Abstract
Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of rapidly generating approximate visualizations while preserving crucial visual properties of interest to analysts. Our primary focus will be on sampling algorithms that preserve the visual property of ordering; our techniques will also apply to some other visual properties. For instance, our algorithms can be used to generate an approximate visualization of a bar chart very rapidly, where the comparisons between any two bars are correct. We formally show that our sampling algorithms are generally applicable and provably optimal in theory, in that they do not take more samples than necessary to generate the visualizations with ordering guarantees. They also work well in practice, correctly ordering output groups while taking orders of magnitude fewer samples and much less time than conventional sampling schemes.
Original language | English |
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Title of host publication | Proceedings of the VLDB Endowment |
Editors | Simonas Saltenis, Ki-Joune Li, Christophe Claramunt |
Publisher | Association for Computing Machinery |
Pages | 521-532 |
Number of pages | 12 |
Volume | 8 |
Edition | 5 5 |
DOIs | |
State | Published - 2015 |
Event | 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of Duration: 11 Sep 2006 → 11 Sep 2006 |
Conference
Conference | 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 11/09/06 → 11/09/06 |