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    Distributed information retrieval in peer-to-peer networks
    1.
    发明授权
    Distributed information retrieval in peer-to-peer networks 失效
    对等网络中的分布式信息检索

    公开(公告)号：US07743044B1

    公开(公告)日：2010-06-22

    申请号：US10156417

    申请日：2002-05-28

    申请人： Vana Kalogeraki ,  Dimitrios Gunopulos

    发明人： Vana Kalogeraki ,  Dimitrios Gunopulos

    IPC分类号： G06F17/30

    CPC分类号： H04L67/104 ,  G06F17/30106 ,  G06F17/30206 ,  H04L67/1072

    摘要： A mechanism for information retrieval in fully decentralized, distributed, peer-to-peer network systems. Peer profiles are aggregated and collected in real-time by each peer. Each peer uses and integrates knowledge that it collects during query-reply cycles for each future query received, thereby learning over time and making information retrieval a more intelligent and rapid process. Each peer then autonomously decides which of its peers are most likely to have an answer to a given query. A routine is provided for monitoring the messages and profiling each peer, building a local peer profile for each node exchanging messages in the peer-to-peer network based on messages passing through the node.

    摘要翻译： 完全分散式，分布式，点对点网络系统中的信息检索机制。 每个对等体实时聚合和收集对等体配置文件。 每个对等体在查询回复周期期间使用和集成收集的每个未来查询的知识，从而随着时间的推移学习，使信息检索更加智能和快速。 然后，每个对等体自主地确定其对等体中最有可能对给定查询的答案。 提供了一个例程，用于监视消息并对每个对等体进行概要分析，根据通过该节点的消息为每个节点在对等网络中交换消息，构建本地对等体配置文件。

    System and method for constraint-based rule mining in large, dense data-sets
    6.
    发明授权
    System and method for constraint-based rule mining in large, dense data-sets 有权
    在大型，密集数据集中基于约束的规则挖掘的系统和方法

    公开(公告)号：US06278997B1

    公开(公告)日：2001-08-21

    申请号：US09245319

    申请日：1999-02-05

    申请人： Rakesh Agrawal ,  Roberto Javier Bayardo ,  Dimitrios Gunopulos

    发明人： Rakesh Agrawal ,  Roberto Javier Bayardo ,  Dimitrios Gunopulos

    IPC分类号： G06F1730

    CPC分类号： G06F17/30539 ,  Y10S707/99936

    摘要： A dense data-set mining system and method is provided that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint, known as minimum gap, which prunes any rule having conditions that do not contribute to its predictive accuracy. The method maintains efficiency even at low supports on data that is dense in the sense that many items appear with high frequency (e.g. relational data).

    摘要翻译： 提供了一种密集的数据集挖掘系统和方法，其直接利用所有用户指定的约束，包括最小支持，最小置信度和称为最小间隙的新约束，该约束修剪具有条件的任何规则，这些条件对其预测准确性无贡献 。 即使在许多项目以高频率出现（例如关系数据）的意义上，密集的数据的低支持性也能够保持效率。