公开(公告)号：US20140358644A1

公开(公告)日：2014-12-04

申请号：US14151517

申请日：2014-01-09

Applicant: Linkedin Corporation

Inventor： Sathyanarayan Anand ,  Guangde Chen ,  Xin Fu

IPC: G06Q10/06

CPC classification number: G06Q10/06398 ,  G06Q10/06375 ,  G06Q50/01

Abstract: The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.

Abstract translation: 所公开的实施例涉及用于分析在线专业网络中的性能的系统。 在操作期间，系统接收用于用户动作的时间序列数据，其中对于每个用户动作，时间序列数据包括与连续时间间隔相关联的一系列数字，其中给定的数字指示在该时间期间发生用户动作的次数 间隔。 系统还接收用于性能度量的时间序列数据，其中对于每个性能度量，时间序列数据包括与连续时间间隔相关联的一系列数字，其中给定数量指示在时间间隔期间发生性能度量的次数。 然后系统对接收到的用户动作和性能指标的时间序列数据执行时间序列分析，以确定用户操作与性能指标之间的关系。

公开(公告)号：US20140324964A1

公开(公告)日：2014-10-30

申请号：US13871570

申请日：2013-04-26

Applicant: LINKEDIN CORPORATION

Inventor： Sathyanarayan Anand ,  Ganesh Ramesh ,  Alexis Blevins Baird

IPC: H04L29/08

CPC classification number: H04L67/22 ,  G06Q30/0201 ,  G06Q50/01 ,  H04L67/18 ,  H04L67/306

Abstract: Disclosed in some examples is a method including receiving a plurality of transaction records, each of the transaction records including data about a particular transaction engaged in by a member of a social networking service and including a geographic location and a timestamp of the particular transaction; scoring each of the plurality of transaction records based upon the recency of the transaction; clustering the plurality of transaction records into a plurality of clusters, each cluster including transaction records which contain similar geographic locations; creating an aggregate score for each particular one of the plurality of clusters based upon a sum total of the scores calculated for each transaction record clustered into the particular cluster; and creating a probability distribution based upon the scores for the plurality of clusters, the probability distribution indicating a probability that the member was in each of the plurality of locations represented by the clusters.

Abstract translation: 在一些示例中公开的是包括接收多个交易记录的方法，每个交易记录包括关于由社交网络服务的成员参与并且包括特定交易的地理位置和时间戳的特定交易的数据; 基于交易的新近程度对多个交易记录中的每一个进行评分; 将多个交易记录聚类成多个集群，每个集群包括包含相似地理位置的交易记录; 基于针对聚簇到所述特定簇中的每个交易记录计算的分数的总和来创建所述多个集群中的每个特定集群的聚合分数; 以及基于所述多个聚类的得分创建概率分布，所述概率分布指示所述成员在由所述聚类表示的所述多个位置中的每一个中的每一个中的概率。

公开(公告)号：US09635116B2

公开(公告)日：2017-04-25

申请号：US13871570

申请日：2013-04-26

Applicant: LinkedIn Corporation

Inventor： Sathyanarayan Anand ,  Ganesh Ramesh ,  Alexis Blevins Baird

IPC: H04L29/08 ,  G06Q50/00 ,  G06Q30/02

CPC classification number: H04L67/22 ,  G06Q30/0201 ,  G06Q50/01 ,  H04L67/18 ,  H04L67/306

Abstract: Disclosed in some examples is a method including receiving a plurality of transaction records, each of the transaction records including data about a particular transaction engaged in by a member of a social networking service and including a geographic location and a timestamp of the particular transaction; scoring each of the plurality of transaction records based upon the recency of the transaction; clustering the plurality of transaction records into a plurality of clusters, each cluster including transaction records which contain similar geographic locations; creating an aggregate score for each particular one of the plurality of clusters based upon a sum total of the scores calculated for each transaction record clustered into the particular cluster; and creating a probability distribution based upon the scores for the plurality of clusters, the probability distribution indicating a probability that the member was in each of the plurality of locations represented by the clusters.

公开(公告)号：US09524481B2

公开(公告)日：2016-12-20

申请号：US14151517

申请日：2014-01-09

Applicant: LinkedIn Corporation

Inventor： Sathyanarayan Anand ,  Guangde Chen ,  Xin Fu

IPC: G06Q10/06 ,  G06Q50/00

CPC classification number: G06Q10/06398 ,  G06Q10/06375 ,  G06Q50/01

Abstract: The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.

Abstract translation: 所公开的实施例涉及用于分析在线专业网络中的性能的系统。 在操作期间，系统接收用于用户动作的时间序列数据，其中对于每个用户动作，时间序列数据包括与连续时间间隔相关联的一系列数字，其中给定的数字指示在该时间期间发生用户动作的次数 间隔。 系统还接收用于性能度量的时间序列数据，其中对于每个性能度量，时间序列数据包括与连续时间间隔相关联的一系列数字，其中给定数量指示在时间间隔期间发生性能度量的次数。 然后，系统对接收到的用户动作和性能指标的时间序列数据执行时间序列分析，以确定用户动作与绩效指标之间的关系。

公开(公告)号：US08694635B1

公开(公告)日：2014-04-08

申请号：US13907506

申请日：2013-05-31

Applicant: LinkedIn Corporation

Inventor： Sathyanarayan Anand ,  Guangde Chen ,  Xin Fu

IPC: G06F15/173 ,  G06Q99/00

CPC classification number: G06Q10/06398 ,  G06Q10/06375 ,  G06Q50/01

Abstract: The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.

Abstract translation: 所公开的实施例涉及用于分析在线专业网络中的性能的系统。 在操作期间，系统接收用于用户动作的时间序列数据，其中对于每个用户动作，时间序列数据包括与连续时间间隔相关联的一系列数字，其中给定的数字指示在该时间期间发生用户动作的次数 间隔。 系统还接收用于性能度量的时间序列数据，其中对于每个性能度量，时间序列数据包括与连续时间间隔相关联的一系列数字，其中给定数量指示在时间间隔期间发生性能度量的次数。 然后，系统对接收到的用户动作和性能指标的时间序列数据执行时间序列分析，以确定用户动作与绩效指标之间的关系。