Towards insight-driven sampling for big data visualisation

References

• Adhinarayanan, V. 2015. “On the Greenness of In-Situ and Post-Processing Visualization Pipelines.” 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, May, 880–887.
   Google Scholar
• Berres, Anne Sabine, Vignesh Adhinarayanan, Terece Turton, Wu Feng, and David Honegger. Rogers. 2017. A Pipeline for Large Data Processing Using Regular Sampling for Unstructured Grids. Technical Report. Los Alamos National Lab.(LANL), Los Alamos, NM.
   Google Scholar
• Borghesi, Andrea, Andrea Bartolini, Michela Milano, and Luca Benini. 2018. “Pricing schemes for energy-efficient HPC systems: Design and exploration.” The International Journal of High Performance Computing Applications 109434201881459. http://dx.doi.org/10.1177/1094342018814593.
   Google Scholar
• Card, Stuart K., Jock D. Mackinlay, and Ben Shneiderman. 1999. “Using vision to think.” Readings in information visualization, 579–581. Morgan Kaufmann Publishers Inc.
   Google Scholar
• Chang, Remco, Caroline Ziemkiewicz, Tera Marie Green, and William. Ribarsky. 2009. “Defining Insight for Visual Analytics.” IEEE Computer Graphics and Applications 29 (2): 14–17.
   PubMed Web of Science ®Google Scholar
• Chen, H., S. Zhang, W. Chen, H. Mei, J. Zhang, A. Mercer, R. Liang, and H. Qu. 2015. “Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration.” IEEE Transactions on Visualization and Computer Graphics 21 (9): 1072–1086.
   PubMed Web of Science ®Google Scholar
• Choe, Eun Kyoung, Bongshin Lee, and M. C. Schraefel. 2015. “Characterizing Visualization Insights from Quantified Selfers' Personal Data Presentations.” IEEE Computer Graphics and Applications 35 (4): 28–37.
   PubMed Web of Science ®Google Scholar
• Dahshan, Mai, and Nicholas Polys. 2018. “Making Sense of Scientific Simulation Ensembles.” Poster presented at SC 2018, Dallas, Texas, Nov. https://sc18.supercomputing.org/proceedings/tech_poster/poster_files/post165s2-file3.pdf.
   Google Scholar
• Fekete, Jean-Daniel. 2015. “ProgressiVis: A Toolkit for Steerable Progressive Analytics and Visualization.” 1st Workshop on Data Systems for Interactive Analysis, 5.
   Google Scholar
• Fisher, Danyel, Igor Popov, Steven Drucker, and M. C. Schraefel. 2012. “Trust Me, I'm Partially Right: Incremental Visualization Lets Analysts Explore Large Datasets Faster.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '12, New York, NY, 1673–1682. ACM. http://doi.acm.org/10.1145/2207676.2208294.
   Google Scholar
• Galakatos, Alex, Andrew Crotty, Emanuel Zgraggen, Carsten Binnig, and Tim Kraska. 2017. “Revisiting Reuse for Approximate Query Processing.” Proc. VLDB Endow. 10 (10): 1142–1153. https://doi.org/10.14778/3115404.3115418
   Web of Science ®Google Scholar
• Gilmour, Steven G. 1996. “The Interpretation of Mallows's C_p-statistic.” The Statistician 45: 49–56.
   Google Scholar
• Gori, A., G. Craparo, M. Giannini, Y. Loscalzo, V. Caretti, D. La Barbera, G. M. Manzoni, 2015. “Development of a New Measure for Assessing Insight: Psychometric Properties of the Insight Orientation Scale (IOS).” Schizophrenia Research 169 (1–3): 298–302.
   PubMed Web of Science ®Google Scholar
• Grtler, J., C. Schulz, D. Weiskopf, and O. Deussen. 2018. “Bubble Treemaps for Uncertainty Visualization.” IEEE Transactions on Visualization and Computer Graphics 24 (1): 719–728.
   PubMed Web of Science ®Google Scholar
• Holzinger, Andreas. 2013. “Human–Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together?” International Conference on Availability, Reliability, and Security, 319–328. Springer.
   Google Scholar
• Hong, Seong E., J. Kim Hwa, and J. Cha Kyung. 2018. “Big Data Preliminary Analysis: A Framework for Easier Data Sharing and Discovery.” International Information Institute (Tokyo). Information 21 (2): 755–763. http://login.ezproxy.lib.vt.edu/login?url=https://search.proquest.com/docview/2038677630?accountid=14826
   Google Scholar
• Kaisler, Stephen, Frank Armour, J. Alberto Espinosa, and William Money. 2013. “Big Data: Issues and Challenges Moving Forward.” 2013 46th Hawaii International Conference on System Sciences (HICSS), 995–1004. IEEE.
   Google Scholar
• Kulessa, Moritz, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, and Kristian. Kersting. 2018. “Model-based Approximate Query Processing.” arXiv preprint arXiv:1811.06224.
   Google Scholar
• Leetaru, Kalev. 2019. “The Big Data Revolution will be Sampled: How ‘Big Data’ Has Come To Mean ‘Small Sampled Data’.” Forbes.
   Google Scholar
• Lin, Qingwei, Weichen Ke, Jian-Guang Lou, Hongyu Zhang, Kaixin Sui, Yong Xu, Ziyi Zhou, Bo Qiao, and Dongmei Zhang. 2018. “BigIN4: Instant, Interactive Insight Identification for Multi-Dimensional Big Data.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 547–555. ACM.
   Google Scholar
• Liu, L., A. P. Boone, I. T. Ruginski, L. Padilla, M. Hegarty, S. H. Creem-Regehr, W. B. Thompson, C. Yuksel, and D. H. House. 2017. “Uncertainty Visualization by Representative Sampling from Prediction Ensembles.” IEEE Transactions on Visualization and Computer Graphics 23 (9): 2165–2178.
   PubMed Web of Science ®Google Scholar
• Liu, Zhicheng, Biye Jiang, and Jeffrey. Heer. 2013. “imMens: Real-time Visual Querying of Big Data.” Computer Graphics Forum 32 (3pt4): 421–430. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.12129
   Web of Science ®Google Scholar
• Macke, Stephen, Yiming Zhang, Silu Huang, and Aditya Parameswaran. 2018. “Adaptive Sampling for Rapidly Matching Histograms.” Proc. VLDB Endow. 11 (10): 1262–1275 https://doi.org/10.14778/3231751.3231753
   Web of Science ®Google Scholar
• Melnik, Sergey, Andrey Gubarev, JingJing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, and Theo. Vassilakis. 2010. “Dremel: Interactive Analysis of Web-scale Datasets.” Proceedings of the VLDB Endowment 3 (1-2): 330–339.
   Google Scholar
• Moritz, Dominik, Danyel Fisher, Bolin Ding, and Chi. Wang. 2017. “Trust, but Verify: Optimistic Visualizations of Approximate Queries for Exploring Big Data.” Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI '17, New York, NY, 2904–2915. ACM. http://doi.acm.org/10.1145/3025453.3025456.
   Google Scholar
• Nguyen, T. T., and I. Song. 2016. “Centrality Clustering-based Sampling for Big Data Visualization.” 2016 International Joint Conference on Neural Networks (IJCNN), July, 1911–1917.
   Google Scholar
• North, Chris. 2006. “Toward Measuring Visualization Insight.” IEEE Computer Graphics and Applications 26 (3): 6–9.
   PubMed Web of Science ®Google Scholar
• O'Neil, Cathy. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Broadway Books. https://www.worldcat.org/title/weapons-of-math-destruction-how-big-data-increases-inequality-and-threatens-democracy/oclc/1005213790.
   Google Scholar
• Pang, Alex T, Craig M. Wittenbrink, and Suresh K. Lodha. 1997. “Approaches to Uncertainty Visualization.” The Visual Computer 13 (8): 370–390.
   Web of Science ®Google Scholar
• Park, Yongjoo, Michael Cafarella, and Barzan Mozafari. 2016. “Visualization-Aware Sampling for Very Large Databases.” 2016 IEEE 32nd International Conference on Data Engineering (ICDE) http://dx.doi.org/10.1109/ICDE.2016.7498287.
   Google Scholar
• Rojas, Julian A Ramos, Mary Beth Kery, Stephanie Rosenthal, and Anind. Dey. 2017. “Sampling Techniques to Improve Big Data Exploration.” 2017 IEEE 7th Symposium on Large Data Analysis and Visualization (LDAV), 26–35. IEEE.
   Google Scholar
• Ruan, Zichan, Yuantian Miao, Lei Pan, Nicholas Patterson, and Jun. Zhang. 2017. “Visualization of Big Data Security: a Case Study on the KDD99 Cup Data Set.” Digital Communications and Networks 3 (4): 250–259.
   Web of Science ®Google Scholar
• Sacha, D., H. Senaratne, B. C. Kwon, G. Ellis, and D. A. Keim.. 2016. “The Role of Uncertainty, Awareness, and Trust in Visual Analytics.” IEEE Transactions on Visualization and Computer Graphics 22 (1): 240–249.
   PubMed Web of Science ®Google Scholar
• Saraiya, Purvi, Chris North, and Karen. Duca. 2004. “An Evaluation of Microarray Visualization Tools for Biological Insight.” IEEE Symposium on Information Visualization, 2004 (INFOVIS'04), 1–8. IEEE.
   Google Scholar
• Turkay, C., E. Kaya, S. Balcisoy, and H. Hauser. 2017. “Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis.” IEEE Transactions on Visualization and Computer Graphics 23 (1): 131–140.
   PubMed Web of Science ®Google Scholar
• Wang, Lidong, Guanghui Wang, and Cheryl Ann. Alexander. 2015. “Big Data and Visualization: Methods, Challenges and Technology Progress.” Digital Technologies 1 (1): 33–38.
   Google Scholar
• Xiao, Fengjun, Mingming Lu, Ying Zhao, Soumia Menasria, Dan Meng, Shangsheng Xie, Juncai Li, and Chengzhi Li. 2018. “An Information-aware Visualization for Privacy-Preserving Accelerometer Data Sharing.” Human-Centric Computing and Information Sciences 8 (1): 13. https://doi.org/10.1186/s13673-018-0137-6
   Web of Science ®Google Scholar
• Yi, Ji Soo, Youn-ah Kang, John T. Stasko, and Julie A. Jacko. 2008. “Understanding and Characterizing Insights: How do People Gain Insights Using Information Visualization?” Proceedings of the 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization, 4. ACM.
   Google Scholar
• Zgraggen, Emanuel, Alex Galakatos, Andrew Crotty, Jean-Daniel Fekete, and Tim. Kraska. 2017. “How Progressive Visualizations Affect Exploratory Analysis.” IEEE Transactions on Visualization & Computer Graphics 23 (8): 1977–1987.
   PubMed Web of Science ®Google Scholar