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    • 4. 发明申请
    • SCALABLE AND EXTENDABLE STREAM PROCESSING
    • 可扩展和可扩展的流程处理
    • US20130111054A1
    • 2013-05-02
    • US13282483
    • 2011-10-27
    • Timothy Harrington
    • Timothy Harrington
    • G06F15/16
    • H04L65/605G06F15/16H04L41/145H04L67/1031H04L67/2833H04L67/2838
    • An extensible architecture that enables developers to focus solely on the domain-specific nature of the stream processing algorithm to be implemented. It is positioned as an intermediary component between streaming data feeds and stream algorithms, thereby aggregating demand on data sources and hiding the complexity involved in managing active connections to different data sources. Per-algorithm stream throttling is provided so that individual algorithms do not get overloaded; thus, ensuring that algorithms receive fresh items from the data feeds to which the algorithms subscribe. Feed items can be discarded when an algorithm is not able to process the items in realtime to ensure that feed items are sampled at the fastest processing rate of the algorithm. Thus, a single instance of an algorithm can handle an entire data stream. Moreover, redundancy can be achieved by running the same configuration on multiple machines.
    • 一种可扩展架构,使开发人员能够专注于要实现的流处理算法的特定于域的性质。 它被定位为流数据馈送和流算法之间的中间组件,从而聚合对数据源的需求并隐藏管理到不同数据源的活动连接所涉及的复杂性。 提供每算法流限制,使得各个算法不会过载; 因此,确保算法从算法订阅的数据馈送中接收新的项目。 当算法无法实时处理项目以确保以最快的算法处理速率对Feed项进行采样时,可以丢弃Feed项。 因此,算法的单个实例可以处理整个数据流。 此外,可以通过在多台机器上运行相同的配置来实现冗余。
    • 6. 发明授权
    • Social network recommended content and recommending members for personalized search results
    • 社交网络推荐内容,推荐会员查询个性化搜索结果
    • US08949232B2
    • 2015-02-03
    • US13252215
    • 2011-10-04
    • Timothy HarringtonRajesh ShenoyMarc NajorkRina Panigrahy
    • Timothy HarringtonRajesh ShenoyMarc NajorkRina Panigrahy
    • G06F17/30H04L12/58
    • H04L51/32G06F17/30864
    • Architecture that provides a data structure to facilitate personalized ranking over recommended content (e.g., documents). The data structure approximates the social distance of the searching user to the content at query time. A graph is created of content recommended by members of the social network, where the nodes of the graph include content nodes (for the content) and recommending member nodes (for members of the social network who recommended the content). If a member recommends content, an edge is created between the member node and the content node. If a member is a “friend” (tagged as related in some way) of another member, an edge is created between the two member nodes. Each node is converted to a lower dimensional feature set. Feature sets of the content are indexed and the feature set of the searching user is utilized to match and rank the search results at query time.
    • 提供数据结构以促进针对推荐内容(例如,文档)的个性化排名的架构。 数据结构近似于搜索用户在查询时的内容的社交距离。 创建由社交网络成员推荐的内容的图形,其中图的节点包括内容节点(用于内容)和推荐成员节点(为推荐内容的社交网络的成员)。 如果成员建议内容,则会在成员节点和内容节点之间创建一个边。 如果一个成员是另一个成员的“朋友”(以某种方式标记为相关),则在两个成员节点之间创建一个边。 每个节点都转换为较低维度的特征集。 内容的特征集被索引,并且利用搜索用户的特征集来在查询时间匹配和排列搜索结果。
    • 8. 发明授权
    • Scalable and extendable stream processing
    • 可扩展和可扩展的流处理
    • US08930563B2
    • 2015-01-06
    • US13282483
    • 2011-10-27
    • Timothy Harrington
    • Timothy Harrington
    • H04L29/06H04L29/08H04L12/24G06F15/16
    • H04L65/605G06F15/16H04L41/145H04L67/1031H04L67/2833H04L67/2838
    • An extensible architecture that enables developers to focus solely on the domain-specific nature of the stream processing algorithm to be implemented. It is positioned as an intermediary component between streaming data feeds and stream algorithms, thereby aggregating demand on data sources and hiding the complexity involved in managing active connections to different data sources. Per-algorithm stream throttling is provided so that individual algorithms do not get overloaded; thus, ensuring that algorithms receive fresh items from the data feeds to which the algorithms subscribe. Feed items can be discarded when an algorithm is not able to process the items in realtime to ensure that feed items are sampled at the fastest processing rate of the algorithm. Thus, a single instance of an algorithm can handle an entire data stream. Moreover, redundancy can be achieved by running the same configuration on multiple machines.
    • 一种可扩展架构,使开发人员能够专注于要实现的流处理算法的特定于域的性质。 它被定位为流数据馈送和流算法之间的中间组件,从而聚合数据源的需求并隐藏管理到不同数据源的活动连接所涉及的复杂性。 提供每算法流限制,使得各个算法不会过载; 因此,确保算法从算法订阅的数据馈送中接收新的项目。 当算法无法实时处理项目以确保以最快的算法处理速率对Feed项进行采样时,可以丢弃Feed项。 因此,算法的单个实例可以处理整个数据流。 此外,可以通过在多台机器上运行相同的配置来实现冗余。
    • 9. 发明申请
    • SOCIAL NETWORK RECOMMENDED CONTENT AND RECOMMENDING MEMBERS FOR PERSONALIZED SEARCH RESULTS
    • 社会网络推荐内容和推荐会员个人搜索结果
    • US20130086057A1
    • 2013-04-04
    • US13252215
    • 2011-10-04
    • Timothy HarringtonRajesh ShenoyMarc NajorkRina Panigrahy
    • Timothy HarringtonRajesh ShenoyMarc NajorkRina Panigrahy
    • G06F17/30
    • H04L51/32G06F17/30864
    • Architecture that provides a data structure to facilitate personalized ranking over recommended content (e.g., documents). The data structure approximates the social distance of the searching user to the content at query time. A graph is created of content recommended by members of the social network, where the nodes of the graph include content nodes (for the content) and recommending member nodes (for members of the social network who recommended the content). If a member recommends content, an edge is created between the member node and the content node. If a member is a “friend” (tagged as related in some way) of another member, an edge is created between the two member nodes. Each node is converted to a lower dimensional feature set. Feature sets of the content are indexed and the feature set of the searching user is utilized to match and rank the search results at query time.
    • 提供数据结构以促进针对推荐内容(例如,文档)的个性化排名的架构。 数据结构近似于搜索用户在查询时的内容的社交距离。 创建由社交网络成员推荐的内容的图形,其中图的节点包括内容节点(用于内容)和推荐成员节点(为推荐内容的社交网络的成员)。 如果成员建议内容,则会在成员节点和内容节点之间创建边。 如果一个成员是另一个成员的朋友(以某种方式标记为相关),则在两个成员节点之间创建一个边。 每个节点都转换为较低维度的特征集。 内容的特征集被索引,并且利用搜索用户的特征集来在查询时间匹配和排列搜索结果。