CS Database Research¶
Faculty of Science, Ontario Tech University
Our group supports research in different aspects of data analytics and query processing platforms. This includes but not limited to:
Database technology such as novel indexing structures and query processing techniques
Applications of database systems in data science and related fields such as machine learning
Applications of statistical learning and machine learning in the context of data processing
Novel data models and their unique query processing challenges
Our missions are:
Advance the field of databases and bring benefits to governments and industry partners.
Provide high quality training environment for graduate and undergraduate students.
Ken Pu is an associate professor in Computer Science at Ontario Tech University. His expertise is in the general area of database systems and information retrieval. He is particularly interested in novel data processing techniques and applications of databases.
Ken received his PhD from University of Toronto in 2006 in Computer Science.
Limin is working on his master’s degree. His research area is intelligent search and self-organization of open data.
Andrei is working on his master’s degree. His research is extending relational databases with topic modeling. His work allows one to integrate SQL with novel text mining operators, and thus supporting complex text analytic pipelines seamlessly inside the relational database engine.
Jude is working on his master’s degree as a part-time student. His research is to apply deep learning techniques to code analysis in open source repositories. By combining information from both the source code as well as repository commit comments, Jude is hoping to train a family of neural networks to extract semantic knowledge about the relationship between bugs, source code, and the human developer.
Jude is co-supervised by Professor Jeremy Bradbury.
Michael successfully defended his thesis in January 2021. His thesis was on the extension of the relational data model to include first-order constraints as first class data objects. To do this, we had to extend the relational query language (SQL) to handle creation, transformation, and satisfaction of constraints. The result is a robust, powerful and performant constraint solver that works completely within a relational database system. Michael demonstrated the ease in modeling large scale constraint problems using the extended SQL, and also showed that the resulting constraint problem can be solved in reasonable time.