Final Examination Schedule


Summer 2017


Monday, July 10

Mark Cafaro

Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
5:45 P.M.; DISC 464
A Web System in Support of Wide-Field Ethnography

Wide-Field Ethnography (WFE) is an emergent approach of gathering and collaboratively analyzing multi-modal, multi-stream datasets of physical-cyber-social systems. In contrast to many quantitative approaches, WFE aims to capture and preserve the richness of people’s collaboration by using multiple video cameras, microphones, and other sensors to widely and extensively collect ethnographic materials. It is thought that sharing such rich datasets among an interdisciplinary team of researchers may support more comprehensive analysis of complex systems.

However, while studies are ongoing to explore these possibilities, WFE is currently hindered by the sheer size and complexity of its datasets. One gathered in 2014, for example, contains six terabytes of data across 114 thousand files. Predictions say that by 2025 these datasets will grow to contain petabytes of data across significantly more files. Present systems lack efficient means to share datasets of this size and complexity among distributed teams of researchers. Further, present systems lack efficient means to help researchers understand, navigate, browse, filter, annotate, and analyze these datasets in a collaborative manner.

This report details the requirements, design, and preliminary implementation of a web system to help address these needs. The primary goals of this system are: (1) to provide a central repository for secure, shared, access and storage of WFE datasets, and (2) to facilitate development of software tools for collaborative analysis of these datasets. Key features of this system include dataset hosting and management, data querying and filtering, provenance recording, and a web application programming interface.

Tuesday, August 8

Delmar B. Davis

Chair: Dr. Hazeline Asuncion
Candidate: Master of Science in Computer Science & Software Engineering
1:00 P.M.; DISC 464
Data Provenance for Multi-Agent Models in a Distributed Memory

Multi-agent systems (MAS) reveal emergent collective behavior of individual entities in social, biological, economic, network, and physical systems.  Applied to MAS, data provenance can support agent-based modeling by explaining individual agent behavior.  However, there is no provenance support for MAS in a distributed setting.  The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating agent-based models (ABM) at fine granularity, as multi-agent models (MAMs), where agents and spatial data are shared application resources in a distributed memory.  This Master’s thesis evaluates the adequacy and performance of ProvMASS, a novel approach to capture MAM provenance in a distributed memory.  Results indicate that an adaptive and scalable variation adequately explains coordination of distributed shared resources, simulation logic, and agent behavior with limited overhead.

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