Salesforce Com, Inc.

Salesforce, Inc.

Xingyu Wang

David Yourdon

Nathan Irace Burke

Kexin Xie

Wanderley Liu

Jonathan Budd

Gloriose Hitimana

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.

Attribute diversity for frequent pattern analysis

A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.

Correlating user device attribute groups

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Xingyu Wang