Data Unity Lab

The Data Unity Lab at the Rochester Institute of Technology seeks to develop algorithms, techniques, and tools to improve understanding and simplify analysis of semi-structured data.

Data Unity Lab
Michael Mior

Michael Mior

Assistant Professor

Rochester Institute of Technology

Lab Director

Michael Mior is an Assistant Professor in the Data Science cluster at the Rochester Institute of Technology. His research focuses on data integration and understanding for non-relational data. The primary goal of his research is to develop tools and techniques to make diverse data sources easier to analyze.

Research interests
  • NoSQL Databases
  • Data Integration
  • Open Data
  • Semi-structured Data
  • Semantic Type Analysis
Education
  • PhD in Computer Science, 2018

    University of Waterloo

  • MSc in Computer Science, 2011

    University of Toronto

  • BSc in Computing Science, 2009

    University of Ontario Institute of Technology

Meet the Team

Grad Students

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Justin Namba

Rochester Institute of Technology

PhD Student, NRT Trainee

Research interests
JSON schema discovery

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Shuang Wei

Rochester Institute of Technology

PhD Student

Research interests
Semantic type inference

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Aryan Jha

Rochester Institute of Technology

Student Developer

Collaborators

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Pablo Suárez-Otero

University of Oviedo

Postdoc

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Ken Pu

Ontario Tech University

Associate Professor

Awards and Grants

Provost’s Learning Innovation Grant
Students funded to work on a tool for interactive exploration of relational database query processing.
Seed Funding Award

Recent Publications

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(2023). CoDEvo: Column family database evolution using model transformations. Journal of Systems and Software.

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(2022). Learning from Uncurated Regular Expressions. Computing Research Repository.

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(2021). How Inclusive are We?. SIGMOD Record.

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(2021). Fast Discovery of Nested Dependencies on JSON Data. Computing Research Repository.

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(2021). An Integrated Approach for Column-Oriented Database Application Evolution Using Conceptual Models. Advances in Conceptual Modeling - ER 2021 Workshops CoMoNoS, EmpER, CMLS, St. John’s, NL, Canada, October 18-21, 2021, Proceedings.

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(2020). Maintaining NoSQL Database Quality During Conceptual Model Evolution. MIDP.

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(2019). Semantic JSON Generation.

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(2018). Renormalization of NoSQL Database Schemas. ER.

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(2017). Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources. SIGMOD.

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(2017). NoSE: Schema Design for NoSQL Applications. TKDE.

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(2016). NoSE: Schema design for NoSQL applications. ICDE.

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