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11. 发明授权

US10311105B2 Filtering queried data on data stores 有权
公开(公告)号：US10311105B2
公开(公告)日：2019-06-04
申请号：US12979467
申请日：2010-12-28
申请人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
发明人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
IPC分类号： G06F17/30 , G06F16/903 , G06F16/2453
摘要： A data set may be distributed over many data stores, and a query may be distributively evaluated by several data stores with the results combined to form a query result (e.g., utilizing a MapReduce framework). However, such architectures may violate security principles by performing sophisticated processing, including the execution of arbitrary code, on the same machines that store the data. Instead of processing queries, a data store may be configured only to receive requests specifying one or more filtering criteria, and to provide the data items satisfying the filtering criteria. A compute node may apply a query by generating a request including one or more filter criteria, providing the request to a data node, and applying the remainder of the query (including sophisticated processing, and potentially the execution of arbitrary code) to the data items provided by the data node, thereby improving the security and efficiency of query processing.

12. 发明授权

US09208155B2 Adaptive recommendation system 有权
标题翻译：自适应推荐系统
公开(公告)号：US09208155B2
公开(公告)日：2015-12-08
申请号：US13229482
申请日：2011-09-09
申请人： Nir Nice , Dror Kremer , Daniel Sitton , Michael Feldman , Shimon Shlevich , Ori Folger
发明人： Nir Nice , Dror Kremer , Daniel Sitton , Michael Feldman , Shimon Shlevich , Ori Folger
IPC分类号： G06N5/04 , G06F17/30 , G06Q30/02 , G06Q30/06
CPC分类号： G06F17/30029 , G06N5/04 , G06Q30/0255 , G06Q30/0631
摘要： A recommendation system for optimizing content recommendation lists is disclosed. The system dynamically tracks a list interaction history of a user, which details that user's interactions with a plurality of different lists presenting different recommended items to that user. The system automatically correlates one or more list preferences with that user based on the list interaction history, and builds a recommendation list with a plurality of candidate items having different recommendation confidences. The recommendation list is built such that each candidate item with a higher recommendation confidence is prioritized over each candidate item with a lower recommendation confidence according to the one or more list preferences correlated to that user.
摘要翻译：公开了一种优化内容推荐列表的推荐系统。系统动态地跟踪用户的列表交互历史，其详细描述了用户与向该用户呈现不同推荐项目的多个不同列表的交互。该系统基于列表交互历史自动地将一个或多个列表偏好与该用户相关联，并且建立具有不同推荐信息的多个候选项目的推荐列表。构建推荐列表，使得根据与该用户相关联的一个或多个列表偏好，具有较高建议置信度的每个候选项优先于具有较低建议置信度的每个候选项。

13. 发明授权

US08510284B2 Large-scale event evaluation using realtime processors 有权
标题翻译：使用实时处理器进行大规模事件评估
公开(公告)号：US08510284B2
公开(公告)日：2013-08-13
申请号：US12972575
申请日：2010-12-20
申请人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
发明人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/30424 , G06Q30/02 , G06Q50/01
摘要： Large-scale event processing systems are often designed to perform data mining operations by storing a large set of events in a massive database, applying complex queries to the records of the events, and generating reports and notifications. However, because such queries are performed on very large data sets, the processing of the queries often introduces a significant delay between the occurrence of the events and the reporting or notification thereof. Instead, a large-scale event processing system may be devised as a large state machine organized according to an evaluation plan, comprising a graph of event processors that, in realtime, evaluate each event in an event stream to update an internal state of the event processor, and to perform responses when response conditions are met. The continuous monitoring and evaluation of the stream of events may therefore enable the event processing system to provide realtime responses and notifications of complex queries.
摘要翻译：大型事件处理系统通常被设计为通过将大量事件存储在海量数据库中，将复杂查询应用于事件记录以及生成报告和通知来执行数据挖掘操作。然而，因为这样的查询是在非常大的数据集上执行的，所以查询的处理通常引起事件发生与其报告或通知之间的显着延迟。相反，大型事件处理系统可以被设计为根据评估计划组织的大型状态机，其包括实时地评估事件流中的每个事件以更新事件的内部状态的事件处理器的图形处理器，并在满足响应条件时执行响应。因此，事件流的连续监视和评估可以使得事件处理系统能够提供复杂查询的实时响应和通知。

14. 发明申请

US20120166447A1 FILTERING QUERIED DATA ON DATA STORES 审中-公开
标题翻译：在数据存储中过滤查询数据
公开(公告)号：US20120166447A1
公开(公告)日：2012-06-28
申请号：US12979467
申请日：2010-12-28
申请人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
发明人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
IPC分类号： G06F17/30
CPC分类号： G06F17/30979 , G06F17/30445
摘要： A data set may be distributed over many data stores, and a query may be distributively evaluated by several data stores with the results combined to form a query result (e.g., utilizing a MapReduce framework). However, such architectures may violate security principles by performing sophisticated processing, including the execution of arbitrary code, on the same machines that store the data. Instead of processing queries, a data store may be configured only to receive requests specifying one or more filtering criteria, and to provide the data items satisfying the filtering criteria. A compute node may apply a query by generating a request including one o more filter criteria, providing the request to a data node, and applying the remainder of the query (including sophisticated processing, and potentially the execution of arbitrary code) to the data items provided by the data node, thereby improving the security and efficiency of query processing.
摘要翻译：数据集可以分布在许多数据存储上，并且查询可以被多个数据存储分散地评估，结果被组合以形成查询结果（例如，利用MapReduce框架）。然而，这种架构可能会在存储数据的同一台机器上执行复杂的处理（包括执行任意代码）来违反安全原则。代替处理查询，数据存储可以被配置为仅接收指定一个或多个过滤标准的请求，并且提供满足过滤标准的数据项。计算节点可以通过生成包括一个或多个过滤器标准的请求来应用查询，向数据节点提供请求，并将该查询的剩余部分（包括复杂处理，并且潜在地执行任意代码）应用于数据项由数据节点提供，从而提高查询处理的安全性和效率。

15. 发明申请

US20120158783A1 LARGE-SCALE EVENT EVALUATION USING REALTIME PROCESSORS 有权
标题翻译：使用实时处理器进行大规模事件评估
公开(公告)号：US20120158783A1
公开(公告)日：2012-06-21
申请号：US12972575
申请日：2010-12-20
申请人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
发明人： Nir Nice , Daniel Sitton , Dror Kremer , Michael Feldman
IPC分类号： G06F17/30 , G06F9/44
CPC分类号： G06F17/30424 , G06Q30/02 , G06Q50/01
摘要： Large-scale event processing systems are often designed to perform data mining operations by storing a large set of events in a massive database, applying complex queries to the records of the events, and generating reports and notifications. However, because such queries are performed on very large data sets, the processing of the queries often introduces a significant delay between the occurrence of the events and the reporting or notification thereof. Instead, a large-scale event processing system may be devised as a large state machine organized according to an evaluation plan, comprising a graph of event processors that, in realtime, evaluate each event in an event stream to update an internal state of the event processor, and to perform responses when response conditions are met. The continuous monitoring and evaluation of the stream of events may therefore enable the event processing system to provide realtime responses and notifications of complex queries.
摘要翻译：大型事件处理系统通常被设计为通过将大量事件存储在海量数据库中，将复杂查询应用于事件记录以及生成报告和通知来执行数据挖掘操作。然而，因为这样的查询是在非常大的数据集上执行的，所以查询的处理通常引起事件发生与其报告或通知之间的显着延迟。相反，大型事件处理系统可以被设计为根据评估计划组织的大型状态机，其包括实时地评估事件流中的每个事件以更新事件的内部状态的事件处理器的图形处理器，并在满足响应条件时执行响应。因此，事件流的连续监视和评估可以使得事件处理系统能够提供复杂查询的实时响应和通知。

16. 发明申请

US20130066819A1 ADAPTIVE RECOMMENDATION SYSTEM 有权
标题翻译：自适应推荐系统
公开(公告)号：US20130066819A1
公开(公告)日：2013-03-14
申请号：US13229482
申请日：2011-09-09
申请人： Nir Nice , Dror Kremer , Daniel Sitton , Michael Feldman , Shimon Shlevich , Ori Folger
发明人： Nir Nice , Dror Kremer , Daniel Sitton , Michael Feldman , Shimon Shlevich , Ori Folger
IPC分类号： G06F15/18
CPC分类号： G06F17/30029 , G06N5/04 , G06Q30/0255 , G06Q30/0631
摘要： A recommendation system for optimizing content recommendation lists is disclosed. The system dynamically tracks a list interaction history of a user, which details that user's interactions with a plurality of different lists presenting different recommended items to that user. The system automatically correlates one or more list preferences with that user based on the list interaction history, and builds a recommendation list with a plurality of candidate items having different recommendation confidences. The recommendation list is built such that each candidate item with a higher recommendation confidence is prioritized over each candidate item with a lower recommendation confidence according to the one or more list preferences correlated to that user.
摘要翻译：公开了一种优化内容推荐列表的推荐系统。系统动态地跟踪用户的列表交互历史，其详细描述了用户与向该用户呈现不同推荐项目的多个不同列表的交互。该系统基于列表交互历史自动地将一个或多个列表偏好与该用户相关联，并且建立具有不同推荐信息的多个候选项目的推荐列表。构建推荐列表，使得根据与该用户相关联的一个或多个列表偏好，具有较高建议置信度的每个候选项优先于具有较低建议置信度的每个候选项。

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