FDepHunter

Summary: FDepHunter is a novel and interactive method for dependency discovery that augments an Armstrong relation with additional tuples. It relies on artificially generated negative examples that emulate real-world tuples to help expose fake FDs, together with domain experts to confirm that positive examples indeed reflect the characteristics of the original dataset.

A separate website is maintained at https://fdephunter.mmcatdb.com.

Maintained by

Pavel Koupil

Assistant professor

Latest publications

FDepHunter: Harnessing Negative Examples to Expose Fakes and Reveal Ghosts (2025)

Research topics

Multi-model databases

Relatively recently emerged NoSQL and other modern database management systems allowed to deploy databases based on other logical models than just the traditional relational. Unfortunately, the number of available models, formats, implementations, and query languages the existing systems exploit, often proprietary, is not sustainable from a longer perspective. Therefore we focus on various aspects of efficient and unified management of multi-model data, including conceptual modeling, schema inference, unified querying, or evolution management.