Overview
In the design of analytical procedures and machine-learning solutions, a critical and time-consuming task is that of feature engineering, for which various recipes and tooling approaches have been developed. We develop and analyze database foundations for feature engineering, with the goal of opening the way to research and techniques to assist developers by utilizing the database’s modeling and understanding of data and queries, and by deploying the well studied principles of database management.
People
Selected Publications
Pablo Barceló, Alexander Baumgartner, Victor Dalmau and Benny Kimelfeld, "Regularizing Conjunctive Features for Classification", To appear in PODS, 2019
Benny Kimelfeld, Christopher Ré,
"A Relational Framework for Classifier Engineering", SIGMOD Record 47(1): 6-13 (2018)
abstractpaperIn the design of analytical procedures and machine-learning solutions, a critical and time-consuming task is that of feature engineering, for which various recipes and tooling approaches have been developed. We embark on the establishment of database foundations for feature engineering. Specifically, we propose a formal framework for classification in the context of a relational database. The goal of this framework is to open the way to research and techniques to assist developers with the task of feature engineering by utilizing the database’s modeling and understanding of data and queries, and by deploying the well studied principles of database management. We demonstrate the usefulness of the framework by formally defining key algorithmic challenges and presenting preliminary complexity results.
