IDEA 2014 - Workshop on Interactive Data Exploration and Analytics (IDEA)

We have entered the era of big data. Massive datasets, surpassing terabytes and petabytes, are now commonplace. They arise in numerous settings in science, government, and enterprises. Today, 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 (HCI) 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.
Our focus and emphasis is on interactivity and effective integration of techniques from data mining, visualization and human-computer interaction. In other words, we intend to explore how the best of these different but related domains can be combined such that the sum is greater than the parts.
Call for Papers
Topics of interests for the workshop include, but are not limited to:
interactive data mining algorithms
visualizations for interactive data mining
demonstrations of interactive data mining
quick, high-level data analysis methods
any-time data mining algorithms
visual analytics
methods that allow meaningful intermediate results
data surrogates
on-line algorithms
adaptive stream mining algorithms
theoretical/complexity analysis of instant data mining
learning from user input for action replication/prediction
active learning / mining