Michael Mior Headshot

Michael Mior

Assistant Professor

Department of Computer Science
Golisano College of Computing and Information Sciences

Office Hours
MW 11am-12pm
Office Location
Office Mailing Address
102 Lomb Memorial Drive Rochester, NY 14623-5608

Michael Mior

Assistant Professor

Department of Computer Science
Golisano College of Computing and Information Sciences


BS in Computing Science, University of Ontario Institute of Technology (Canada); MS in Computer Science, University of Toronto (Canada); Ph.D. in Computer Science, University of Waterloo (Canada)


Michael completed his Masters degree at the University of Toronto and received a PhD from the University of Waterloo. He joined RIT as an Assistant Professor in 2018. His research revolves around schema design and management and data integration for non-relational data.


Areas of Expertise

Select Scholarship

Published Conference Proceedings
Mior, Michael J. and Ken Q. Pu. "Semantic Data Understanding With Character Level Learning." Proceedings of the Information Reuse and Integration for Data Science. Ed. Chengcui Zhang, et al. Las Vegas, CA: IEEE, 2020. Web.
Mior, Michael J. and Kenneth Salem. "ReSpark: Automatic Caching for Iterative Applications in Apache Spark." Proceedings of the IEEE BigData 2020. Ed. Yixin Chen, Heiko Ludwig, and Yicheng Tu. Atlanta, GA: IEEE, 2020. Web.
Suárez-Otero, Pablo, et al. "Maintaining NoSQL Database Quality During Conceptual Model Evolution." Proceedings of the th International Workshop on Methods to Improve Big Data Science Projects. Ed. Jeffrey Saltz and Mary Magee Quinn. Atlanta, GA: IEEE, 2020. Web.

Currently Teaching

3 Credits
This course provides an introduction to the major concepts and techniques used in data mining of large databases. Topics include the knowledge discovery process; data exploration and cleaning; data mining algorithms; and ethical issues underlying data preparation and mining. Data mining projects, presentations, and a term paper are required.
3 Credits
This course provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. First, practical techniques used in exploratory data analysis and mining are introduced; topics include data preparation, visualization, statistics for understanding data, and grouping and prediction techniques. Second, approaches used to store, retrieve, and manage data in the real world are presented; topics include traditional database systems, query languages, and data integrity and quality. Case studies will examine issues in data capture, organization, storage, retrieval, visualization, and analysis in diverse settings such as urban crime, drug research, census data, social networking, and space exploration. Big data exploration and management projects, a term paper and a presentation are required. Sufficient background in database systems and statistics is recommended.
3 Credits
This course examines how database systems evolved to meet the workloads of modern applications. Limitations of relational databases led to NoSQL systems that are highly scalable and provide flexible data modeling but sacrifice important consistency properties. More recently, “NewSQL” data systems seek to understand and address fundamental scalability bottlenecks while maintaining relational database consistency. This course will describe shortcomings of relational databases for certain data management tasks and the specific challenges addressed by NoSQL and NewSQL database systems. Case studies will investigate both established and state-of-the-art systems. Students will critique and present existing work in the area and complete a research project individually or in teams that explores an outstanding problem in the area.
3 Credits
This course examines current topics in Data Management. This is intended to allow faculty to pilot potential new graduate offerings. Specific course details (such as prerequisites, course topics, format, learning outcomes, assessment methods, and resource needs) will be determined by the faculty member(s) who propose a specific topics course in this area. Specific course instances will be identified as belonging to the Data Management cluster, the Security cluster, or both clusters.

In the News

  • March 17, 2023

    RIT students Mohammed Raeesul Irfan Riaz Ahmed, Eric Karschner, and Quinn Tucker

    CS@RIT hosts regional programming competition

    CS@RIT recently hosted regional competitors of the International Collegiate Programming Contest (ICPC), with 84 registered teams from 19 universities competing. The top four universities will advance to the North America Championship, from which the top teams will advance to the World Finals. Two RIT teams performed well, placing 13th and 17th overall. The contest involves teams of up to three students solving problems within five hours, using a single computer.

  • November 18, 2019

    International Collegiate Programming Contest

    Over the past month, teams of RIT students have been competing in the International Collegiate Programming Contest. In addition to taking top places at the regional qualifier, RIT’s top team placed 7th in the regional competition.