DocumentCode
1667963
Title
Queriosity: Automated Data Exploration
Author
Wasay, Abdul ; Athanassoulis, Manos ; Idreos, Stratos
fYear
2015
Firstpage
716
Lastpage
719
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 -- designed for data retrieval rather than exploration -- 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 "data robot" that provides a direct answer to what is interesting in a user\´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.
Keywords
data mining; information retrieval; learning (artificial intelligence); statistical analysis; Queriosity; automated personalized data exploration system; autonomy; data retrieval; interactive user feedback; knowledge discovery; learning; personalized data robot; statistical properties; usability; Big data; Context; Data mining; Learning (artificial intelligence); Usability; Curious data systems; Data analysis; Data exploration; Data systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location
New York, NY
Print_ISBN
978-1-4673-7277-0
Type
conf
DOI
10.1109/BigDataCongress.2015.116
Filename
7207300
Link To Document