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A Big Data and Visual Analytics System for Port Risk Warning

  • Jie Song
  • Yinsheng LiEmail author
  • Xu Liang
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

Abstract

Big data and visual analytics are critical for a product tracking and risk warning system to provide friendly GUIs, with which users can find the associated facts fast and precisely. In this work, the authors developed a big data and visual analytics tool for an underdeveloped product tracking and risk warning system for the smart port. The goal is to provide information statistics and further risk mining to reveal possible hidden facts. The proposed tool is to improve the data comprehensiveness, deep insight, and relevance. The Knowledge Graph is applied to organize and describe risk-related data, while data mining algorithms such as clustering and association analysis are used to tap the in-depth value of information. Based on Knowledge Graphs, the system solves the problem of searching related risk products with the same attributes, and analyzes the potential relationship between products of the same category through association analysis. The tool has been developed to support visual navigation in multi-dimensional, multi-faceted, multi-attribute, including association rules, risk warning and dynamic target.

Keywords

Smart port Knowledge graph Data mining Big data visualization Risk warning 

Notes

Acknowledgment

The authors would give our special thanks to all the colleagues from the Institute of E-business, the implementation of the system owes to their instructive suggestions and technical support. Special thanks are also given to the Shanghai ICIQ, for their help on the business.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Software SchoolFudan UniversityShanghaiPeople’s Republic of China

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