A.S.K. Wijayawardena; Ruvan Abeysekera; M.W.P Maduranga

Improve Usability of Multidimensional Data Exploration with Water Fountain Based 3D User Interface Metaphor

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Author(s)

A.S.K. Wijayawardena ^1,* Ruvan Abeysekera ¹ M.W.P Maduranga ²

1. Faculty of Graduate Studies, IIC University of Technology, Phnom Penh 121206, Kingdom of Cambodia

2. Department of Software Engineering, Sri Lanka Technology Campus, Padukka, Sri Lanka

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2024.05.06

Received: 17 Jan. 2024 / Revised: 14 Feb. 2024 / Accepted: 16 May 2024 / Published: 8 Oct. 2024

Index Terms

3D Metaphors, Interactive Data Visualization, Human-Computer-Interaction, Metaphoric Data Visualization

Abstract

Big data such as social network data, financial data, and disease data have multiple dimensions that are more complicated to interpret by the human brain. In this regard, the concept of three-dimensional metaphor-based information visualization and navigation has become very important for big data visualization. The three-dimensional visual metaphors can be used to represent information allowing dealing with more abstract data of larger volumes. Therefore new three-dimensional metaphors are needed for the visualization of multidimensional attributes into easily readable and understandable forms. When compared with 2D data representations, 3D brings many advantages in complex data visualization. But most of the existing 3D visualizations result in complex Graphical User Interfaces that require high cognitive efforts to clearly understand these datasets. Therefore this paper presents a novel 3D user interface metaphor for visual analytics of multidimensional data which leads to drawing better conclusions on the datasets. The proposed system represents information in a more realistic 3D setting. The concept of the 3D water fountain metaphor is adopted to implement the novel data exploration mechanism in 3D space. This paper provides an outline of the proposed conceptual design. Employing a Vector-Borne Disease dataset as a case study, a proof-of-concept prototype based on this conceptual design is developed. The applicability of the conceptual metaphor is showcased through two distinct experiments, each involving four groups engaged in decision-making scenarios within the realm of multidimensional data visualization. Key findings reveal that 85% of the data analysis tasks were efficiently completed using the proposed 3D metaphor. Notably, user satisfaction levels including feedback on learnability, interface aesthetics, ease of use, and overall user experience were graded high. These key findings of the evaluation underscore the heightened potential of 3D user interface metaphors for facilitating visual analytics of multidimensional datasets.

Cite This Paper

A. S. K. Wijayawardena, Ruvan Abeysekera, M. W. P. Maduranga, "Improve Usability of Multidimensional Data Exploration with Water Fountain Based 3D User Interface Metaphor", International Journal of Modern Education and Computer Science(IJMECS), Vol.16, No.5, pp. 88-104, 2024. DOI:10.5815/ijmecs.2024.05.06

Reference

[1]B. Jena, “An Approach for Forecast Prediction in Data Analytics Field by Tableau Software.,” International Journal of Information Engineering & Electronic Business, vol. 11, no. 1, 2019.
[2]S. Nazir et al., “Big data visualization in cardiology—a systematic review and future directions,” IEEE Access, vol. 7, pp. 115945–115958, 2019.
[3]M. Cordeil, A. Cunningham, T. Dwyer, B. H. Thomas, and K. Marriott, “ImAxes: Immersive Axes as Embodied Affordances for Interactive Multivariate Data Visualisation,” in Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology - UIST ’17, Québec City, QC, Canada: ACM Press, 2017, pp. 71–83. doi: 10.1145/3126594.3126613.
[4]S. Liu, D. Maljovec, B. Wang, P.-T. Bremer, and V. Pascucci, “Visualizing High-Dimensional Data: Advances in the Past Decade,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 3, pp. 1249–1268, Mar. 2017, doi: 10.1109/TVCG.2016.2640960.
[5]C. P. Chen and C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information sciences, vol. 275, pp. 314–347, 2014.
[6]D. J.-L. Lee, H. Dev, H. Hu, H. Elmeleegy, and A. Parameswaran, “Avoiding drill-down fallacies with vispilot: Assisted exploration of data subsets,” in Proceedings of the 24th International Conference on Intelligent User Interfaces, ACM, 2019, pp. 186–196.
[7]J. Zhang and M. L. Huang, “2D approach measuring multidimensional data pattern in big data visualization,” in 2016 IEEE International Conference on Big Data Analysis (ICBDA), IEEE, 2016, pp. 1–6.
[8]“Tableau: Business Intelligence and Analytics Software,” Tableau Software. Accessed: May 28, 2020. [Online]. Available: https://www.tableau.com/
[9]“SaTScan - Software for the spatial, temporal, and space-time scan statistics.” Accessed: May 28, 2020. [Online]. Available: https://www.satscan.org/
[10]A. Schreiber and M. Misiak, “Visualizing Software Architectures in Virtual Reality with an Island Metaphor,” in Virtual, Augmented and Mixed Reality: Interaction, Navigation, Visualization, Embodiment, and Simulation, vol. 10909, J. Y. C. Chen and G. Fragomeni, Eds., in Lecture Notes in Computer Science, vol. 10909. , Cham: Springer International Publishing, 2018, pp. 168–182. doi: 10.1007/978-3-319-91581-4_13.
[11]S. Dubel, M. Rohlig, H. Schumann, and M. Trapp, “2D and 3D presentation of spatial data: A systematic review,” in 2014 IEEE VIS International Workshop on 3DVis (3DVis), Paris, France: IEEE, Nov. 2014, pp. 11–18. doi: 10.1109/3DVis.2014.7160094.
[12]A. David and C. J M Tauro, “Web 3D Data Visualization of Spatio Temporal Data using Data Driven Document (D3js),” International Journal of Computer Applications, vol. 111, no. 4, pp. 42–46, Feb. 2015, doi: 10.5120/19529-1169.
[13]J. Brooke, “System usability scale (sus): A quick-and-dirty method of system evaluation user information. Digital equipment co ltd,” Reading, UK, pp. 1–7, 1986.
[14]A. Dix, Human-computer interaction. Springer, 2009.
[15]J. Dykes and S. Bleisch, “Using Web-Based 3-D Visualization for Planning Hikes Virtually An Evaluation,” in Representing, Modeling, and Visualizing the Natural Environment, vol. 20084271, P. Aplin, N. Mount, G. Priestnall, and G. Harvey, Eds., CRC Press, 2008, pp. 353–365. doi: 10.1201/9781420055504.ch21.
[16]A. Moran, V. Gadepally, M. Hubbell, and J. Kepner, “Improving Big Data visual analytics with interactive virtual reality,” in 2015 IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA: IEEE, Sep. 2015, pp. 1–6. doi: 10.1109/HPEC.2015.7322473.
[17]X. Li, Z. Lv, J. Hu, B. Zhang, L. Shi, and S. Feng, “XEarth: A 3D GIS platform for managing massive city information,” in 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Shenzhen, China: IEEE, Jun. 2015, pp. 1–6. doi: 10.1109/CIVEMSA.2015.7158625.
[18]Z. Lv et al., “Managing Big City Information Based on WebVRGIS,” IEEE Access, vol. 4, pp. 407–415, 2016, doi: 10.1109/ACCESS.2016.2517076.
[19]J. Wagner, C. T. Silva, W. Stuerzlinger, and L. Nedel, “Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration,” arXiv preprint arXiv:2402.00344, 2024.
[20]A. K. Fetterman, J. L. Bair, M. Werth, F. Landkammer, and M. D. Robinson, “The scope and consequences of metaphoric thinking: Using individual differences in metaphor usage to understand how metaphor functions.,” Journal of personality and social psychology, vol. 110 3, pp. 458–76, 2016.
[21]P. R. Huttenlocher and others, “Morphometric study of human cerebral cortex development,” Brain development and cognition: A reader, pp. 117–128, 2002.
[22]S. T. Grossberg, Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motor control, vol. 70. Springer Science & Business Media, 2012.
[23]H. Supèr, “Working Memory in the Primary Visual Cortex,” Archives of Neurology, vol. 60, no. 6, p. 809, Jun. 2003, doi: 10.1001/archneur.60.6.809.
[24]W.-C. A. Lee et al., “Anatomy and function of an excitatory network in the visual cortex,” Nature, vol. 532, no. 7599, p. 370, 2016.
[25]G. G. Robertson, S. K. Card, and J. D. Mackinlay, “Information visualization using 3D interactive animation,” Communications of the ACM, vol. 36, no. 4, pp. 57–71, 1993.
[26]S. Few, “Data visualization for human perception,” The Encyclopedia of Human-Computer Interaction, 2nd Ed., 2013.
[27]J. L. Cybulski, S. Keller, and D. Saundage, “Interactive Exploration of Data with Visual Metaphors,” International Journal of Software Engineering and Knowledge Engineering, vol. 25, no. 02, pp. 231–252, Mar. 2015, doi: 10.1142/S0218194015400082.
[28]W. Dunn Jr, A. Burgun, M.-O. Krebs, and B. Rance, “Exploring and visualizing multidimensional data in translational research platforms,” Briefings in bioinformatics, vol. 18, no. 6, pp. 1044–1056, 2016.
[29]W. Li and S. Wang, “PolarGlobe: A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data,” International Journal of Geographical Information Science, vol. 31, no. 8, pp. 1562–1582, 2017.
[30]A. Gracia, S. González, V. Robles, E. Menasalvas, and T. von Landesberger, “New insights into the suitability of the third dimension for visualizing multivariate/multidimensional data: A study based on loss of quality quantification,” Information Visualization, vol. 15, no. 1, pp. 3–30, Jan. 2016, doi: 10.1177/1473871614556393.
[31]Y. Zhonghua and W. Lingda, “3D-Parallel Coordinates: Visualization for time varying multidimensional data,” in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), IEEE, 2016, pp. 655–658.
[32]S. Alwajidi and L. Yang, “3D Parallel Coordinates for Multidimensional Data Cube Exploration,” in Proceedings of the 2018 International Conference on Computing and Big Data - ICCBD ’18, Charleston, SC, USA: ACM Press, 2018, pp. 23–27. doi: 10.1145/3277104.3277106.
[33]K. Akram Hassan, N. Ronnberg, C. Forsell, M. Cooper, and J. Johansson, “A Study on 2D and 3D Parallel Coordinates for Pattern Identification in Temporal Multivariate Data,” in 2019 23rd International Conference Information Visualisation (IV), Paris, France: IEEE, Jul. 2019, pp. 145–150. doi: 10.1109/IV.2019.00033.
[34]B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” p. 8.
[35]H. Zhao and L. Lu, “Variational circular treemaps for interactive visualization of hierarchical data,” in 2015 IEEE Pacific Visualization Symposium (PacificVis), IEEE, 2015, pp. 81–85.
[36]G. R. Bawane and P. Deshkar, “Integration of OLAP and association rule mining,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), IEEE, 2015, pp. 1–4.
[37]A. Ying-Ping and J. Hu, “OLAP application in enterprise marketing management system,” in 2016 3rd International Conference on Systems and Informatics (ICSAI), IEEE, 2016, pp. 588–592.
[38]A. Nanda, S. Gupta, and M. Vijrania, “A Comprehensive Survey of OLAP: Recent Trends,” in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, 2019, pp. 425–430.
[39]R. Herrera-Acuña, V. Argyriou, and S. A. Velastin, “A Kinect-based 3D hand-gesture interface for 3D databases,” Journal on Multimodal User Interfaces, vol. 9, no. 2, pp. 121–139, Jun. 2015, doi: 10.1007/s12193-014-0173-0.
[40]G. Drossis, G. Margetis, and C. Stephanidis, “Towards Big Data Interactive Visualization in Ambient Intelligence Environments,” in Distributed, Ambient and Pervasive Interactions, vol. 9749, N. Streitz and P. Markopoulos, Eds., in Lecture Notes in Computer Science, vol. 9749. , Cham: Springer International Publishing, 2016, pp. 58–68. doi: 10.1007/978-3-319-39862-4_6.
[41]A. Lugmayr, Y. J. Lim, J. Hollick, J. Khuu, and F. Chan, “Financial Data Visualization in 3D on Immersive Virtual Reality Displays: A Case-Study for Data Storytelling and Information Visualization of Financial Data of Australia’s Energy Sector,” in Enterprise Applications, Markets and Services in the Finance Industry, vol. 345, N. Mehandjiev and B. Saadouni, Eds., Cham: Springer International Publishing, 2019, pp. 118–130. doi: 10.1007/978-3-030-19037-8_8.
[42]H. Wang, X. Chen, N. Polys, and P. Sforza, “A Web3D forest geo-visualization and user interface evaluation,” in Proceedings of the 22nd International Conference on 3D Web Technology, 2017, pp. 1–9.
[43]W. Pei, B. Guthier, and A. El Saddik, “The Solar System as a 3D Metaphor to Visualize User Interactions in a Social Network,” in 2017 IEEE International Symposium on Multimedia (ISM), IEEE, 2017, pp. 388–393.
[44]J. A. Wagner Filho, C. M. D. S. Freitas, and L. Nedel, “VirtualDesk: A comfortable and efficient immersive information visualization approach,” in Computer Graphics Forum, Wiley Online Library, 2018, pp. 415–426.
[45]J. A. Wagner Filho, W. Stuerzlinger, and L. Nedel, “Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 514–524, 2019.
[46]J. Li, S. Chen, W. Chen, G. Andrienko, and N. Andrienko, “Semantics-space-time cube. a conceptual framework for systematic analysis of texts in space and time,” IEEE transactions on visualization and computer graphics, 2018.
[47]B. Bach, P. Dragicevic, D. Archambault, C. Hurter, and S. Carpendale, “A descriptive framework for temporal data visualizations based on generalized space-time cubes,” in Computer Graphics Forum, Wiley Online Library, 2017, pp. 36–61.
[48]T. Schneider, Y. Tymchuk, R. Salgado, and A. Bergel, “Cuboidmatrix: Exploring dynamic structural connections in software components using space-time cube,” in 2016 IEEE Working Conference on Software Visualization (VISSOFT), IEEE, 2016, pp. 116–125.
[49]K. Okada, M. Yoshida, T. Itoh, T. Czauderna, and K. Stephens, “VR system for spatio-temporal visualization of tweet data and support of map exploration,” Multimedia Tools and Applications, vol. 78, no. 23, pp. 32849–32868, Dec. 2019, doi: 10.1007/s11042-019-08016-y.
[50]B. Bach, E. Pietriga, and J.-D. Fekete, “Visualizing dynamic networks with matrix cubes,” in Proceedings of the SIGCHI conference on Human Factors in Computing Systems, 2014, pp. 877–886.
[51]P. Gaucher, F. Argelaguet, J. Royan, and A. Lécuyer, “A novel 3D carousel based on pseudo-haptic feedback and gestural interaction for virtual showcasing,” in 2013 IEEE Symposium on 3D User Interfaces (3DUI), IEEE, 2013, pp. 55–58.
[52]C. Donalek et al., “Immersive and collaborative data visualization using virtual reality platforms,” in 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA: IEEE, Oct. 2014, pp. 609–614. doi: 10.1109/BigData.2014.7004282.
[53]M. Drouhard, C. A. Steed, S. Hahn, T. Proffen, J. Daniel, and M. Matheson, “Immersive visualization for materials science data analysis using the oculus rift,” in 2015 IEEE International Conference on Big Data (Big Data), IEEE, 2015, pp. 2453–2461.
[54]“Digital worlds that feel human | Ultraleap.” Accessed: Mar. 15, 2020. [Online]. Available: https://www.ultraleap.com/
[55]“Oculus | VR Headsets & Equipment.” Accessed: Mar. 15, 2020. [Online]. Available: https://www.oculus.com/
[56]“Unity - Multiplatform - Publish your game to over 25 platforms.” Accessed: Oct. 09, 2019. [Online]. Available: https://unity3d.com/unity/features/multiplatform
[57]“Computer System Usability Questionnaire.” Accessed: May 25, 2020. [Online]. Available: https://garyperlman.com/quest/quest.cgi?

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