公开(公告)号：US20200301966A1

公开(公告)日：2020-09-24

申请号：US16355996

申请日：2019-03-18

Applicant: salesforce.com, inc.

Inventor： Nathan Irace Burke ,  Kexin Xie ,  Xingyu Wang ,  Wanderley Liu ,  David Yourdon

IPC: G06F16/906 ,  G06K9/62 ,  G06F17/18

Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

公开(公告)号：US11556595B2

公开(公告)日：2023-01-17

申请号：US17163081

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Nathan Irace Burke ,  Kexin Xie ,  Xingyu Wang ,  Wanderley Liu ,  David Yourdon

IPC: G06F16/906 ,  G06F17/18 ,  G06K9/62 ,  G06Q30/00

Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

公开(公告)号：US20210157847A1

公开(公告)日：2021-05-27

申请号：US17163081

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Nathan Irace Burke ,  Kexin Xie ,  Xingyu Wang ,  Wanderley Liu ,  David Yourdon

IPC: G06F16/906 ,  G06K9/62 ,  G06F17/18

Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

公开(公告)号：US11275768B2

公开(公告)日：2022-03-15

申请号：US16120067

申请日：2018-08-31

Applicant: salesforce.com, inc.

Inventor： Yacov Salomon ,  Kexin Xie ,  Wanderley Liu

IPC: G06F16/28 ,  G06F16/2458 ,  G06F16/906 ,  G06N5/02

Abstract: Methods, systems, and devices supporting differential support for frequent pattern (FP) analysis are described. Some database systems may analyze data sets to determine FPs of data attributes within the data sets. However, if data distributions for different types of data attributes vary greatly, more frequent data attribute types may skew the FPs away from the less frequent types. To reduce the noise of common attributes while maintaining sensitivity to the less common attributes, the database system may implement multiple minimum support (e.g., frequency) thresholds. For example, the database system may adaptively categorize the different data attribute types into data categories based on their distributions and may dynamically determine support thresholds for the categories. Using different minimum support thresholds for different data categories allows the system to filter out data attribute patterns based on the distributions of the data attribute types in the pattern.

公开(公告)号：US11061937B2

公开(公告)日：2021-07-13

申请号：US16144715

申请日：2018-09-27

Applicant: salesforce.com, inc.

Inventor： Yacov Salomon ,  Jonathan Purnell ,  Wanderley Liu ,  Kexin Xie

IPC: G06F16/00 ,  G06F16/28 ,  G06Q30/02 ,  G06F16/907 ,  G06F16/9535 ,  G06F16/34

Abstract: A database system performs lookalike analysis on a data set including a plurality of user identifiers, which are associated with one or more attribute records. The database system classifies the user identifiers into one or more segments of user identifiers based on the attribute records. The database system performs Linear Discriminant Analysis (LDA) to calculate a measure of importance of the attribute records relative to the one or more segments. The database system auto-correlates the attribute records based on the numbers of attribute records in the user identifier population and the one or more segments. The database system identifies a set of user identifiers relative to one or more segments using the measures of importance and the auto-correlated parameters.

公开(公告)号：US10963519B2

公开(公告)日：2021-03-30

申请号：US16355996

申请日：2019-03-18

Applicant: salesforce.com, inc.

Inventor： Nathan Irace Burke ,  Kexin Xie ,  Xingyu Wang ,  Wanderley Liu ,  David Yourdon

IPC: G06F16/906 ,  G06K9/62 ,  G06F17/18 ,  G06Q30/00

Abstract: A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.