TY - JOUR
T1 - Requirements on and Selection of Data Storage Technologies for Life Cycle Assessment
AU - Ulbig, Michael
AU - Merschak, Simon
AU - Hehenberger, Peter
AU - Bachler, Johann
N1 - Funding Information:
Acknowledgments. The research has been applied for and was granted as COMET project under the guidance of the Austrian Research Promotion Agency FFG and is funded by the Federal Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry for Digital and Economic Affairs (BMDW) and the provinces of Upper Austria and Styria.
Publisher Copyright:
© 2023, IFIP International Federation for Information Processing.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - The importance of a centralized data storage system for life cycle assessment (LCA) will be addressed in this paper. Further, the decision-making process for a suitable data storage system is discussed. LCA requires a lot of relevant data such as resource/material data, production process data and logistics data, originating from many different sources, which must be integrated. Therefore, data collection for LCA is quite difficult. In practice, relevant data for LCA is often not available or is uncertain and has therefore to be estimated or generalized. This implies less accuracy of the calculated carbon footprint. State of the Art research shows that the LCA data collection process can benefit from data engineering approaches. Key of these approaches is a suitable and efficient data storage system like a data warehouse or a data lake. Depending on the LCA use case, a data storage system can also benefit from the combination with other technologies such as big data and cloud computing. As a result, in this paper a criteria catalog is developed and presented. It can be used to evaluate and decide which data storage systems and additional technologies are recommended to store and process data for more efficient and more precise carbon footprint calculation in life cycle assessment.
AB - The importance of a centralized data storage system for life cycle assessment (LCA) will be addressed in this paper. Further, the decision-making process for a suitable data storage system is discussed. LCA requires a lot of relevant data such as resource/material data, production process data and logistics data, originating from many different sources, which must be integrated. Therefore, data collection for LCA is quite difficult. In practice, relevant data for LCA is often not available or is uncertain and has therefore to be estimated or generalized. This implies less accuracy of the calculated carbon footprint. State of the Art research shows that the LCA data collection process can benefit from data engineering approaches. Key of these approaches is a suitable and efficient data storage system like a data warehouse or a data lake. Depending on the LCA use case, a data storage system can also benefit from the combination with other technologies such as big data and cloud computing. As a result, in this paper a criteria catalog is developed and presented. It can be used to evaluate and decide which data storage systems and additional technologies are recommended to store and process data for more efficient and more precise carbon footprint calculation in life cycle assessment.
KW - Carbon Footprint
KW - Life Cycle Assessment
KW - Data Engineering
KW - Data Storage Technology
KW - Carbon footprint
KW - Data engineering
KW - Data storage technology
KW - Life cycle assessment
UR - http://www.scopus.com/inward/record.url?scp=85151052605&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25182-5_9
DO - 10.1007/978-3-031-25182-5_9
M3 - Conference article
SP - 86
EP - 95
JO - Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies
JF - Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies
T2 - IFIP 19th International Conference on Product Lifecycle Management (PLM2022)
Y2 - 10 July 2022 through 13 July 2022
ER -
