TY - JOUR
T1 - Big Data Life Cycle in Shop-Floor-Trends and Challenges
AU - Pulikottil, Terrin
AU - Estrada-Jimenez, Luis A.
AU - Abadia, José Joaquín Peralta
AU - Carrera-Rivera, Angela
AU - Torayev, Agajan
AU - Rehman, Hamood Ur
AU - Mo, Fan
AU - Nikghadam-Hojjati, Sanaz
AU - Barata, José
N1 - info:eu-repo/grantAgreement/EC/H2020/814078/EU#
Publisher Copyright:
© 2013 IEEE.
PY - 2023/3/6
Y1 - 2023/3/6
N2 - Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.
AB - Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.
KW - Big data
KW - data life cycle
KW - intelligent manufacturing
KW - literature review
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85149870423&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3253286
DO - 10.1109/ACCESS.2023.3253286
M3 - Review article
AN - SCOPUS:85149870423
SN - 2169-3536
VL - 11
SP - 30008
EP - 30026
JO - IEEE Access
JF - IEEE Access
ER -