Salesforce Com, Inc.

Salesforce, Inc.

Business Objects Software Limited

Sybil Shim

Amit Kumar

Daniel Scott Perry

Saeed Jahankhani

Lingtao Zhang

Chang Lu

Brittany Zenger

Yi Zhao Liu

Daniel Georges

Charles Wilson

Paul Van Der Eerden

A method to provide multi-objective recommendations. The method includes receiving user input indicating a plurality of objectives, where each of the plurality of objectives indicates a desired goal for a field of interest, receiving user input indicating a plurality of actionable fields, receiving user input indicating selection of one of a plurality of records in a data set, determining, based on applying an evolutionary algorithm, one or more candidate changes to values of the plurality of actionable fields of the selected record, determining, for each of the one or more candidate changes, a multi-objective score for that candidate change, selecting one or more of the one or more candidate changes to recommend to a user based on the multi-objective scores of the one or more candidate changes, and providing, for display to the user, the selected one or more candidate changes as recommended changes.

Multi-objective recommendations in a data analytics system

A computer implemented method of providing suggestions of related columns for a table operation in a data management system is provided. In one embodiment, the method includes storing, by a processor, a table having a plurality of rows and a plurality of columns, metadata associated with the plurality of columns, and data stored within the plurality of rows and the plurality of columns; receiving, by a processor, a table operation selected by a system or user; processing, by a processor, at least one of the data stored in the plurality of columns and the metadata associated with plurality columns based on a type of the selected table operation to determine at least one relationship between at least two columns of the plurality of columns; and generating, by the processor, dialog data that presents the at least one relationship between the at least two columns to a system or user for use in the table operation.

Smart column selection for table operations in data preparation

Examples of auto-monitoring and adjusting dynamic data visualizations are provided herein. A data visualization based on initial data can be generated. A series of data updates can be received. The data visualization can be updated based on the series of data updates. Various performance metrics can be monitored, and data updates and/or the updated data visualization can be adjusted accordingly. Performance metrics can include at least one of: a data visualization rendering time; a data transfer time; or a data update generation time. Upon determining that one or more performance metrics exceed a threshold: a time between data updates of the series of data updates can be increased; sampled data can be requested for subsequent data updates; and/or a time-dimension extent of the updated data visualization can be reduced.

Auto-monitoring and adjustment of dynamic data visualizations

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Vancouver, Canada

Sybil Shim