The Nanocubes^® technology 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!

How does it work

The main Nanocubes program is a command-line utility that processes your data and starts a web server to answer query requests. We provide Javascript APIs for visualization. But the Nanocubes program can be used for fast analysis of your data as well: you can think of it as a very fast (if somehow limited) database query engine. As an illustrative example, we have used anomaly detection routines to query the Nanocubes engine and output potential outliers and hotspots.

Papers

• 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

• A follow-up paper that extends the Nanocubes technology to handle top-k queries: TopKube: A Rank-Aware Data Cube for Real-Time Exploration of Spatiotemporal Data.

• Lukas Scharlau (FH Münster University of Applied Sciences) has also studied how to build Nanocubes query engines in parallel for his bachelor thesis. PDF

Open Source

Download our implementation of the Nanocubes technology here.

Full source code is hosted on GitHub. Please file any bugs, feature requests, etc. as issues thru GitHub.

Team

Lauro Lins, Jim Klosowski, Carlos Scheidegger, Horace C. Y. Ip.

Why the strange name?

The Nanocubes technology builds 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 "Nanocubes" sounded better than "tiny cubes".

Awards

• Oct 2014 Won Gold Award for the Best New Product or Service of the Year at the 2014 International Business Awards.
• Sep 2014 Won Gold Award for Technology Innovation of the Year at the 2014 American Business Awards.
• Apr 2014 Won Gold Award for Research and Business Optimization at the 2014 Edison Awards.
• Oct 2013 Awarded InfoVis Best Paper Honorable Mention 2013 IEEE Vis Conference.

In the News

• Sep 2017 AT&T Innovation Blog: Total Eclipse of the Network.
• Mar 2016 The New IP: AT&T's Nanocubes Brings Data Analytics to Life.
• Feb 2016 Inside Big Data: How Open Source is Driving New Innovations in Data Analytics.
• Feb 2016 The Examiner: Eighty-One Million Social Media Posts with Pictures during Super Bowl.
• Feb 2016 AT&T Innovation Blog: New Data Record Set at Big Game by the Bay.
• Feb 2016 AT&T Newsroom: Denver vs Carolina: Revealing the Loudest Fans on Social.
• Jan 2016 AT&T Innovation Blog: Expanding Innovation in Healthcare, the Internet of Things, Big Data and Open Source.
• Apr 2015 Astronomy Computing Today: Interactive Visualization of Billion-Point Data Sets: Nanocubes.
• Apr 2015 AT&T Innovation Blog: A Policy (Engine) for Open Source.
• May 2014 Mashable: AT&T Innovation Showcase Features Trackable Luggage, Big Data Visualizers.
• May 2014 VentureBeat: 5 of the Coolest Projects in the Works at AT&T's Innovation Centers.
• May 2014 FierceWirelessTech: AT&T's Open Source Nanocube for Data Analytics Wins Accolade.
• May 2014 AT&T Technology Innovation Showcase: Mobilizing Your World: A Look Ahead at Next-Generation Techonlogies.
• Nov 2013 InformationWeek: Analyze 46 Petabytes of Data Via Laptop? Here's How.
• Oct 2013 AT&T Newsroom: From Digits to Diagrams.
• Oct 2013 AT&T Research: Into Main Memory: Nanocubes for Interactively Visualizing Billion-Point Data Sets.

Acknowledgments

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.