@conference {254236,
	title = {Queriosity: Automated Data Exploration},
	booktitle = {Proceedings of the IEEE International Congress on Big Data},
	year = {2015},
	month = {2015},
	address = {New York, USA},
	abstract = {Curiosity, a fundamental drive amongst higher living organisms, is what enables exploration, learning and creativity. In our increasingly data-driven world, data exploration, i.e., making sense of mounting haystacks of data, is akin to intelligence for science, business and individuals. However, modern data systems {\textendash} designed for data retrieval rather than exploration {\textendash} only let us retrieve data and ask if it is interesting. This makes knowledge discovery a game of hit-and-trial which can only be orchestrated by expert data scientists.
We present the vision toward Queriosity, an automated and personalized data exploration system. Designed on the principles of autonomy, learning and usability, Queriosity envisions a paradigm shift in data exploration and aims to become a a personalized {\textquotedblleft}data robot{\textquotedblright} that provides a direct answer to what is interesting in a user{\textquoteright}s data set, instead of just retrieving data. Queriosity autonomously and continuously navigates toward interesting findings based on trends, statistical properties and interactive user feedback.},
	author = {Wasay, Abdul and Athanassoulis, Manos and Stratos Idreos}
}
