From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry
Published in CIKM, 2021
Recommended citation: Roee Shraga, Gil Katz, Yael Badian, Nitay Calderon and Avigdor Gal https://doi.org/10.1145/3459637.3481921
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.bibtex
@inproceedings{shraga2021limited,
author = {Roee Shraga and
Gil Katz and
Yael Badian and
Nitay Calderon and
Avigdor Gal},
editor = {Gianluca Demartini and
Guido Zuccon and
J. Shane Culpepper and
Zi Huang and
Hanghang Tong},
title = {From Limited Annotated Raw Material Data to Quality Production Data:
{A} Case Study in the Milk Industry},
booktitle = {CIKM '21: The 30th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Queensland, Australia, November
1 - 5, 2021},
pages = {4114--4124},
publisher = ,
year = {2021},
url = {https://doi.org/10.1145/3459637.3481921},
doi = {10.1145/3459637.3481921},
timestamp = {Tue, 16 Aug 2022 23:04:38 +0200},
biburl = {https://dblp.org/rec/conf/cikm/ShragaKBCG21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}