Publication Date
2011
Document Type
Thesis
Committee Members
Ramakanth Kavaluru (Committee Member), Amit Sheth (Advisor), Krishnaprasad Thirunarayan (Committee Member)
Degree Name
Master of Science (MS)
Abstract
The emergence of dynamic information sources - like social, mobile and sensors, has led to ginormous streams of real time data on the web also called, the era of Big Data [1]. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. Gigaom article on Big data shows, how the total information generated by these dynamic information sources has completely surpassed the total storage capacity. Thus keeping in mind the problem of ever-increasing data, this thesis focuses on semantically integrating and analyzing multiple, multimodal, heterogeneous streams of weather data with the goal of creating meaningful thematic abstractions in real-time. This is accomplished by implementing an infrastructure for creating and mining thematic abstractions over massive amount of real-time sensor streams. Evaluation section shows 69% data reduction with this approach.
Page Count
80
Department or Program
Department of Computer Science
Year Degree Awarded
2011
Copyright
Copyright 2011, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.
