应对冷链行业中的数据工程挑战，通过分析手段减少其环境影响

中文摘要 AI 辅助

数据分析为冷链行业内的组织提供了实现更可持续发展和减少环境足迹的巨大机遇。然而，尽管数据科学近期取得了进展，它们仍面临挑战，即通过适配的全规模生态系统确保数据质量和简化数据流程，这限制了它们进行可持续分析的能力。大多数数据技术和工具源自数据科学社区，并不总是适合领域特性。例如，几乎所有数据质量框架都被设计为单独考虑实体，即现实世界元素的表示。然而，在工业环境中，特别是在冷链部门，实现环境影响减少通常需要对由多个相互依赖的实体组成的整个系统进行建模和研究。本出版物挑战了在冷链部门数据生态系统中应用传统数据科学和工程概念的做法，并提出了一种替代方法，以可持续地解决普遍存在的挑战，从而优化分析流程。

英文摘要

Data analytics offer a great opportunity for organizations within the cold-chain industry to become more sustainable and reduce their environmental footprint. Yet, despite recent progress in data sciences, they still face challenges to ensure data quality and streamline data processes through adapted full-scale ecosystems, limiting their ability to perform sustainable analytics. Most data techniques and tools stem from the data-science community and are not always suitably adapted to domain specificities. For instance, nearly all data-quality frameworks are designed to individually consider entities, i.e. representations of real-world elements. However, achieving environmental impact reduction in industrial settings, particularly within the cold-chain sector, often requires modelling and studying entire systems comprised of multiple interdependent entities. This publication challenges the application of conventional data science and engineering concepts in data ecosystems within the cold-chain sector and proposes an alternative approach to sustainably address prevalent challenges and thus optimize analytical processes.

发表机构

• CEMAFROID
• TECNEA

机构由 AI 辅助整理，请以论文原文为准。