Masters Theses

Date of Award

12-2020

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Major Professor

James S. Plank

Committee Members

James S. Plank, Chao Tian, Michael R. Jantz

Abstract

Data corruption and data loss create huge problems when they occur, so naturally safeguards are usually in place to recover lost data. This often involves allowing less space for data in order to allow space for an encoding that can be used to recover any data that might be lost. The question arises, then, about how to most efficiently implement these safeguards with respect to storage, network bandwidth, or some linear combination of those two things. This work has two main goals for the information theory community: to produce an intuitive-to-use problem description parser that facilitates research in the area, and to demonstrate the parser’s utility.

To these ends, I completed three objectives: First, I hardened an existing problem description parser to sanitize input. Then, after that code was released open-source to the community, I rewrote the parser using C++, making it more efficient and more agreeable to a Python-based workflow. Once this new parser had also been open-sourced, I wrote a program to generate problem description files to show how to use the parser and how useful it can be. This paper will give an account of the results of those three objectives.

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