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    • 3. 发明申请
    • SERVER SELECTION
    • 服务器选择
    • WO2013166707A1
    • 2013-11-14
    • PCT/CN2012/075362
    • 2012-05-11
    • HEWLETT-PACKARD DEVELOPMENT COMPANY,L.P.LIN, QunyangXIE, JunqingSHEN, Zhiyong
    • LIN, QunyangXIE, JunqingSHEN, Zhiyong
    • H04L12/803
    • G06F9/5083H04L67/1004H04L67/1036
    • Systems (360), methods (240), and machine-readable and executable instructions (368) are provided for selecting a server. Server selection can include receiving a first query (114 and 242) at a management server (106) from a local server (104). Server selection can also include triggering a reply race (116, 244) by sending a number of query notifications from the management server (106) to a number of actor servers (108-1, 108-2, and 108-3), wherein each of the number of actor servers (108-1, 108-2, and 108-3), in response to receiving the query notifications (116), sends a response (118) to the local server (104) and wherein a first actor server (108-1) from the number of actor servers (108-1, 108-2, and 108-3) is selected (120) by the local server (104). Server selection can further include resolving, at the management server (116), future queries (246) from the local server by referencing a first report that was received (126) from the first actor server.
    • 系统(360),方法(240)和机器可读和可执行指令(368)被提供用于选择服务器。 服务器选择可以包括在本地服务器(104)处从管理服务器(106)接收第一查询(114和242)。 服务器选择还可以包括通过将多个查询通知从管理服务器(106)发送到多个运营商服务器(108-1,108-2和108-3)来触发回复比赛(116,244),其中 多个演员服务器(108-1,108-2和108-3)中的每一个响应于接收到查询通知(116)而向本地服务器(104)发送响应(118),并且其中第一 由本地服务器(104)选择来自演员服务器(108-1,108-2和108-3)的数量的演员服务器(108-1)(120)。 服务器选择还可以包括通过引用从第一actor服务器接收到的第一报告(126)来解析来自本地服务器的未来查询(246)在管理服务器(116)处。
    • 10. 发明申请
    • RANK AGGREGATION BASED ON MARKOV MODEL
    • 基于MARKOV模型的RANK聚合
    • WO2016015267A1
    • 2016-02-04
    • PCT/CN2014/083379
    • 2014-07-31
    • HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P.YU, XiaofengXIE, Junqing
    • YU, XiaofengXIE, Junqing
    • G06F17/30
    • G06F17/30687G06F17/18G06F17/30864
    • Rank aggregation based on a Markov model is disclosed. One example is a system including a query processor, at least two information retrievers, a Markov model, and an evaluator. The query processor receives a query via a processing system. Each of the at least two information retrievers retrieves a plurality of document categories responsive to the query, each of the plurality of document categories being at least partially ranked. The Markov model generates a Markov process based on the at least partial rankings of the respective plurality of document categories. The evaluator determines, via the processing system, an aggregate ranking for the plurality of document categories, the aggregate ranking based on a probability distribution of the Markov process.
    • 公布了基于马尔科夫模型的排名聚合。 一个示例是包括查询处理器,至少两个信息检索器,马尔可夫模型和评估器的系统。 查询处理器通过处理系统接收查询。 所述至少两个信息检索器中的每一个检索响应于所述查询的多个文档类别,所述多个文档类别中的每一个至少部分地被分级。 马尔可夫模型基于相应的多个文档类别的至少部分排名来生成马尔可夫过程。 评估者通过处理系统确定多个文档类别的综合排名,基于马尔可夫过程的概率分布的总体排名。