2026-05-18T14:21:15Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-2007102025-05-15T22:28:22Zoai_pmh:alloai_pmh:repo_items200710
https://hdl.handle.net/2286/R.2.N.200710
http://rightsstatements.org/vocab/InC/1.0/
http://creativecommons.org/licenses/by-nc-sa/4.0
2025-05
10 pages
Martinez, Sebastian
Gupta, Vivek
Bryan, Chris
Barrett, The Honors College
Computer Science and Engineering Program
This thesis introduces an intelligent database system that harnesses Natural Language Processing (NLP) and Machine Learning (ML) to enable seamless querying and visualization of sports data. Centered on the English Premier League—the top tier of English football—the system empowers users to interact with complex datasets through simple natural language queries. These queries are automatically translated into structured SQL commands, eliminating the need for technical expertise and making data retrieval more accessible.
In addition to flexible querying, the system supports dynamic data visualization, presenting results in user-specified formats such as tables, charts, or graphs. By integrating NLP and ML, the system streamlines the end-to-end process of data access, analysis, and presentation. This not only enhances the usability of sports data for analysts, researchers, and enthusiasts but also promotes data-driven exploration and insight generation. The proposed system represents a step toward democratizing sports analytics by bridging the gap between natural language understanding and structured data querying, enabling richer, more intuitive interactions with complex information.
Natural Language Processing
Artificial Intelligence
Machine learning
Data Visualization
SQL
Large Language Models (LLM)
Natural Language Processing for Intuitive Sports Data Querying and Visualization