Pioneering Past and Bright of Impactful Research and Scholarly Achievements

“EXCELLENCE THROUGH KNOWLEDGE” P A G E 83 section IV, we present our proposal and discuss possible use cases (section V). Section VI presents related work, followed by future work (section VII) and conclusion (section VIII). In the most basic of terms, Big Data refers to the collection, processing and analysis of extremely large data sets usually at scale of petabyte limits and beyond, especially for large scale University laboratory environments like ours. The magnitude and complexity of these data sets are very significant by way of volume that it becomes extremely difficult to process using contemporary database management tools or traditional data processing applications. For Big Data systems, there are normally challenges relating to capture, curation, storage, search transfer analysis and visualization of these data sets. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44 times larger between 2009 and 2020. The rise in prominence of Big Data stems from the value that can be extracted – correlations that can spot consumer and or business trends, insight that can be used to help with disease prevention, crime abatement, traffic routing, security breach detection, product enhancement, supply chain optimization, among others. Formally, there is no official consensus on the scientific definition of Big Data. Gartner, and the rest of the computing industry, formally use the “3Vs” model as the basis for describing Big Data [5]. In 2012, Gartner presented its updated definition, which the industry’s de facto standard and states that “Big Data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization” [6]. In recent times, the dimensions of value and veracity have been added to the list of important characteristics of Big Data. Thus, the “5Vs” model is currently the acceptable standard for Big Data. Editor’s Note: This paper was published in full in the Proceedings of the IEEE International Conference on Big Data (Big Data), 2014.