InnoCentive Reduction to Practice Challenge: Visualization of Large Multi-Dimensional Datasets

Innovation challenge seeking a software solution to efficiently visualize and explore multi-dimensional datasets containing spatial, temporal, and other types of dimensions, with the capability to process and visualize five-dimensional data with tens of millions of points on a standard quad-core computer.

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This InnoCentive Reduction to Practice (RTP) Challenge – Visualization of Large Multi-Dimensional Datasets – is seeking a software solution to efficiently visualize and explore multi-dimensional datasets containing spatial, temporal, and other types of dimensions, with the capability to process and visualise five-dimensional data with tens of millions of points on a standard quad-core computer.

Multi-dimensional data is ubiquitous in many technical fields and can be used to track the variation of a dependent variable as a function of multiple independent variables (spatial, temporal, or other dimensional variables types such as species, mass, etc).

While visualisation and characterisation of data is well established in cases of one or two independent variables, it becomes increasing complex and difficult to visualise and utilise as more dimensions are added. Standard desktop computers are often not capable of handling the software required to process multi-dimensional datasets.

This Challenge is seeking software tools for efficient visualisation and exploration of these multi-dimensional datasets. The data for this Challenge consists of multiple data files of varying sizes containing five-dimensional data. The largest data file contains more than 45 million data points.