The rapid development of e-commerce logistics big data in the context of the Internet and cloud computing not only promotes logistics efficiency but also promotes the optimal utilization of social resources. It covers multisource data such as e-commerce, logistics, and consumers, such as logistics economy, consumption, and transportation information. Big data has the characteristics of volume, velocity, variety, value, and veracity. Since 2011, national policies have promoted the development of the big data industry, and e-commerce logistics big data has started from data collection to the rise of data analysis in the 2010s and the deepening of intelligent applications in the 2020s, involving automation, robots, AI, and blockchain technology. At present, the research frontier focuses on the Hadoop ecosystem, big data processing technology system, and key scientific issues such as data integration, real-time collection, storage processing, predictive optimization, and visual decision support. In the future, the expansion of data scale, cloud and big data integration, intelligent logistics, and data security will become important trends.

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E-commerce Logistics Big Data

  • Yong Pan

摘要

The rapid development of e-commerce logistics big data in the context of the Internet and cloud computing not only promotes logistics efficiency but also promotes the optimal utilization of social resources. It covers multisource data such as e-commerce, logistics, and consumers, such as logistics economy, consumption, and transportation information. Big data has the characteristics of volume, velocity, variety, value, and veracity. Since 2011, national policies have promoted the development of the big data industry, and e-commerce logistics big data has started from data collection to the rise of data analysis in the 2010s and the deepening of intelligent applications in the 2020s, involving automation, robots, AI, and blockchain technology. At present, the research frontier focuses on the Hadoop ecosystem, big data processing technology system, and key scientific issues such as data integration, real-time collection, storage processing, predictive optimization, and visual decision support. In the future, the expansion of data scale, cloud and big data integration, intelligent logistics, and data security will become important trends.