TY - CHAP
T1 - A demonstration of hadoopviz
T2 - 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
AU - Eldawy, Ahmed
AU - Mokbel, Mohamed F.
AU - Jonathan, Christopher
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84953881379&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953881379&partnerID=8YFLogxK
U2 - 10.14778/2824032.2824095
DO - 10.14778/2824032.2824095
M3 - Chapter
AN - SCOPUS:84953881379
T3 - Proceedings of the VLDB Endowment
SP - 1896
EP - 1899
BT - Proceedings of the VLDB Endowment
PB - Association for Computing Machinery
Y2 - 11 September 2006 through 11 September 2006
ER -