Fast visualization of large spatiotemporal datasets
Nanocubes provides you with real-time visualization of large datasets. Slice and dice your data with respect to space, time, or some of your data attributes, and view the results in real-time on a web browser over heatmaps, bar charts, and histograms. We've used it for tens of billions of data points: maybe you can push it even farther!
If you want to know more details about the algorithm behind nanocubes, you can read the research paper that describes it:
Lauro Lins, James T. Klosowski, and Carlos Scheidegger. Nanocubes for Real-Time Exploration of Spatiotemporal Datasets. Visualization and Computer Graphics, IEEE Transactions on 19, no. 12 (2013): 2456-2465. Nominated for best paper award. PDF.
Interested? Try the live demos! All of these datasets are running off of a single machine with 16GB of RAM. For the demos not tagged as "tablet-friendly", you will need a WebGL capable browser. We have tested it on Chrome and Firefox, but ourselves use Chrome and OS X for development.
Nanocubes build on data cube technology. Until recently, data cubes took a very large amount of space. This means they could not be stored in main memory, so their computation and access for large datasets did not mix well with interactive visualization. Our main innovation is an algorithm for hierarchical data cubes that has very modest memory requirements. So it is just like a data cube, but it's tiny! We thought "nanocube" sounded better than "tinycube".
This project uses a litany of open-source projects and software, for which we are incredibly grateful. In no particular order, we want to acknowledge our use of: Bootstrap, Bootstrap Tour, jQuery, Underscore.js, d3, OpenStreetMap, Leaflet.js, and Lux
In addition, we wish to acknowledge the comments, suggestions and help of Stephen North, Drew Skau, Hadley Wickham, Luiz Scheidegger, Chris Volinsky, Simon Urbanek, Robert Kosara, John Moeller, David Kormann, T.J. Jankun-Kelly and Steve Haroz.