Supporting Large-scale Geographical Visualization in a Multi-granularity Way

Li, M, Bao, Z, Choudhury, F and Sellis, T 2018, 'Supporting Large-scale Geographical Visualization in a Multi-granularity Way', in Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018), California, United States, 5-9 February 2018, pp. 767-770.


Document type: Conference Paper
Collection: Conference Papers

Title Supporting Large-scale Geographical Visualization in a Multi-granularity Way
Author(s) Li, M
Bao, Z
Choudhury, F
Sellis, T
Year 2018
Conference name WSDM 2018: Web Search and Data Mining
Conference location California, United States
Conference dates 5-9 February 2018
Proceedings title Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 767
End page 770
Total pages 4
Abstract Urban data (e.g., real estate data, crime data) often have multiple attributes which are highly geography-related. With the scale of data increases, directly visualizing millions of individual data points on top of a map would overwhelm users' perceptual and cognitive capacity and lead to high latency when users interact with the data. In this demo, we present ConvexCubes, a system that supports interactive visualization of large-scale multidimensional urban data in a multi-granularity way. Comparing to state-of-the-art visualization-driven data structures, it exploits real-world geographic semantics (e.g., country, state, city) rather than using grid-based aggregation. Instead of calculating everything on demand, ConvexCubes utilizes existing visualization results to efficiently support different kinds of user interactions, such as zooming & panning, filtering and granularity control. Our system can be accessed at http://115.146.89.158/ConvexCubes/.
Subjects Database Management
Keyword(s) Visualization
efficiency
Copyright notice Copyright © 2018 by the Association for Computing Machinery, Inc. (ACM). Permission
ISBN 9781450355810
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