Publication Date
2020
Document Type
Thesis
Committee Members
Amit Sheth, Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Valerie Shalin, Ph.D. (Committee Member)
Degree Name
Master of Science in Computer Engineering (MSCE)
Abstract
Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide a way to combine (interlink and integrate) data streams in different modalities in a meaningful way, with the in-depth use case of flood events. The DisasterRecord project was originally developed as a demo to showcase the efforts of the team at Kno.e.sis in the area of combining and analyzing multimodal data for the IBM CallForCode challenge in 2018. This thesis represents extensive follow-on work in the areas of deployability, flexibility, and reliability. Specific topics addressed are: a method that utilizes current technologies to easily deploy into cloud infrastructure; the modifications made to add flexibility to add and modify the multimodal analysis pipeline; and reliability improvements to make it a stable and reliable system.
Page Count
47
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2020
Copyright
Copyright 2020, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
