配的计划。 合同,与服务非保证合同相关的成本以及与合同相关的业绩目标。