{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/45094"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/45094","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Schema-Driven Exceptional Query Generation for Soccer Analytics using Relational and Graph Databases","abstract":"This thesis explores interesting fact analysis and how it could be implemented through automatically generating and executing exceptional queries using relational and graph databases. A schema-driven framework loading soccer data on a weekly basis was introduced using the Statsbomb open-source dataset. This framework automatically generates thousands of queries, executes them efficiently, and retrieves relevant facts for any entity of interest. A key contribution of this thesis was the development of a fully automatic approach for generating exceptional queries which are based solely on the underlying database schema. To go along with the full-season analysis of the data, the inclusion of rolling-window analyses to derive facts from only specific portions of a season was another contribution made in this thesis. The evaluation of this pipeline was performed using one relational database and two graph databases, comparing both the efficiency and maintenance overhead of these different databases.","abstract_html":"This thesis explores interesting fact analysis and how it could be implemented through automatically generating and executing exceptional queries using relational and graph databases. A schema-driven framework loading soccer data on a weekly basis was introduced using the Statsbomb open-source dataset. 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