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Visual analytics for exploring air quality data in an AI-enhanced IoT environment

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Published:10 January 2020Publication History

ABSTRACT

Visual analytics have an important role in the exploration and analysis of large amounts of data in IoT applications. Data visualizations can provide overviews of different aspects of data and user interaction can assist exploration. Recent advances in machine learning and Artificial Intelligence have provided methods that can be used in conjunction with visual analytics to enhance user perception. However, AI methods are often used as "black boxes", making them difficult for end-users to trust. In this paper, a novel visual analytics platform is presented, targeting two goals: a) an architecture for the creation of custom interactive visual analytics dashboards using well-defined components linked to each other, and b) the inclusion of components specifically for making AI methods more explainable. The proposed architecture and components are being used in the context of the AI4IoT pilot within the AI4EU project, which targets air quality monitoring through AI and visualization.

References

  1. September 2019. European Project AI4EU. https://www.ai4eu.eu/.Google ScholarGoogle Scholar
  2. September 2019. Orange - Data Mining Fruitful and Fun. https://orange.biolab.si/.Google ScholarGoogle Scholar
  3. September 2019. Vega - A Visualization Grammar. https://vega.github.io/vega/.Google ScholarGoogle Scholar
  4. Sigmund Akselsen, Pontus Edvard Aurdal, Kerstin Bach, João Paulo Costeira, Ilias Kalamaras, Andreas Jacobsen Lepperød, Pedro Lima, Ieva Martinkenaite, Ole Jakob Mengshoel, Arne Munch-Ellingsen, Hai Thanh Nguyen, Dimitrios Tzovaras, Tiago Veiga, Konstantinos Votis, Leendert Wienhofen, Weiqing Zhang, and Pinar Øzturk. 2019. On the need for explanations, visualisations and measurements in data-driven air quality monitoring and forecasting. In 1st International Workshop on Evaluation and Benchmarking of Human-Centered AI Systems (EBHAIS-2019).Google ScholarGoogle Scholar
  5. Pontus Edvard Aurdal. 2019. VisualBox -- A Generic Data Integration and Visualization Tool. Master's Thesis. The Arctic Univeristy of Norway (UiT), Tromsø, Norway.Google ScholarGoogle Scholar
  6. Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek. 2018. Interpreting and explaining deep neural networks for classification of audio signals. arXiv preprint arXiv:1807.03418 (2018).Google ScholarGoogle Scholar
  7. Pengyu Chen. 2019. Visualization of real-time monitoring datagraphic of urban environmental quality. EURASIP Journal on Image and Video Processing 2019, 1 (2019), 42.Google ScholarGoogle ScholarCross RefCross Ref
  8. Fangzhou Guo, Tianlong Gu, Wei Chen, Feiran Wu, Qi Wang, Lei Shi, and Huamin Qu. 2019. Visual exploration of air quality data with a time-correlation-partitioning tree based on information theory. ACM Transactions on Interactive Intelligent Systems (TiiS) 9, 1 (2019), 4.Google ScholarGoogle Scholar
  9. Fred Matthew Hohman, Minsuk Kahng, Robert Pienta, and Duen Horng Chau. 2018. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE transactions on visualization and computer graphics (2018).Google ScholarGoogle Scholar
  10. Minsuk Kahng, Pierre Y Andrews, Aditya Kalro, and Duen Horng Polo Chau. 2017. ACTIVIS: Visual exploration of industry-scale deep neural network models. IEEE transactions on visualization and computer graphics 24, 1 (2017), 88--97.Google ScholarGoogle Scholar
  11. Andreas Jacobsen Lepperød. 2019. Air Quality Prediction with Machine Learning. Master's Thesis. Norwegian University of Science and Technology (NTNU), Trondheim, Norway.Google ScholarGoogle Scholar
  12. Huan Li, Hong Fan, and Feiyue Mao. 2016. A visualization approach to air pollution data exploration - a case study of air quality index (PM2.5) in Beijing, China. Atmosphere 7, 3 (2016), 35.Google ScholarGoogle ScholarCross RefCross Ref
  13. Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, and Shixia Liu. 2016. Towards better analysis of deep convolutional neural networks. IEEE transactions on visualization and computer graphics 23, 1 (2016), 91--100.Google ScholarGoogle Scholar
  14. Wei Lu, Tinghua Ai, Xiang Zhang, and Yakun He. 2017. An interactive web mapping visualization of urban air quality monitoring data of China. Atmosphere 8, 8 (2017), 148.Google ScholarGoogle ScholarCross RefCross Ref
  15. Scott M Lundberg, Gabriel G Erion, and Su-In Lee. 2018. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888 (2018).Google ScholarGoogle Scholar
  16. Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4765--4774. http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdfGoogle ScholarGoogle Scholar
  17. Scott M Lundberg, Bala Nair, Monica S Vavilala, Mayumi Horibe, Michael J Eisses, Trevor Adams, David E Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, et al. 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature biomedical engineering 2, 10 (2018), 749.Google ScholarGoogle Scholar
  18. Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, and Huamin Qu. 2017. Understanding hidden memories of recurrent neural networks. In 2017 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 13--24.Google ScholarGoogle ScholarCross RefCross Ref
  19. Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing 73 (2018), 1--15.Google ScholarGoogle ScholarCross RefCross Ref
  20. Huamin Qu, Wing-Yi Chan, Anbang Xu, Kai-Lun Chung, Kai-Hon Lau, and Ping Guo. 2007. Visual analysis of the air pollution problem in Hong Kong. IEEE Transactions on visualization and Computer Graphics 13, 6 (2007), 1408--1415.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 1135--1144.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller. 2017. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296 (2017).Google ScholarGoogle Scholar
  23. Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013).Google ScholarGoogle Scholar
  24. Irene Sturm, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller. 2016. Interpretable deep neural networks for single-trial EEG classification. Journal of neuroscience methods 274 (2016), 141--145.Google ScholarGoogle ScholarCross RefCross Ref
  25. Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 3319--3328.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Zhiguang Zhou, Zhifei Ye, Yanan Liu, Fang Liu, Yubo Tao, and Weihua Su. 2017. Visual analytics for spatial clusters of air-quality data. IEEE computer graphics and applications 37, 5 (2017), 98--105.Google ScholarGoogle Scholar

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          cover image ACM Other conferences
          MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems
          November 2019
          350 pages
          ISBN:9781450362382
          DOI:10.1145/3297662

          Copyright © 2019 ACM

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          Publication History

          • Published: 10 January 2020

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          MEDES '19 Paper Acceptance Rate41of102submissions,40%Overall Acceptance Rate267of682submissions,39%

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