Sas Institute Inc.

Xiangxiang Meng

Biruk Gebremariam

Rajendra Prasad Singh

Jordan Riley Benson

Xiangqian Hu

David James Caira

Robert Wayne Thompson

Duane Hamilton

A computing system transforms variable values in a dataset using a transformation flow definition applied in parallel. The transformation flow definition indicates flow variables and transformation phases to apply to the flow variables. A computation is defined for each variable and for each transformation phase. A phase internal parameter value is computed for each defined computation from observation vectors read from the dataset. A current variable, a first variable value, a first transformation phase, the phase internal parameter value, and a current transformation phase are selected based on an observation vector read from the dataset. A result value is computed by executing the transformation function with the phase internal parameter value and the first variable value. The computed result value is output to a transformed input dataset. The process is repeated for each variable, transformation phase, and observation vector.

Distributed data transformation system

Various embodiments are generally directed to techniques for visualizing clustered datasets, such as by utilizing multiple colors and multiple color gradients to represent data from different clustered datasets, for instance. Some embodiments are particularly directed to using different colors associated with each cluster of data to visualize which cluster is dominant in each cell of a heat map. Further, in many embodiments, a color gradient may be used among different heat map cells of a common color that correspond to a common cluster to visualize data distributions within each cluster of data represented in the heat map. In multiple embodiments, colors and color gradients may be utilized in conjunction with visualizing clustered datasets to enable identification of useful patterns and relationships among a collection of clustered datasets. In several embodiments, heat maps and/or heat map matrices may be generated and presented via a user interface (UI).

Techniques for visualizing clustered datasets

Techniques for providing interactive decision trees are included. For example, a system is provided that stores data related to a decision tree, wherein the data includes one or more data structures and one or more portions of code. The system receives input corresponding to an interaction request associated with a modification to the decision tree. The system determines whether the modification requires multiple-processing iterations of the distributed data set. The system generates an application layer modified decision tree when the generating requires no multiple-processing iterations of the distributed data set. The system facilitates server layer modification of the decision tree when the modification requires multiple-processing iterations of the distributed data set. The system generates a representation of the application layer modified decision tree or the server layer modified decision tree.

Techniques for interactive decision trees

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Cary, NC, United States of America

Xiangxiang Meng