Tableau Software, Inc.

Eric Roy Brochu

David John Mosimann

Andrew C Beers

Mark Siegel

Kazem Jahanbakhsh

Connie Hong-Ying Wong

Hon To Ming

Mya Rose Warren

Yogesh Sood

Leiling Tao

Rachel Stern Kalmar

Lu Yu

Embodiments are directed to managing visualizations of data. Visualization models and a user profile may be provided such that the visualization models and the user profile may be associated with an organization. A complexity score for the organization may be provided based on one or more characteristics of the organization. A recommendation model may be provided based on the complexity score and a baseline model. The recommendation model may be employed to determine one or more recommended visualization models based on the user profile such that the recommendation model associates each recommended visualization model with a confidence score. The one or more recommended visualization models may be rank ordered based on each associated confidence score. A report that includes a rank ordered list of the one or more recommended visualization models may be provided to a user associated with the user profile.

Providing data visualizations based on personalized recommendations

Embodiments are directed to managing visualizations of data. Visualizations to monitor may be determined based on metrics associated with the monitored visualizations. Mark values may be sampled from each monitored visualization such that the sampled mark values may be stored with a timestamp. If an amount of the sampled mark values for a monitored visualization exceeds a threshold value mark models may be trained to classify a portion of the sampled mark values associated with the monitored visualization such that the mark models may predict ranges of values for the classified mark values associated with the monitored visualization. If the portion of the classified mark values associated with the monitored visualization has a value outside of the predicted range values, indicating that the monitored visualization may be associated with an anomalous mark value.

Detecting anomalies in visualizations

A method identifies relevant data sources in a data visualization user interface. The process constructs an interaction matrix X whose rows correspond to users and whose columns correspond to data sources. Each entry measures the extent to which a user interacts with a data source according to user interactions with data visualizations generated from the data source. The method factors X into a product of a user matrix U times a source matrix Susing iterative refinement to minimize the least squares difference between X and U&#xb7;S. A user connects to a data server. The method computes affinity scores between the user and data sources. Each affinity score is a dot product of a row of U (a user) and a row of S (a data source). The method selects some data sources corresponding to high computed affinity scores and returns the selected data sources to the user.

Identifying relevant data sources for a data visualization application

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Vancouver, Canada

Eric Roy Brochu