research-article

A Relational Framework for Classifier Engineering

Authors:

Benny Kimelfeld,

Christopher RéAuthors Info & Claims

ACM SIGMOD Record, Volume 47, Issue 1

Pages 6 - 13

https://doi.org/10.1145/3277006.3277009

Published: 10 September 2018 Publication History

Abstract

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 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.

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Cited By

ten Cate BFunk MChristoph Jung JLutz C(2024)Fitting Algorithms for Conjunctive QueriesACM SIGMOD Record10.1145/3641832.364183452:4(6-18)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3641832.3641834
Ten Cate BDalmau VFunk MLutz CGeerts FNgo HSintos S(2023)Extremal Fitting Problems for Conjunctive QueriesProceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3584372.3588655(89-98)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3584372.3588655
Carmeli NZeevi SBerkholz CConte AKimelfeld BSchweikardt N(2022)Answering (Unions of) Conjunctive Queries using Random Access and Random-Order EnumerationACM Transactions on Database Systems10.1145/353105547:3(1-49)Online publication date: 25-Jun-2022
https://dl.acm.org/doi/10.1145/3531055

A Relational Framework for Classifier Engineering
1. Computing methodologies

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Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 47, Issue 1

March 2018

45 pages

ISSN:0163-5808

DOI:10.1145/3277006

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Zackary Ives
University of Pennsylvania
,
Anastasios Kementsietsidis
Google Research
,
Jeffrey Naughton
University of Wisconsin-Madison
,
Frank Neven
Hasselt University
,
Olga Papaemmanoui
Brandeis Univesity
,
Aditya Parameswaran
University of Illinois
,
Alkis Simitsis
HP Labs
,
Wang-Chiew Tan
University of California Santa Cruz
,
Pinar Tözü
IBM Almaden Research Center
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University

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Copyright © 2018 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2018

Published in SIGMOD Volume 47, Issue 1

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Cited By

ten Cate BFunk MChristoph Jung JLutz C(2024)Fitting Algorithms for Conjunctive QueriesACM SIGMOD Record10.1145/3641832.364183452:4(6-18)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3641832.3641834
Ten Cate BDalmau VFunk MLutz CGeerts FNgo HSintos S(2023)Extremal Fitting Problems for Conjunctive QueriesProceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3584372.3588655(89-98)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3584372.3588655
Carmeli NZeevi SBerkholz CConte AKimelfeld BSchweikardt N(2022)Answering (Unions of) Conjunctive Queries using Random Access and Random-Order EnumerationACM Transactions on Database Systems10.1145/353105547:3(1-49)Online publication date: 25-Jun-2022
https://dl.acm.org/doi/10.1145/3531055

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