METHOD AND APPARATUS FOR PROVIDING LOCATION-BASED SOCIAL SEARCH SERVICE
    2.
    发明申请
    METHOD AND APPARATUS FOR PROVIDING LOCATION-BASED SOCIAL SEARCH SERVICE 审中-公开
    提供基于位置的社会搜索服务的方法和装置

    公开(公告)号:US20160110372A1

    公开(公告)日:2016-04-21

    申请号:US14635647

    申请日:2015-03-02

    CPC classification number: G06F16/9537 G06F16/248

    Abstract: Provided is a method of providing a location-based social search service. The method includes collecting, from a social network system, one or more pieces of visit history information including at least one of visit information of social search service users in association with at least a place and review data associated with the place, and storing the collected information; selecting a topic for each topic category from the visit history information; when a location-based query is received from at least one of the users, extracting a topic for each topic category from the location-based query; analyzing, for each topic category, a relationship between the topic extracted from the location-based query and the topic selected from the visit history information, and selecting at least one of the users as an expert.

    Abstract translation: 提供了一种提供基于位置的社会搜索服务的方法。 所述方法包括从社交网络系统收集包括至少一个地方的社会搜索服务用户的访问信息中的至少一个的访问历史信息,并且查看与该地点相关联的数据,以及存储所收集的 信息; 从访问历史信息中选择每个主题类别的主题; 当从至少一个用户接收到基于位置的查询时,从基于位置的查询中提取每个主题类别的主题; 针对每个主题类别分析从基于位置的查询提取的主题与从访问历史信息中选择的主题之间的关系,以及作为专家来选择至少一个用户。

    METHOD OF DETECTING OVERLAPPING COMMUNITY IN NETWORK
    3.
    发明申请
    METHOD OF DETECTING OVERLAPPING COMMUNITY IN NETWORK 审中-公开
    检测网络覆盖社区的方法

    公开(公告)号:US20140149430A1

    公开(公告)日:2014-05-29

    申请号:US13930069

    申请日:2013-06-28

    CPC classification number: G06F16/24578

    Abstract: A method of detecting an overlapping community in a network including nodes and links between the nodes, includes calculating a similarity between the links, and generating a line graph of the network. The method further includes detecting one or more cores in the line graph, and growing a cluster for each of the one or more cores. The method further includes converting the cluster into a cluster of nodes of a node graph.

    Abstract translation: 一种检测网络中重叠社区的方法,包括节点和节点之间的链路,包括计算链路之间的相似度,并生成网络的线形图。 该方法还包括检测线图中的一个或多个核心,以及为一个或多个核心中的每个核心增长群集。 该方法还包括将簇转换成节点图的节点簇。

    METHOD AND APPARATUS FOR PREDICTING IMPORTED INFECTIOUS DISEASE INFORMATION BASED ON DEEP NEURAL NETWORKS

    公开(公告)号:US20220293282A1

    公开(公告)日:2022-09-15

    申请号:US17686632

    申请日:2022-03-04

    Abstract: A method for operating an apparatus for predicting confirmed cases of an infectious disease is provided. The method comprises predicting infectious disease information per country, including an infection risk per country, expected number of entrants per country, and number of imported cases per country, based on collected epidemic statistics data per country and inflow data between a corresponding country and a destination country, grouping two or more countries based on geographic or economic relevance, and correcting the infectious disease information per country of countries within a grouped group according to a contagion risk impact set depending on a correlation between the countries within the group, and predicting total number of imported cases flowing into the destination country by re-correcting the infectious disease information per country through applying a correlation for the confirmed cases of the infectious disease between groups to the infectious disease information per country.

    REAL-TIME OUTLIER DETECTION METHOD AND APPARATUS IN MULTIDIMENSIONAL DATA STREAM

    公开(公告)号:US20220284076A1

    公开(公告)日:2022-09-08

    申请号:US17354219

    申请日:2021-06-22

    Abstract: An outlier detection device sets a weight for a kernel center of a grid cell based on a distribution of the data disposed on the grid cell region, calculates a cumulative change of a weight for each corresponding kernel center, sets a stationary region in the grid cell region based on the cumulative change, maintains a density of a kernel center of the stationary region as a previous density, calculates a density of a kernel center excluding the stationary region to update the calculated density, estimates a density of multidimensional data at the current time, and detects an arbitrary number of outliers based on a relative difference between the density of the multidimensional data and a density of a kernel center nearest to the multidimensional data.

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