research-article

From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry

Authors:

Nitay Calderon,

Avigdor GalAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4114 - 4124

https://doi.org/10.1145/3459637.3481921

Published: 30 October 2021 Publication History

Abstract

Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that data is readily available (the big data phenomenon), is oftentimes challenged by the need to effectively acquire quality data under severe constraints. In this paper we propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data. The proposed methodology extends existing active learning methods to effectively solve regression-based learning problems and may serve settings where data acquisition requires excessive resources in the physical world. We further suggest a set of qualitative measures to analyze learners performance. The proposed methodology is demonstrated using an actual application in the milk industry, where milk is gathered from multiple small milk farms and brought to a dairy production plant to be processed into cottage cheese.

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Cited By

Peroni LGorinsky STashtarian FTimmerer C(2023)Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming QualityProceedings of the ACM on Networking10.1145/36291391:CoNEXT3(1-27)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629139
Genossar BGal AShraga R(2023)The Battleship Approach to the Low Resource Entity Matching ProblemProceedings of the ACM on Management of Data10.1145/36267111:4(1-25)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626711
Zhou HFang YGao H(2023)Using Active Learning for the Computational Design of Polymer Molecular Weight DistributionsACS Engineering Au10.1021/acsengineeringau.3c000564:2(231-240)Online publication date: 25-Dec-2023
https://doi.org/10.1021/acsengineeringau.3c00056

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From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
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Guido Zuccon
The University of Queensland, Australia
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Program Chairs:
J. Shane Culpepper
RMIT University, Australia
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Zi Huang
The University of Queensland, Australia
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Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Cited By

Peroni LGorinsky STashtarian FTimmerer C(2023)Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming QualityProceedings of the ACM on Networking10.1145/36291391:CoNEXT3(1-27)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629139
Genossar BGal AShraga R(2023)The Battleship Approach to the Low Resource Entity Matching ProblemProceedings of the ACM on Management of Data10.1145/36267111:4(1-25)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626711
Zhou HFang YGao H(2023)Using Active Learning for the Computational Design of Polymer Molecular Weight DistributionsACS Engineering Au10.1021/acsengineeringau.3c000564:2(231-240)Online publication date: 25-Dec-2023
https://doi.org/10.1021/acsengineeringau.3c00056

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