research-article

TaCLe: Learning Constraints in Tabular Data

Authors:

Sergey Paramonov,

Samuel Kolb,

Tias Guns,

Luc De RaedtAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 2511 - 2514

https://doi.org/10.1145/3132847.3133193

Published: 06 November 2017 Publication History

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Abstract

Spreadsheet data is widely used today by many different people and across industries. However, writing, maintaining and identifying good formulae for spreadsheets can be time consuming and error-prone. To address this issue we have introduced the TaCLe system (Tabular Constraint Learner). The system tackles an inverse learning problem: given a plain comma separated file, it reconstructs the spreadsheet formulae that hold in the tables. Two important considerations are the number of cells and constraints to check, and how to deal with multiple formulae for the same cell. Our system reasons over entire rows and columns and has an intuitive user interface for interacting with the learned constraints and data. It can be seen as an intelligent assistance tool for discovering formulae from data. As a result, the user obtains a spreadsheet that can automatically recompute dependent cells when updating or adding data.

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Cited By

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Kadıoğlu SPravin Dakle PUppuluri KPoliti RRaghavan PRallabandi SSrinivasamurthy R(2024)Ner4Opt: named entity recognition for optimization modelling from natural languageConstraints10.1007/s10601-024-09376-5Online publication date: 26-Nov-2024
https://doi.org/10.1007/s10601-024-09376-5
Bessiere CCarbonnel CDries AHebrard EKatsirelos GNarodytska NQuimper CStergiou KTsouros DWalsh T(2023)Learning Constraints through Partial QueriesArtificial Intelligence10.1016/j.artint.2023.103896(103896)Online publication date: Mar-2023
https://doi.org/10.1016/j.artint.2023.103896
Dakle PKadıoğlu SUppuluri KPoliti RRaghavan PRallabandi SSrinivasamurthy R(2023)Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural LanguageIntegration of Constraint Programming, Artificial Intelligence, and Operations Research10.1007/978-3-031-33271-5_20(299-319)Online publication date: 23-May-2023
https://doi.org/10.1007/978-3-031-33271-5_20
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TaCLe: Learning Constraints in Tabular Data

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Published In

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

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Association for Computing Machinery

New York, NY, United States

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Published: 06 November 2017

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CIKM '17

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CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Kadıoğlu SPravin Dakle PUppuluri KPoliti RRaghavan PRallabandi SSrinivasamurthy R(2024)Ner4Opt: named entity recognition for optimization modelling from natural languageConstraints10.1007/s10601-024-09376-5Online publication date: 26-Nov-2024
https://doi.org/10.1007/s10601-024-09376-5
Bessiere CCarbonnel CDries AHebrard EKatsirelos GNarodytska NQuimper CStergiou KTsouros DWalsh T(2023)Learning Constraints through Partial QueriesArtificial Intelligence10.1016/j.artint.2023.103896(103896)Online publication date: Mar-2023
https://doi.org/10.1016/j.artint.2023.103896
Dakle PKadıoğlu SUppuluri KPoliti RRaghavan PRallabandi SSrinivasamurthy R(2023)Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural LanguageIntegration of Constraint Programming, Artificial Intelligence, and Operations Research10.1007/978-3-031-33271-5_20(299-319)Online publication date: 23-May-2023
https://doi.org/10.1007/978-3-031-33271-5_20
Ben Said AMouhoub M(2022)A Constraint Satisfaction Problem (CSP) Approach for the Nurse Scheduling Problem2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022250(790-795)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022250
Contreras-Ochando LFerri CHernández-Orallo J(2021)AUTOMAT[R]IX: learning simple matrix pipelinesMachine Learning10.1007/s10994-021-05950-7Online publication date: 13-Apr-2021
https://doi.org/10.1007/s10994-021-05950-7
Contreras-Ochando LFerri CHernández-Orallo J(2020)Automating Common Data Science Matrix TransformationsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-43823-4_2(17-27)Online publication date: 28-Mar-2020
https://doi.org/10.1007/978-3-030-43823-4_2

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