This paper introduces HadoopViz; a MapReduce-based framework for visualizing big spatial data. HadoopViz has three unique features that distinguish it from other techniques. (1) It exposes an extensible interface which allows users to define a new visualization types, e.g., scatter plot, road network, or heat map, by defining five abstract functions, without delving into the implementation details of the MapReduce algorithms. As it is open source, HadoopViz allows algorithm designers to focus on how the data should be visualized rather than performance or scalability issues. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase technique, partition-plot-merge. (3) HadoopViz provides a smoothing functionality which can fuse nearby records together as the image is plotted. This makes it capable of generating more types of images with high quality as compared to existing work. Experimental results on real datasets of up to 14 Billion points show the extensibility, scalability, and efficiency of HadoopViz to handle different visualization types of spatial big data.
['Ahmed Eldawy', 'Mohamed F. Mokbel', 'Christopher Jonathan']
HadoopViz: A MapReduce framework for extensible visualization of big spatial data