IDENTIFYING INFLUENCERS FOR TOPICS IN SOCIAL MEDIA
    11.
    发明申请
    IDENTIFYING INFLUENCERS FOR TOPICS IN SOCIAL MEDIA 有权
    识别社会媒体主题的影响因素

    公开(公告)号:US20160306888A1

    公开(公告)日:2016-10-20

    申请号:US15186975

    申请日:2016-06-20

    Abstract: A computer determines social media influencers in a specific topic by receiving a dataset of information associated with a website, the information including a first list of users of the website and a list of content that each user posts on the website, wherein each user is associated with other users from the first list of users. The computer determines initial values representing variables of the dataset of information on the website, wherein the variables include one or more topics for the list of content that each user from the first list of users posts on the website. The computer performs an iteration of Gibbs Sampling utilizing the initial values. The computer determines the one or more new values representing variables of the dataset represent a distribution of the one or more topics for the list of content that each user from the first list of users posts.

    Abstract translation: 计算机通过接收与网站相关联的信息的数据集来确定特定主题中的社交媒体影响者,所述信息包括网站的用户的第一列表和每个用户在网站上张贴的内容的列表,其中每个用户被关联 与其他用户从第一个用户列表。 计算机确定表示网站上的信息数据集的变量的初始值,其中变量包括来自第一用户列表的每个用户在网站上发布的内容列表的一个或多个主题。 计算机利用初始值执行吉布斯抽样的迭代。 计算机确定表示数据集变量的一个或多个新值表示来自第一用户列表的每个用户的内容列表的一个或多个主题的分布。

    Dynamic interaction graphs with probabilistic edge decay

    公开(公告)号:US10249070B2

    公开(公告)日:2019-04-02

    申请号:US15951064

    申请日:2018-04-11

    Abstract: A computer-implemented method, according to one embodiment, includes: generating two or more sample graphs by sampling edges of a current snapshot of a dynamic graph, generating two or more partial results by executing an algorithm on the two or more sample graphs, combining the partial results into a final result, and incrementally maintaining the sample graphs. Edges included in the current snapshot of a dynamic graph and which were added to the dynamic graph in a most recent update thereto are included in each of the generated two or more sample graphs. Moreover, incrementally maintaining the sample graphs includes: subsampling each of the edges of each of the sample graphs at a given time by applying a Bernoulli trial, and combining a result of the subsampling with new edges received in a batch corresponding to the given time to form new sample graphs.

    JOINING DATA ACROSS A PARALLEL DATABASE AND A DISTRIBUTED PROCESSING SYSTEM
    16.
    发明申请
    JOINING DATA ACROSS A PARALLEL DATABASE AND A DISTRIBUTED PROCESSING SYSTEM 有权
    通过并行数据库和分布式处理系统连接数据

    公开(公告)号:US20160103877A1

    公开(公告)日:2016-04-14

    申请号:US14511345

    申请日:2014-10-10

    CPC classification number: G06F17/30445 G06F17/30545

    Abstract: Embodiments relate to joining data across a parallel database and a distributed processing system. Aspects include receiving a query on data stored in parallel database T and data stored in distributed processing system L, applying local query predicates and projection to data T to create T′, and applying local query predicates and projection to L to create L′. Based on determining that a size of L′ is less than a size of T′ and that the size of L′ is less than a first threshold, transmitting L′ to the parallel database and executing a join between T′ and L′. Based on determining that a number of the nodes distributed processing system n multiplied by the size of T′ is less than the size of L′ and that the size of T′ is less than a second threshold; transmitting T′ to the distributed processing system and executing a join between T′ and L′.

    Abstract translation: 实施例涉及跨并行数据库和分布式处理系统连接数据。 方面包括接收对存储在并行数据库T中的数据和存储在分布式处理系统L中的数据的查询,将本地查询谓词和投影应用于数据T以创建T',以及将本地查询谓词和投影应用于L创建L'。 基于确定L'的大小小于T'的大小并且L'的大小小于第一阈值,将L'发送到并行数据库并执行T'和L'之间的连接。 基于确定多个节点分布处理系统n乘以T'的大小小于L'的大小并且T'的大小小于第二阈值; 将T'发送到分布式处理系统并执行T'和L'之间的连接。

    IDENTIFYING INFLUENCERS FOR TOPICS IN SOCIAL MEDIA
    17.
    发明申请
    IDENTIFYING INFLUENCERS FOR TOPICS IN SOCIAL MEDIA 有权
    识别社会媒体主题的影响因素

    公开(公告)号:US20150193535A1

    公开(公告)日:2015-07-09

    申请号:US14149422

    申请日:2014-01-07

    Abstract: A computer determines social media influencers in a specific topic. The computer receives a dataset of information on a website, the information including a list of users of the website and a list of content that each user posts, wherein each user is associated with one or more other users. The computer identifies a plurality of variables associated with the dataset, wherein the plurality of variables represent the information of the dataset on the website. The computer executes a topic specific search based on the plurality of variables, the topic search providing at least another list of users representing influencers in a specific topic.

    Abstract translation: 一台电脑确定一个特定主题的社会媒体影响者。 计算机接收网站上的信息数据集,该信息包括网站的用户列表和每个用户发布的内容的列表,其中每个用户与一个或多个其他用户相关联。 计算机识别与数据集相关联的多个变量,其中多个变量表示网站上数据集的信息。 计算机基于多个变量执行主题特定搜索,主题搜索提供代表特定主题中的影响者的至少另一用户列表。

    Executing graph path queries
    19.
    发明授权

    公开(公告)号:US11106671B2

    公开(公告)日:2021-08-31

    申请号:US16131427

    申请日:2018-09-14

    Abstract: Embodiments of the invention relate to executing graph path queries. A database stores data entities and attributes in node tables and stores links between nodes in an edge table. Edges form a path between a source node and a target node. A source node set is generated and joined with the edge table to produce a first intermediate set. Similarly, a target node set is generated and joined with the edge table to produce a second intermediate set. A result path is generated through a joining of the first and second intermediate paths and application of a length condition.

    Executing graph path queries
    20.
    发明授权

    公开(公告)号:US11100102B2

    公开(公告)日:2021-08-24

    申请号:US16139962

    申请日:2018-09-24

    Abstract: Embodiments relate to executing graph path queries. A database stores data entities and attributes in node tables and stores links between nodes in an edge table. Edges form a path between a source node and a target node. A source node set is generated and joined with the edge table to produce a first intermediate set. Similarly, a target node set is generated and joined with the edge table to produce a second intermediate set. A result path is generated through a joining of the first and second intermediate paths and application of a length condition.

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