ORCID Identifier(s)

0000-0002-2308-5137

Graduation Semester and Year

2020

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Ramez Elmasri

Abstract

Improving video games can be exponentially more efficient by utilizing big data. Big data plays a big part in modern gaming, especially for multiplayer games like poker, first person shooter games. Utilizing the data gathered from video games can be used in ways that will improve the player experience massively and can be eye-opening to find issues, player pattern, and improve the game in ways that will be hard to pin point without gathering data of how the players are playing the game. However, while big data is being utilized in multiplayer games, it’s not utilized as much in single player games where the story or action is the main focus of the game. This thesis proposes ways of using big data in single-player story-driven games. There is a lot of potential in taking advantage of the data gathered by players in single player games to improve future single player games. The thesis will cover multiple topics regarding how to improve single player games step by step, starting with what type of video games will be using as an example for the hypothesis, what type of information will be extracted, how to use the data gathered to improve the game, and tools used to develop the game and gather data.

Keywords

Data, Improvements, Insight, Analysis

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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