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 :
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