Abstract
Data visualization plays an important role in gaining insight from data. Generally, traditional methods are used to systematically create graphical formats of data attributes of either numeric or textual data. However, these traditional methods are very time-consuming computationally when they must display data points of big data sources. It is significant to explore new methods and algorithms that require less computational time while taking into consideration the volume of data attributes involved. In this chapter, the behavior of animals is explored to help create a method and an algorithm for data visualization suited for big data visualization.
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References
Agbehadji, I. E., Millham, R., Fong, S. J., & Yang, H. (2018). Kestrel-based search algorithm for association rule mining of frequently changed items with numeric and time dimension (under consideration).
Agbehadji, I. E., Millham, R., Thakur, S., Yang, H. & Addo, H. (2018). Visualization of frequently changed patterns based on the behaviour of dung beetles. In International Conference on Soft Computing in Data Science (pp. 230–245).
Agrawal, D., Das, S., & El Abbadi, A. (2010). Big data and cloud computing: New wine or just new bottles? Proceedings of the VLDB Endowment, 3(1–2), 1647–1648.
Burtica, R., et al. (2012). Practical application and evaluation of no-SQL databases in cloud computing. In 2012 IEEE International Systems Conference (SysCon). IEEE.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization—Using vision to think. San Francisco, CA: Morgan Kaufmann Publishers.
Choy, J., Chawla, V., & Whitman, L. (2011). Data visualization techniques: From basics to big data with SAS visual analytics. https://www.slideshare.net/AllAnalytics/data-visualization-techniques.
Dull, R. B., & Tegarden, D. P. (1999). A comparison of three visual representations of complex multidimensional accounting information. Journal of Information Systems, 13(2), 117.
Etienne, A. S., & Jeffery, K. J. (2004). Path integration in mammals. Hippocampus, 14, 180–192.
Etienne, A. S., Maurer, R., & Saucy, F. (1988). Limitations in the assessment of path dependent information. Behavior, 106, 81–111.
Gemignani, Z. (2010). Better know a visualization: Parallel coordinates. www.juiceanalytics.com/writing/parallel-coordinates.
Golani, I., Benjamini, Y., & Eilam, D. (1993). Stopping behavior: Constraints on exploration in rats (Rattus norvegicus). Behavioural Brain Research, 53, 21–33.
Heinrich, J. (2013). Visualization techniques for parallel coordinates.
Inselberg, A. (1981). N-dimensional graphics (Technical Report G320-2711). IBM. Cited on page 7.
Inselberg, A. (1985). The plane with parallel coordinates. The Visual Computer, 1(4), 69–91. Cited on pages 7,8,18, 25, and 38.
Inselberg, A., & Dimsdale, B. (1990). Parallel coordinates: A tool for visualizing multi-dimensional geometry (pp. 361–370). San Francisco, CA: Visualization 90.
Keim, D. (2000). Designing pixel-oriented visualization techniques: Theory and applications. IEEE Trans Visualization and Computer Graphics, 6(1), 59–78.
Keim, D. A. (2001). Visual exploration of large data sets. Communications of the ACM, 44, 38–44.
Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1).
Keim, D. A., Kriegel, H. (1996). Visualization techniques for mining large databases: A comparison. IEEE Transactions on Knowledge and Data Engineering, Special Issue on Data Mining, 8(6), 923–938.
Keim, D. A., Bergeron, R. D., & Pickett, R. M. (1994). Test data sets for evaluating data visualization techniques. https://pdfs.semanticscholar.org/7959/fd04a4f0717426ce8a6512596a0de1b99d18.pdf.
Khan, M., & Khan, S. S. (2011). Data and information visualization methods and interactive mechanisms: A survey. International Journal of Computer Applications, 34(1), 1–14.
Kuhn, T., & Woolley, O. (2013). Modeling and simulating social systems with MATLAB; Lecture 4—Cellular automata. ETH Zürich.
Leung, C. K., Kononov, V. V., Pazdor, A. G. M., Jiang, F. (2016). PyramidViz: Visual analytics and big data visualization of frequent patterns. In IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress.
Lieberman, M. (2014). Visualizing big data: Social network analysis. In Digital Research Conference.
Lu, C. -T., Sripada, L. N., Shekhar, S., & Liu, R. (2005). Transportation data visualisation and mining for emergency management. International Journal of Critical Infrastructures, 1(2/3), 170–194.
Mamduh, S. M., Kamarudin, K., Shakaff, A. Y. M., Zakaria, A., & Abdullah, A.H. (2014). Comparison of Braitenberg vehicles with bio-inspired algorithms for odor tracking in laminar flow. NSI Journals Australian Journal of Basic and Applied Sciences, 8(4), 6–15.
Marghescu, D. (2008). Evaluating multidimensional visualization techniques in data mining tasks. http://www.doria.fi/bitstream/handle/10024/69974/MarghescuDorina.pdf?sequence=3&isAllowed=y.
Mittelstaedt, H., & Mittelstaedt, M.-L. (1982). Homing by path integration. In F. Papi & H. G. Wallraff (Eds.), Avian navigation (pp. 290–297). New York: Springer.
Moere, A. V. (2004). Time-varying data visualization using information flocking boids. In IEEE Symposium on Information Visualization (p. 8).
Moere, A. V., & Lau, A. (2007). Information flocking: An approach to data visualization using multi-agent formation behavior. In Proceedings of Australian Conference on Artificial Life (pp. 292–304). Springer.
Moere, A. V., Clayden, J. J., & Dong, A. (2006). Data clustering and visualization using cellular automata ants. Berlin Heidelberg: Springer.
Risden, K., & Czerwinski, M. P. (2000). An initial examination of ease of use for 2D and 3D information visualizations of web content. International Journal of Human—Computer Studies, 53, 695–714.
Santos, B. S. (2008). Evaluating visualization techniques and tools: What are the main issues? http://www.dis.uniroma1.it/beliv08/pospap/santos.pdf.
SAS Institute Inc. (2013). Five big data challenges and how to overcome them with visual analytics. Available http://4instance.mobi/16thCongress/five-big-data-challenges-106263.pdf.
SAS Institute Inc. (2017). Data visualization techniques: From basics to big data with SAS visual analytics. sas.com/visual-analytics.
Synocloud. (2013). Overview of big data and NoSQL technologies as of January 2013. Available at http://www.syoncloud.com/big_data_technology_overview. Accessed 22 Dec 2015.
Wang, L., Wang, G., & Alexander, C. A. (2015). Big data and visualization: Methods, challenges and technology progress. Digital Technologies, 1(1), 33–38. Science and Education Publishing Available online at http://pubs.sciepub.com/dt/1/1/7.
Ward, M., Grinstein, G., & Keim, D. (2010). Interactive data visualization: Foundations, techniques, and application, A K Peters.
Wegman, E. J. (1990). Hyper dimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85(411), 664–675. Cited on pages 7, 8, 9, 18, 38, 39, and 101.
Wits University. (2013). Dung beetles follow the milky way: Insects found to use stars for orientation. ScienceDaily. https://www.sciencedaily.com/releases/2013/01/130124123203.htm.
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Agbehadji, I.E., Yang, H. (2021). Data Visualization Techniques and Algorithms. In: Fong, S., Millham, R. (eds) Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-6695-0_10
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