att_abstract={{From economics to sports to entertainment and social media, ranking
objects according to some notion of importance is a fundamental tool
we humans use all the time to better understand our world. With the
ever-increasing amount of user-generated content found online,
"what's trending" is now a commonplace phrase that tries to capture
the zeitgeist of the world by ranking the most popular microblogging
hashtags in a given region and time.  However, before we can
understand what these rankings tell us about the world, we need to be
able to more easily create and explore them, given the significant
scale of today's data.  In this paper, we describe the computational
challenges in building a real-time visual exploratory tool for finding
top-ranked objects; build on the recent work involving in-memory and
rank-aware data cubes to propose TopKube: a data structure that
answers top-k queries up to one order of magnitude faster than
previous state-of-the-art; demonstrate the usefulness of our methods
using a set of real-world, publicly available datasets; and provide a
new set of benchmarks for other researchers to validate their methods
and compare to our own.
	att_authors={ll447y, jk140f},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2016. {{, Volume PP}}{{, Issue 99}}{{, 2017-02-17}}{{, https://doi.org/10.1109/TVCG.2017.2671341}}
	att_tags={Data cube,  data structures,  interactive exploration,  ranking},
	author={Lauro Lins and James Klosowski and Fabio Miranda and Claudio Silva},
	institution={{IEEE TVCG}},
	title={{TopKube: A Rank-Aware Data Cube for Real-Time Exploration of Spatiotemporal Data}},