发明名称 RESOURCE ALLOCATION IN DISTRIBUTED PROCESSING SYSTEMS
摘要 A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine-learning-capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive analysis results from these one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.
申请公布号 US2016094476(A1) 申请公布日期 2016.03.31
申请号 US201514869748 申请日期 2015.09.29
申请人 Dronen Nicholas A.;Foltz Peter W.;Garner Holly;Loring Miles T.;Kapoor Vishal 发明人 Dronen Nicholas A.;Foltz Peter W.;Garner Holly;Loring Miles T.;Kapoor Vishal
分类号 H04L12/911;G06F9/48 主分类号 H04L12/911
代理机构 代理人
主权项 1. A distributed processing system configured to improve processing speeds, the system comprising: a source device configured to provide groups of data, wherein each of the groups of data is associated with one or several user authors, wherein the groups of data together comprise a processing task; a plurality of independent processing units configured to receive a portion of the processing task, wherein the portion of the processing task comprises one or several of the groups of data, and wherein the independent processing units are configured to characterize one or several aspects of the one or several of the groups of data; and a server communicatively connected to the source device and the plurality of independent processing units via a network, wherein the server is configured to: receive a signal encoding the processing task;identify a plurality of features in some of the groups of data;generate a preliminary subset from the groups of data of the processing task;calculate a subset measure for the preliminary subset, wherein the subset measure indicates the degree to which the subset is representative of the entire processing task;maximize the subset measure by replacing some of the groups of data of the subset; andprovide a final subset to the plurality of independent processing units.
地址 Boulder CO US
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