skip to main content
10.1145/3473714.3473831acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccirConference Proceedingsconference-collections
research-article

Query And Clustering of Spatio-Temporal Trajectory Big Data Under the Background of COVID-19

Authors Info & Claims
Published:13 August 2021Publication History

ABSTRACT

With the continuous development of sensor technology, computer technology, artificial intelligence and other advanced technologies, there are more and more researches on trajectory tracking and detection technology, which have been widely used in urban planning, traffic management, safety control and other aspects. Trajectory tracking and detection has always been the focus of research by experts and scholars. The purpose of this study is to track and detect the spatial trajectory of the infected person under the current new crown virus epidemic, to timely and accurately understand the itinerary of the new crown virus infected person and to find out all the suspected contacts that the infected person may come into contact with. The current epidemic situation in various countries has made a certain contribution.

References

  1. R. Y. Kim, "The Impact of COVID-19 on Consumers: Preparing for Digital Sales, " in IEEE Engineering Management Review, vol. 48, no. 3, pp. 212--218, 1 thirdquarter, Sept. 2020.Google ScholarGoogle ScholarCross RefCross Ref
  2. M. Jamshidi et al., "Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment, " in IEEE Access, vol. 8, pp. 109581--109595, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  3. J. Laguarta, F. Hueto and B. Subirana, "COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings, " in IEEE Open Journal of Engineering in Medicine and Biology, vol. 1, pp. 275--281, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  4. N. Ahmed et al., "A Survey of COVID-19 Contact Tracing Apps, " in IEEE Access, vol. 8, pp. 134577--134601, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  5. W. Tan and J. Liu, "Application of Face Recognition in Tracing COVID-19 Fever Patients and Close Contacts, " 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 1112--1116.Google ScholarGoogle Scholar
  6. C. Zhan, Y. Zheng, H. Zhang and Q. Wen, "Random-Forest-Bagging Broad Learning System with Applications for COVID-19 Pandemic, " in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3066575.Google ScholarGoogle Scholar
  7. Z. Liu, J. Zuo, R. lv, Z. Li, Y. Sun and X. Yin, "A Planning Model Based on Swarm Intelligence Evolution and Semi-supervised Clustering Algorithm, " 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 2021, pp. 321--324.Google ScholarGoogle Scholar
  8. Qiu Lei, Wu Zhibing. KNN query algorithm for trajectory based on E2LSH. Computer Technology and Development, 2020(02):1--7.Google ScholarGoogle Scholar
  9. S. Gera, M. Mridul and K. Joshi, "Regression Analysis And Future Forecasting Of COVID-19 Using Machine Learnings Algorithm, " 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021, pp. 1014--1018.Google ScholarGoogle Scholar
  10. A. Abdullha and S. Abujar, "COVID-19: Data Analysis and the situation Prediction Using Machine Learning Based on Bangladesh perspective, " 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 2020, pp. 1--8.Google ScholarGoogle Scholar
  11. X. Zhao, J. Zhang and X. Qin, "kNN-DP: Handling Data Skewness in kNN Joins Using MapReduce, " in IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 3, pp. 600--613, 1 March 2018.Google ScholarGoogle ScholarCross RefCross Ref
  12. S. Zhang, X. Li, M. Zong, X. Zhu and R. Wang, "Efficient kNN Classification With Different Numbers of Nearest Neighbors, " in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, pp. 1774--1785, 2018.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Query And Clustering of Spatio-Temporal Trajectory Big Data Under the Background of COVID-19

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Other conferences
            ICCIR '21: Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics
            June 2021
            807 pages
            ISBN:9781450390231
            DOI:10.1145/3473714

            Copyright © 2021 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 13 August 2021

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

            Acceptance Rates

            ICCIR '21 Paper Acceptance Rate131of239submissions,55%Overall Acceptance Rate131of239submissions,55%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader