4 papers
Learning Over Dirty Data Without Cleaning
Jose Picado, John Davis, Arash Termehchy +1
Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values…
Usable & Scalable Learning Over Relational Data With Automatic Language Bias
Jose Picado, Arash Termehchy, Sudhanshu Pathak +3
Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate defini…
Schema Independent Relational Learning
Jose Picado, Arash Termehchy, Alan Fern +1
Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorit…
Representation Independent Analytics Over Structured Data
Yodsawalai Chodpathumwan, Jose Picado, Arash Termehchy +2
Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be repre…