Real-time Stream Reasoning over Large Scale Data

Big-data is the expression used to describe large data sets, which are complex and require analysis techniques for extraction and description. The availability of big data and efficient methods of analysis have positively influenced different business activities due to the accuracy of analysis; promoting superior business decision-making. Analysis of big data is done using tools and techniques in various domains such as stream and graph processing, semantic web, logic programming. The use of big data has grown exponentially in the field of business, and the proposed project aims at providing knowledge on the different tools and techniques that are used to define, analyze and categorize large volumes of data.

In this thesis, I have documented my research in Large-scale data processing. I have identified specific tools, which are currently being adopted widely in academia and industry as well for processing data. This dissertation will present the data obtained and establish plans towards designing some generic framework or algorithms for data processing on Apache Storm while reducing latency. Additionally, I will demonstrate an approach for the implementation of recursion in real-time reasoning.