Big data analytics requires new workflows: high latency queries, massively-parallel code, and cloud computing infrastructures all make handling a big dataset different (and harder) than working on a local machine. We are exploring user experiences for analysts, and thinking about new ways to deal with big datasets.
BigDataUX: building a better user experience for Big Data.
Lots of different definitions can be found for "big data," but they all have one aspect in common: big data is inconvenient. It's too big to fit on screen, or in memory, or on disk. There are more fields than are easy to articulate. And it is so ill-organized and messy that it will take a fair bit of nursing to get it into usable shape.
We want to explore what technologies will make it easier for users -- for data scientists, business intelligence analysts, or anyone with a dataset -- to clean, process, and interact with big datasets. To do that, we've assembled a collaborative team: specialists in UX, visualization and computer-language experts, back-end algorithms and system builders; database designers.
- Mike Barnett, Badrish Chandramouli, Robert DeLine, Steven Drucker, Danyel Fisher, Jonathan Goldstein, Patrick Morrison, and John Platt, Stat! - An Interactive Analytics Environment for Big Data, in ACM SIGMOD International Conference on Management of Data (SIGMOD 2013), ACM SIGMOD, June 2013
- Danyel Fisher, Igor Popov, Steven M. Drucker, and mc schraefel, Trust Me, I'm Partially Right: Incremental Visualization Lets Analysts Explore Large Datasets Faster, in Proceedings of the 2012 Conference on Human Factors in Computing Systems (CHI 2012), ACM Conference on Human Factors in Computing Systems, 5 May 2012
- Danyel Fisher, Rob DeLine, Mary Czerwinski, and Steven Drucker, Interactions with Big Data Analytics, in ACM Interactions, ACM, May 2012
- Danyel Fisher, Incremental, Approximate Database Queries and Uncertainty for Exploratory Visualization, in IEEE Symposium on Large Data Analysis and Visualization, IEEE, 23 October 2011