Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts
With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the rec...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Online Access: | https://hdl.handle.net/10356/161147 |
| _version_ | 1826116973652606976 |
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| author | Das, Monidipa Ghosh, Soumya K. |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Das, Monidipa Ghosh, Soumya K. |
| author_sort | Das, Monidipa |
| collection | NTU |
| description | With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data. |
| first_indexed | 2024-10-01T04:20:10Z |
| format | Journal Article |
| id | ntu-10356/161147 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:20:10Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1611472022-08-16T08:37:01Z Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts Das, Monidipa Ghosh, Soumya K. School of Computer Science and Engineering Engineering::Computer science and engineering Data-Driven Modeling Spatio-Temporal Data With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data. 2022-08-16T08:37:00Z 2022-08-16T08:37:00Z 2020 Journal Article Das, M. & Ghosh, S. K. (2020). Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts. Journal of Computer Science and Technology, 35(3), 665-696. https://dx.doi.org/10.1007/s11390-020-9349-0 1000-9000 https://hdl.handle.net/10356/161147 10.1007/s11390-020-9349-0 2-s2.0-85086140165 3 35 665 696 en Journal of Computer Science and Technology © 2020 Institute of Computing Technology, Chinese Academy of Sciences. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Data-Driven Modeling Spatio-Temporal Data Das, Monidipa Ghosh, Soumya K. Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts |
| title | Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts |
| title_full | Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts |
| title_fullStr | Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts |
| title_full_unstemmed | Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts |
| title_short | Data-driven approaches for spatio-temporal analysis: a survey of the state-of-the-arts |
| title_sort | data driven approaches for spatio temporal analysis a survey of the state of the arts |
| topic | Engineering::Computer science and engineering Data-Driven Modeling Spatio-Temporal Data |
| url | https://hdl.handle.net/10356/161147 |
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