Abstract. Most efforts towards analyzing Big Data assume data parallel applications and handle the large volumes of data using Hadoop-like systems. However, Big Data is actually characterized by the 4V's -Volume, Variety, Velocity and Veracity. We propose a Big Data Stack and analytics solution that particularly caters to this important problem of addressing Variety and Velocity aspects of data by exploiting inherent relationship among data elements. A unique approach that we propose to take is to integrate and model the data using non-planar graphs and discover new insights through sophisticated graph analytics techniques. We have integrated the stack with an intuitive visualization toolkit that enables focused exploration of data, through query and selective visualization -which will be demonstrated.
The steady growth of data from social networks has resulted in wide-spread research in a host of application areas including transportation, health-care, customer-care and many more. Owing to the ubiquity and popularity of transportation (more recently) the growth in the number of problems reported by the masses has no bounds. With the advent of social media, reporting problems has become easier than before. In this paper, we address the problem of efficient management of transportation related woes by leveraging the information provided by social media sources such as -Facebook, Twitter etc. We develop techniques for viral event detection, identify frequently co-occurring problem patterns and their root-causes and mine suggestions to solve the identified problems. We predict the occurrence of different problems, (with an accuracy of ≈ 80%) at different locations and times leveraging the analysis done above along with weather information and news reports. In addition, we design a feature-packed visualization that significantly enhances the ability to analyse data in real-time.
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