Automated Runtime Log Analysis to Evaluate System Performance; Math VS Experience: The Controversy of Data Analytics in the National Football League

Lampert, Will, School of Engineering and Applied Science, University of Virginia
Foley, Rider, EN-Engineering and Society, University of Virginia
Graham, Daniel, EN-Comp Science Dept, University of Virginia

The Cloud Custodian team at Capital One found that it had become extremely time consuming and unreasonable to sift through the millions of log files generated daily by their platform in order to identify system runtime issues. My team created an automated process that retrieves logs as they are generated and parses them for execution and failure data. Quantitative analysis is then performed on this data in order to identify trends in system performance. The results of this analysis are presented on a dashboard in a concise manner. The goal of this technology is to free up developers on the Cloud Custodian team to spend more time on meaningful and complex tasks. If successful, this technology will affect the existing workflows of the team and dynamics in how work is divided. While my technical topic addresses data analysis to automate a process within a technical team, my STS research has focused on data analysis within professional football. I used the Social Construction of Technology theory as a framework to analyze different stakeholders’ rhetoric about the current state of data analysis use in the National Football League. I am using Sentiment Analysis of NFL fan tweets and NFL press-conference transcripts in order to understand the attitudes of fans, media, and NFL personnel towards sports analysis. I expect that there will still be a strong divide within these groups, but that overall they will lean towards a positive sentiment. When considering both my technical and STS topics, it appears that data analysis is causing significant changes in all types of work environments.

BS (Bachelor of Science)
National Football League, University of Virginia, Data Analytics, Sentiment Analysis, Social Construction of Technology

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Daniel Graham
STS Advisor: Rider Foley

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