Volume ,Issue
Fall
IVA: A Review of the State-of-the-Art
Industrial machines generate a large amount of data during their production process, which pertains to devices such as motor systems, engine systems, operation recordings, and health information. To present and analyze those data, we have to combine traditional methods like visual analytics with AI, which is called intelligent visual analytics (IVA), to achieve diverse purposes like monitoring and decision-making. This technology combines a group of techniques, such as artificial intelligence, data visualization, and big data, to enable investigators to quickly extract meaningful insights from massive datasets. Thus, we conducted the research to investigate the status and usage of IVA systems, as well as the technologies behind them.
D. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon, Visual analytics: Definition, process, and challenges. Springer, 2008. [Baidu Scholar]
J. Ooge, G. Stiglic, and K. Verbert, "Explaining artificial intelligence with visual analytics in healthcare," WIREs Data Mining and Knowledge Discovery, vol. 12, no. 1, 2021. [Baidu Scholar]
A. Endert et al., "The State of the Art in Integrating Machine Learning into Visual Analytics," Computer Graphics Forum, 36, 8, 458–486. [Baidu Scholar]
T. Jun et al., "Intelligent visualization and visual analytics," Journal of Image and Graphics, vol. 28, no. 6, pp. 1909–1926, doi: 10.11834/jig.230034. [Baidu Scholar]
J. Choo and S. Liu, "Visual Analytics for Explainable Deep Learning," IEEE Comput Graph Appl, vol. 38, no. 4, 84–92, 2018. [Baidu Scholar]
C. Miller, Z. Nagy, and A. Schlueter, "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, vol. 81, 1365–1377. [Baidu Scholar]
H. Xu, A. Berres, Y. Liu, M. R. Allen-Dumas, and J. Sanyal, "An overview of visualization and visual analytics applications in water resources management," Environmental Modelling & Software, vol. 153, 105396. [Baidu Scholar]
A. Khakpour, R. Colomo-Palacios, and A. Martini, "Visual Analytics for Decision Support: A Supply Chain Perspective," IEEE Access, vol. 9, 81326–81344. [Baidu Scholar]
D. Grin, M. Grigorieva, and A. Artamonov, "Visual Analysis Application for the Error Messages Clustering Framework," Procedia Comput Sci, vol. 190, 274–283. [Baidu Scholar]
S. Bae, F. Rossi, J. Hook, S. Davidoff, and K. Ma, "A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews," in 2020 IEEE Conference on Visual Analytics Science and Technology (VAST), Los Alamitos, CA, USA: IEEE Computer Society, Oct. 2020, 24–35. [Baidu Scholar]
P. Misra, G. Kandaswamy, P. Mohapatra, K. Kumar, and P. Balamuralidhar, "Structural Health Monitoring of Multi-Rotor Micro Aerial Vehicles," in Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, in DroNet'18. Association for Computing Machinery, 2018, 21–26. [Baidu Scholar]
A. Castellini et al., "Subspace Clustering for Situation Assessment in Aquatic Drones," in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, in SAC '19. Association for Computing Machinery, 2019, 930–937. [Baidu Scholar]
D. Gkorou et al., "Towards Big Data Visualization for Monitoring and Diagnostics of High Volume Semiconductor Manufacturing," in Proceedings of the Computing Frontiers Conference, in CF'17. Association for Computing Machinery, 2017, 338–342. [Baidu Scholar]
S. Shahrabadi, D. Gonzalez, N. Sousa, T. Adão, E. Peres, and L. Magalhães, "Benchmarking Deep Learning models and hyperparameters for Bridge Defects Classification," Procedia Comput Sci, vol. 219, 345–353. [Baidu Scholar]
V. Rodriguez-Fernandez, D. Montalvo-Garcia, F. Piccialli, G. J. Nalepa, and D. Camacho, "DeepVATS: Deep Visual Analytics for Time Series," Knowl Based Syst, vol. 277, 110793, Oct. 2023. [Baidu Scholar]
J. Lucas, Y. Idris, B. Contreras-Rojas, J.-A. Quiane-Ruiz, and S. Chawla, "RheemStudio: Cross-Platform Data Analytics Made Easy," in 2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), in IEEE International Conference on Data Engineering. IEEE, 2018, 1573–1576. [Baidu Scholar]
S. R. Clark, "Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia," Environmental Modelling & Software, vol. 149, 105295. [Baidu Scholar]
F. Kratzert, D. Klotz, C. Brenner, K. Schulz, and M. Herrnegger, "Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks," Hydrol Earth Syst Sci, vol. 22, no. 11, 6005–6022. [Baidu Scholar]
J.-S. Chou, D.-N. Truong, and C.-C. Kuo, "Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning," Energy, vol. 224, 120100. [Baidu Scholar]
A. I. Grimaldo and J. Novak, "Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios," Procedia Comput Sci, vol. 175, 525–532. [Baidu Scholar]
V. Pham, D. C. Weindorf, and T. Dang, "Soil profile analysis using interactive visualizations, machine learning, and deep learning," Comput Electron Agric, vol. 191, 106539. [Baidu Scholar]
L. Chen, Y. Liu, P. Dong, J. Liang, and A. Wang, "An Intelligent Navigation Control Approach for Autonomous Unmanned Vehicles via Deep Learning-Enhanced Visual SLAM Framework," IEEE Access, vol. 11, 119067–119077, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusID:264467562 [Baidu Scholar]
Y. Ahn and Y.-R. Lin, "FairSight: Visual Analytics for Fairness in Decision Making," IEEE Trans Vis Comput Graph, vol. 26, no. 1, 1086-1095. [Baidu Scholar]
A. Narechania, A. Srinivasan, and J. Stasko, "NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries," IEEE Trans Vis Comput Graph, vol. 27, no. 2, 369–379. [Baidu Scholar]
D. Sacha et al., "What you see is what you can change: Human-centered machine learning by interactive visualization," Neurocomputing, vol. 268, 164–175. [Baidu Scholar]
K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer, "Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations," IEEE Trans Vis Comput Graph, vol. 22, no. 1, 649–658. [Baidu Scholar]