Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Abstract

We have entered the era of big data. Massive datasets, surpassing terabytes and petabytes in size are now commonplace. They arise in numerous settings in science, government, and enterprises, and technology exists by which we can collect and store such massive amounts of information. Yet, making sense of these data remains a fundamental challenge. We lack the means to exploratively analyze databases of this scale. Currently, few technologies allow us to freely "wander" around the data, and make discoveries by following our intuition, or serendipity. While standard data mining aims at finding highly interesting results, it is typically computationally demanding and time consuming, thus may not be well-suited for interactive exploration of large datasets. Interactive data mining techniques that aptly integrate human intuition, by means of visualization and intuitive human-computer interaction techniques, and machine computation support have been shown to help people gain significant insights into a wide range of problems. However, as datasets are being generated in larger volumes, higher velocity, and greater variety, creating effective interactive data mining techniques becomes a much harder task.