Presentation Title

Big Data Reduction: Lessons Learned from Analyzing One Billion Dota 2 Matches

Format of Presentation

Poster to be presented the Friday of the conference

Abstract

The availability of large datasets of player choices in popular online computer games presents an opportunity to identify sudden changes in choice patterns and explore what factors may contribute to such changes. However, the size and scope of large datasets raises big data reduction problems. This study uses a large dataset of over one billion games played in the online computer game Dota 2 as a starting point to examine possible solutions applicable to individual researchers with limited computational resources.

Department

Computing Science

Faculty Advisor

Mila Kwiatowska

This document is currently not available here.

Share

COinS
 

Big Data Reduction: Lessons Learned from Analyzing One Billion Dota 2 Matches

The availability of large datasets of player choices in popular online computer games presents an opportunity to identify sudden changes in choice patterns and explore what factors may contribute to such changes. However, the size and scope of large datasets raises big data reduction problems. This study uses a large dataset of over one billion games played in the online computer game Dota 2 as a starting point to examine possible solutions applicable to individual researchers with limited computational resources.