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    • 61. 发明申请
    • LARGE GRAPH MEASUREMENT
    • 大图测量
    • US20120078927A1
    • 2012-03-29
    • US13314555
    • 2011-12-08
    • Sreenivas Gollapudi
    • Sreenivas Gollapudi
    • G06F17/30
    • G06F17/30876G06F17/30873
    • As provided herein, a pairwise distance between nodes in a large graph can be determined efficiently. URL-sketches are generated for respective nodes in an index by extracting labels from respective nodes, which provide a reference to a link between the nodes, aggregating the labels into sets for respective nodes, and storing the sets of labels as URL-sketches. Neighborhood-sketches are generated for the respective nodes in the index using the URL-sketches, by determining a neighborhood for a node and generating a sketch using labels that are associated with the respective neighboring nodes. A distance between two nodes is determined by computing an approximate number of paths and an approximate path length between the two nodes, using the neighborhood sketches for the two nodes.
    • 如本文所提供的,可以有效地确定大图中的节点之间的成对距离。 通过从相应节点提取标签,提供对节点之间的链接的引用,将标签聚集到各个节点的集合中,并将标签集合存储为URL草图,从而为索引中的相应节点生成URL草图。 通过使用URL草图,通过确定节点的邻域并使用与各个相邻节点相关联的标签生成草图来为索引中的相应节点生成邻域草图。 通过使用两个节点的邻域草图计算两个节点之间的近似路径数和两个节点之间的近似路径长度来确定两个节点之间的距离。
    • 62. 发明申请
    • DETERMINING QUERY INTENT
    • 确定查询内容
    • US20110314012A1
    • 2011-12-22
    • US12816389
    • 2010-06-16
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • Krishnaram N. G. KenthapadiPanayiotis TsaparasSreenivas GollapudiRakesh Agrawal
    • G06F17/30
    • G06F17/30979
    • A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.
    • 树结构具有与项目类别的层次结构的每个类别相关联的节点。 树的子节点与与父节点相关联的类别的子类别相关联。 通过将每个查询与对应于所选择的查询类别的节点相关联,将包括接收到的查询和针对每个接收到的查询的所选项目类别的指示符的训练数据与树结构组合。 当接收到查询时,分类器被应用于节点以产生查询旨在匹配与节点相关联的类别的项目的概率。 应用分类器直到概率低于阈值。 选择与接收到的查询的意图最接近的节点相关联的一个或多个类别,并输出与接收到的查询匹配的那些类别的项目的指示符。
    • 64. 发明申请
    • LEARNING DIVERSE RANKINGS OVER DOCUMENT COLLECTIONS
    • 学习文档集合中的多样性排名
    • US20110264639A1
    • 2011-10-27
    • US12764112
    • 2010-04-21
    • Aleksandrs SlivkinsSreenivas GollapudiFilip Radlinski
    • Aleksandrs SlivkinsSreenivas GollapudiFilip Radlinski
    • G06F17/30
    • G06F16/9535
    • A document selector selects and ranks documents that are relevant to a query. The document selector executes an instance of a multi-armed bandits algorithm to select a document for each slot of a results page according to one or more strategies. The documents are selected in an order defined by the results page and documents selected for previous slots are used to guide the selection of a document for a current slot. If a document in a slot is subsequently selected, the strategy used to select the document is rewarded with positive feedback. When the uncertainty in an estimate of the utility of a strategy is less than the variation between documents associated with the strategy, the strategy is subdivided into multiple strategies. The document selector is able to “zoom in” on effective strategies and provide more relevant search results.
    • 文档选择器选择和排序与查询相关的文档。 文档选择器执行多武装强盗算法的实例,以根据一个或多个策略为结果页面的每个时隙选择文档。 文档按照结果页面定义的顺序进行选择,为先前的插槽选择的文档用于指导当前插槽文档的选择。 如果随后选择了插槽中的文档,则用于选择文档的策略将获得积极的反馈。 当战略效用估计的不确定性低于与战略相关的文件之间的变化时,该策略被细分为多种策略。 文档选择器能够“放大”有效策略并提供更相关的搜索结果。
    • 67. 发明授权
    • Method and apparatus for efficient SQL processing in an n-tier architecture
    • 用于在n层体系结构中进行有效的SQL处理的方法和装置
    • US07580971B1
    • 2009-08-25
    • US09953490
    • 2001-09-10
    • Sreenivas GollapudiDebashis SahaAnindo RoyLakshminarayanan ChidambaranDebashish Chatterjee
    • Sreenivas GollapudiDebashis SahaAnindo RoyLakshminarayanan ChidambaranDebashish Chatterjee
    • G06F15/16
    • G06F17/30474G06F17/3048H04L67/1097
    • A method and apparatus for efficiently processing data requests in a network oriented n-tier database environment is presented. According to one embodiment of the invention, certain or all data from the tables of a database server device can be maintained in tables on the client device in a client side database cache server system. This local cache allows the network oriented n-tier database system to eliminate the expense of repetitive network transmissions to respond to duplicate queries for the same information. Additionally, the local client device may also keep track of what data is cached on peer network nodes. This allows the client to request that data from a peer database cache server and off load that burden from the database server device. Moreover, the local client may also keep statistics regarding the frequency of requested data in order to optimize the data set maintained in the local database cache server.
    • 提出了一种在面向网络的n层数据库环境中有效处理数据请求的方法和装置。 根据本发明的一个实施例,来自数据库服务器设备的表的某些或所有数据可以在客户机侧数据库高速缓存服务器系统中的客户端设备的表中维护。 该本地缓存允许面向网络的n层数据库系统消除重复网络传输的费用,以响应相同信息的重复查询。 此外,本地客户端设备还可以跟踪在对等网络节点上高速缓存哪些数据。 这允许客户端从对等数据库缓存服务器请求数据,并从数据库服务器设备卸载负担。 此外,本地客户端还可以保持关于所请求数据的频率的统计信息,以便优化本地数据库缓存服务器中维护的数据集。
    • 70. 发明授权
    • Method and apparatus for document clustering and document sketching
    • 用于文档聚类和文档素描的方法和装置
    • US07433869B2
    • 2008-10-07
    • US11427781
    • 2006-06-29
    • Sreenivas Gollapudi
    • Sreenivas Gollapudi
    • G06F17/30
    • G06F17/30616G06F17/3071Y10S707/99933Y10S707/99935Y10S707/99942Y10S707/99943
    • A first embodiment of the invention provides a system that automatically classifies documents in a collection into clusters based on the similarities between documents, that automatically classifies new documents into the right clusters, and that may change the number or parameters of clusters under various circumstances. A second embodiment of the invention provides a technique for comparing two documents, in which a fingerprint or sketch of each document is computed. In particular, this embodiment of the invention uses a specific algorithm to compute the document's fingerprint, One embodiment uses a sentence in the document as a logical delimiter or window from which significant words are extracted and, thereafter, a hash is computed of all pair-wise permutations. Words are extracted based on their weight in the document, which can be computed using measures such as term frequency and the inverse document frequency.
    • 本发明的第一实施例提供了一种系统,其基于文档之间的相似性自动地将集合中的文档分类成簇,该文档将新文档自动分类到正确的集群中,并且可以在各种情况下改变集群的数量或参数。 本发明的第二实施例提供了一种用于比较两个文档的技术,其中计算每个文档的指纹或草图。 特别地,本发明的该实施例使用特定的算法来计算文档的指纹。一个实施例将文档中的句子用作提取有效字的逻辑定界符或窗口,此后,计算所有对 - 明智的排列。 根据文档中的权重提取单词,可以使用诸如术语频率和逆文档频率等度量来计算单词。