Deep Visual Analytics (DVA): Applications, Challenges and Future Directions
Abstract Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2021-07-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.2991/hcis.k.210704.003 |
| _version_ | 1797274354507055104 |
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| author | Rafiqul Islam Shanjita Akter Rakybuzzaman Ratan Abu Raihan M. Kamal Guandong Xu |
| author_facet | Rafiqul Islam Shanjita Akter Rakybuzzaman Ratan Abu Raihan M. Kamal Guandong Xu |
| author_sort | Rafiqul Islam |
| collection | DOAJ |
| description | Abstract Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data. |
| first_indexed | 2024-03-07T14:57:16Z |
| format | Article |
| id | doaj.art-2f1bb083751a410db4ff19f8d771b556 |
| institution | Directory Open Access Journal |
| issn | 2667-1336 |
| language | English |
| last_indexed | 2024-03-07T14:57:16Z |
| publishDate | 2021-07-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj.art-2f1bb083751a410db4ff19f8d771b5562024-03-05T19:20:42ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362021-07-0111-231710.2991/hcis.k.210704.003Deep Visual Analytics (DVA): Applications, Challenges and Future DirectionsRafiqul Islam0Shanjita Akter1Rakybuzzaman Ratan2Abu Raihan M. Kamal3Guandong Xu4Advanced Analytics Institute (AAI), University of Technology Sydney (UTS)Department of Computer Science & Engineering, Islamic University of Technology (IUT)Department of Computer Science & Engineering, Islamic University of Technology (IUT)Department of Computer Science & Engineering, Islamic University of Technology (IUT)Advanced Analytics Institute (AAI), University of Technology Sydney (UTS)Abstract Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.https://doi.org/10.2991/hcis.k.210704.003Deep visual analyticsvisual analyticsvisual interactive systemdeep learningmachine learning |
| spellingShingle | Rafiqul Islam Shanjita Akter Rakybuzzaman Ratan Abu Raihan M. Kamal Guandong Xu Deep Visual Analytics (DVA): Applications, Challenges and Future Directions Human-Centric Intelligent Systems Deep visual analytics visual analytics visual interactive system deep learning machine learning |
| title | Deep Visual Analytics (DVA): Applications, Challenges and Future Directions |
| title_full | Deep Visual Analytics (DVA): Applications, Challenges and Future Directions |
| title_fullStr | Deep Visual Analytics (DVA): Applications, Challenges and Future Directions |
| title_full_unstemmed | Deep Visual Analytics (DVA): Applications, Challenges and Future Directions |
| title_short | Deep Visual Analytics (DVA): Applications, Challenges and Future Directions |
| title_sort | deep visual analytics dva applications challenges and future directions |
| topic | Deep visual analytics visual analytics visual interactive system deep learning machine learning |
| url | https://doi.org/10.2991/hcis.k.210704.003 |
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