Adv Topics: Big Data Visualization

Volume visualization is used to understand large amounts of data, in other words, big data, where it can be processed on a server or in the cloud and rendered onto a hand-held device to allow for the end user to interact with the data (Johnson, 2011). Tanahashi, Chen, Marchesin and Ma (2010), define a framework for creating an entirely web-based visualization interface (or web application), which is leveraging the cloud computing environment. The benefit of using this type of interface is that there is no need to download or install the software, but that the software can be accessed through any mobile device with a connection to the internet. A scientist can then use visualization and multimedia functionally as a tool to enhance their thinking and understanding of current problems, from understanding the 3-dimentional structure of DNA or the 3-dimentional structure of a hurricane (Minelli, Chambers, & Dhiraj, 2013; Johnson, 2011).


Figure 1: Hurricane Joaquin’s 3-dimensional rendering of its rain structure from the NASA-enhanced infrared satellite image and GPM data. Adapted from NASA (September 29, 2015). This image shows snow particles in the storm’s anvil, but also shows that significant amounts of heat being released by the storm’s core, which is driving the circulation of the storm and providing the storm energy required for further intensification.

McNurlin, Sprague, and Bui (2008), stated that the ideal web-based visualization tool would have simplified operations, allows for reusable templates, rapid deployment, multilingual support, and allows for control over the creation, update, access, customization, and destruction. Tanahashi et al. (2010) had proposed a web-based visualization framework to have a:

(a)   preprocessing phase = data is collected, indexed, stored

(b)   interface = end-user connecting to data in the databases and makes the request for processing and modifying the data

(c)   processing phase = a set of images, video, 3-dimensional renderings are returned per request

(d)   modification phase = end-user can request further modifications

(e)   reprocessing phase = a set of images, video, 3-dimensional renderings are returned per request, which goes into an iterative loop between parts d and e until a final product is rendered to the end user

It was designed for all people to use, and go by the philosophy that “Knowledge should be openly assessable to the broader community.” (Tanahashi et al., 2010, Sakr, 2014). Performance bottlenecks of the above Tanahashi et al. (2010) framework include difficulty with dealing with different data formats, different rendering algorithms, transferring cloud-based data rendering onto the web interface, and organization of big data for efficient retrieval. With the goal of any visualization is to be providing the right user the right information in their preferred or a suggested rendering these bottlenecks must be addressed (McNurlin et al., 2008). Thus, the algorithms can be indexed to allow for classifying the algorithms ‘properties of aesthetics or analytical significance, which can be searched for by an end-user with a search bar (Tanahashi et al., 2010).

Subsequently, it is proposed that using and indexing metadata can resolve the issues of data organization (Tanahashi et al., 2010). Data transfer issues could be mitigated by minimizing the amount of data-in-motion via a MapReduce paradigm (Tanahashi et al., 2010, Sakr, 2014). In the MapReduce paradigm, the mapper’s process and render the data and reducers create the final composition of the data (Tanahashi et al., 2010).


  • Johnson, C. (2011) Visualizing large data sets. TEDx Salt Lake City. Retrieved from
  • McNurlin, B., Sprague, R., & Bui, T. (2008) Information Systems Management, (8th ed.). Pearson Learning Solution. VitalBook file.
  • Minelli, M., Chambers, M., & Dhiraj, M. (2013) Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.
  • NASA (2015). October 02, 2015 – Update #1 – A 3-D Look at Hurricane Joaquin from NASA’s GPM Satellite.
  • Sakr, S. (2014). Large Scale and Big Data, (1st ed.). Vitalbook file.
  • Tanahashi, Y., Chen, C., Marchesin, S., & Ma, K. (2010). An interface design for future cloud-based visualization services. Proceedings of 2010 IEEE Second International Conference on Cloud Computing Technology and Service, 609–613. doi: 10.1109/CloudCom.2010.46

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