Abstract
This demonstration presents HadoopViz; an extensible MapReduce-based system for visualizing Big Spatial Data. HadoopViz has two main unique features that distinguish it from other techniques. (1) It provides an extensible interface that allows users to visualize various types of data by defining five abstract functions, without delving into the details of the MapReduce algorithms. We show how it is used to create four types of visualizations, namely, scatter plot, road network, frequency heat map, and temperature heat map. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase approach of partitioning, rasterize, and merging. HadoopViz generates single and multi-level images, where the latter allows users to zoom in/out to get more/less details. Both types of images are generated with a very high resolution using the extensible and scalable framework of HadoopViz.
| Original language | English (US) |
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| Title of host publication | Proceedings of the VLDB Endowment |
| Editors | Christophe Claramunt, Simonas Saltenis, Ki-Joune Li |
| Publisher | Association for Computing Machinery |
| Pages | 1896-1899 |
| Number of pages | 4 |
| Volume | 8 |
| Edition | 12 12 |
| 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: Sep 11 2006 → Sep 11 2006 |
Other
| Other | 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 9/11/06 → 9/11/06 |
