This paper documents a study of the relationship between heads up display (HUDs) customization and player performance. Additional measures capture satisfaction and prior gaming experience. The goal of this study was to develop a framework on which future Human Systems Engineering studies could create games that are tailor made to examine a given area of interest. This study utilized a two-by-two design, where participants play a two-dimensional (2D) platformer game with a mechanic that incentivizes attention to the HUD. This study successfully developed a framework and was moderately successful in uncovering limitations and demonstrating areas for improvement in follow-on studies. Specifically, this study illuminated issues with the low amount of usable data caused by design issues, participant apathy, and reliance on self-reporting data collection. Extensions of this study can utilize this framework and should look to recruit beyond crowdsourcing platforms, collect more diverse data, reduce participant effort, and address other considerations that were found during execution.

As Big Data becomes more relevant, existing grouping and clustering algorithms will need to be evaluated for their effectiveness with large amounts of data. Previous work in Similarity Grouping proposes a possible alternative to existing data analytics tools, which acts as a hybrid between fast grouping and insightful clustering. We, the SimCloud Team, proposed Distributed Similarity Group-by (DSG), a distributed implementation of Similarity Group By. Experimental results show that DSG is effective at generating meaningful clusters and has a lower runtime than K-Means, a commonly used clustering algorithm. This document presents my personal contributions to this team effort. The contributions include the multi-dimensional synthetic data generator, execution of the Increasing Scale Factor experiment, and presentations at the NCURIE Symposium and the SISAP 2019 Conference.