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Comparative Analysis of Tools for Big Data Visualization and Challenges

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Data Visualization

Abstract

The equivalent term for data visualization is ‘visual communication,’ representing data visually. The theme of data visualization is to convey information clearly, efficiently to users by using graphics, and it helps to have an inner view of data. Data visualization mesmerizes users by changing tedious data into a visually colorful tale. For this, we need data visualization tools that are popular in understanding data easily and visually. When analyzing data, data visualization is one of the steps that aroused to present the data to the users. Big data has come up considerably in recent times and has become an integral part of modern database research. In fact, there is a need for analyzing large amount of data which is temporal. Other characteristics of these datasets are that these are dynamic, noisy and heterogeneous in nature. As a result, to transform these different types of datasets into an accessible and understandable format, we need big data visualization tools. Traditionally, in the beginning, visualization in big data area was done by traditional systems but this approach was not capable of handling variety in datasets. Also, these traditional systems are restricted to deal with the datasets of small size. So, here comes the need for modern systems to analyze variety of datasets in big data, and this modern system gives awareness of modern data visualization tools that supports a variety of big datasets. Henceforth, the comparative analysis on visualization tools and challenges allows user to go with the best visualization tool for analyzing the big data based on the nature of the dataset. Since their inception, several tools have been proposed to model and analyze the process of data visualization in big datasets. In this chapter, we propose to analyze these tools in the form of their strengths and weaknesses. Also, we have planned to discuss in detail their applications and suitability in dealing with different situations (Caldarola, Picariello, & Rinaldi in Experiences in wordnet visualization with labeled graph database. Berlin: Springer, pp. 80–99, [1], Caldarola, Picariello, Rinaldi, & Sacco in Exploration and visualization of big graphs—The DBpedia case study. KDIR (IC3K 2016), pp. 257–264, [2], Caldarola, & Rinaldi in Improving the visualization of wordnet large lexical database through semantic tag clouds. IEEE, pp. 34–41 [3]).

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References

  1. Caldarola, E. G., Picariello, A., & Rinaldi, A. M. (2015). Experiences in wordnet visualization with labeled graph database. In International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management (pp. 80–99). Berlin: Springer.

    Google Scholar 

  2. Caldarola, E. G., Picariello, A., Rinaldi, A. M., & Sacco, M. (2016). Exploration and visualization of big graphs—The DBpedia case study. In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (Vol. 1, pp. 257–264), KDIR (IC3K 2016).

    Google Scholar 

  3. Caldarola, E. G., & Rinaldi, A. M. (2016). Improving the visualization of wordnet large lexical database through semantic tag clouds. In 2016 IEEE International Congress on Big Data (BigDataCongress) (pp. 34–41). IEEE.

    Google Scholar 

  4. Chen, G., A short introduction on data visualization.

    Google Scholar 

  5. 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.

    Google Scholar 

  6. Shadare, A. E., Sadiku, M. N. O., Musa, S. M., & Akujuobi, C. M., Data visualization. International Journal of Engineering Research And Advanced Technology (IJERAT), 02(12). ISSN: 2454-6135.

    Google Scholar 

  7. Dadzie, A., & Pietriga, E. (2017). Visualization of linked data—Reprise. Semantic Web, 8(1).

    Google Scholar 

  8. Marie, N., & Gandon, F. L. (2014). Survey of linked data based exploration systems. In International Workshop on Intelligent Exploration of Semantic Data.

    Google Scholar 

  9. Dadzie, A., & Rowe, M. (2011). Approaches to visualizing linked data: A survey. SemanticWeb, 2(2).

    Google Scholar 

  10. https://www.fastcompany.com/3029239/30-simple-tools-for-data-visualization.

  11. Ali, S. M., Gupta, N., Nayak, G. K., & Lenka, R. K. (2016). Big data visualization: Tools and challenges. 978-1-5090-5256-1/16/$31.00_c 2016 IEEE.

    Google Scholar 

  12. https://opensource.com/resources/big-data.

  13. Tripathy, B. K., Vishwakarma, H. R., & Kothari, D. P. (2014). Neighbourhood based knowledge acquisition using MapReduce from big data over cloud computing. Proceedings of CSIBIG 14, 183–188.

    Google Scholar 

  14. Tripathy, B. K., & Deepthi, P. H. (2015). Handling Fuzziness in Big Data using Clustering Techniques, NCICT-15, Bangalore.

    Google Scholar 

  15. Tripathy, B. K., & Mittal, D. (2016). Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis. Applied Soft Computing, 46, 886–923.

    Google Scholar 

  16. Tripathy, B. K., Deepthi, P. H., & Mittal, D. (2016). Hadoop with intuitionistic fuzzy C-means for clustering in big data. Advances in Intelligent Systems and Computing, 438, 599–610.

    Google Scholar 

  17. Tripathy, B. K., & Deepthi, P. H. (2017). An investigation of fuzzy techniques in clustering of big data. In V. Sugumaran, S. Arun Kumar & T. Arun Kumar (Eds.), Computational Intelligence Applications in Business Intelligence and Big Data Analytics. CRC Press, (Taylor & Francis Group).

    Google Scholar 

  18. Tripathy, B. K., Seetha, H., & Murthy, M. K. (2017). Uncertainty based clustering algorithms for large data sets. In Modern Technologies for Big Data Classification and Clustering (pp. 1–33). IGI Publications.

    Google Scholar 

  19. Seetha, H., Tripathy, B. K., & Murthy M. K. (2017). Modern Technologies for Big Data Classification and Clustering. IGI Publications.

    Google Scholar 

  20. Tripathy, B. K., & Dutta, D. (2018). Trustworthiness in the social internet of things (SIoT). In M. Panda, A. E. Hassanien & A. Abraham (Eds.), Edited Volume- Big Data Analytics: A social Network Approach (p. 18). Taylor and Francis Publisher.

    Google Scholar 

  21. Tripathy, B. K. (2017). Rough set and neighbourhood systems in big data analysis. In V. Sugumaran, S. Arun Kumar & T. Arun Kumar (Eds.), Computational Intelligence Applications in Business Intelligence and Big Data Analytics. CRC Press, (Taylor & Francis Group).

    Google Scholar 

  22. Tripathy, B. K., Sooraj, T. R., & Mohanty, R. K. (2018). Data mining techniques in big data for social network. In M. Panda, A. E. Hassanien & A. Abraham (Eds.), Edited Volume-Big Data Analytics: A Social Network Approach (p. 21). Taylor and Francis Publisher.

    Google Scholar 

  23. Adate, A., & Tripathy, B. K. (2018). Deep learning techniques for image processing. In S. Bhattacharyya, H. Bhaumik, A. Mukherjee & S. De (Eds.), Machine Learning for Big Data Analysis (pp. 69–90). Berlin, Boston: De Gruyter. https://doi.org/10.1515/9783110551433-003.

  24. Weinberg, B. D., Davis, L., & Berger, P. D. (2013). Perspectives on big data. Journal of Marketing Analytics, 1(4), 187–201.

    Google Scholar 

  25. Caldarola, E. G., Picariello, & Castelluccia, D. (2015). Modern enterprises in the bubble: Why big data matters. ACM SIGSOFT Software Engineering Notes, 40(1), 1–4.

    Google Scholar 

  26. Caldarola, E. G., & Rinaldi, A. M. (2015). Big data: A survey—The new paradigms, methodologies and tools. In Proceedings of 4th International Conference on Data Management Technologies and Applications (pp. 362–370).

    Google Scholar 

  27. Caldarola, E. G., Sacco, M., & Terkaj, W. (2014). Bigdata: The current wave front of the tsunami. ACS Applied Computer Science, 10(4), 7–18.

    Google Scholar 

  28. Su, X., Introduction to big data. Institutt for informatikk og e-læring ved NTNU Learning.

    Google Scholar 

  29. Shukla, S., Kukade, V., & Mujawar, S. (2015). Big data: Concept, handling and challenges: An overview. International Journal of Computer Applications (0975–8887), 114(11).

    Google Scholar 

  30. https://www.zoomdata.com/product/unstructured-data-analytics-visualization/.

  31. Bikakis, N. (2018). Big data visualization tools. ATHENA Research Center, Greece, arXiv: 1801.08336v2[cs.DB], February 22, 2018.

    Google Scholar 

  32. https://www.salesforce.com/hub/analytics/why-use-big-data-visualization/.

  33. http://strata.oreilly.com/2010/01/roger-magoulas-on-big-data.html.

  34. Research Trends, Special Issue on Big Data, Issue 30, September 2012.

    Google Scholar 

  35. https://elearningindustry.com/big-data-analytics-challenges-faced-business-enterprises-7-top.

  36. https://www.scnsoft.com/blog/big-data-challenges-and-their-solutions.

  37. Caldarola, E. G., & Rinaldi, A. M. (2017). Big data visualization tools: A survey—The new paradigms, methodologies and tools for large data sets visualization. Conference Paper, July 2017.

    Google Scholar 

  38. Data visualization techniques from basics to big data with SAS® Visual Analytics. White Paper.

    Google Scholar 

  39. Big data visualization: Turning big data into big insights. The Rise of Visualization-based Data Discovery Tools, White Paper, MARCH 2013, Intel IT Center.

    Google Scholar 

  40. Keim, D., Qu, H., & Ma, K.-L. (2013). Big-data visualization: Published by the IEEE Computer Society 0272-1716/13/$31.00 © 2013 IEEE.

    Google Scholar 

  41. Kumar, O., & Goyal, A. (2016). Visualization: A novel approach for big data analytics. In 2016 Second International Conference on Computational Intelligence & Communication Technology.

    Google Scholar 

  42. Jena, B. (2017). A review on data visualization tools used for big data. International Research Journal of Engineering and Technology (IRJET), 04(01). ISSN: 2395-0056.

    Google Scholar 

  43. Liu, S., Maljovec, D., Wang, B., Bremer, P., & Pascucci, V. (2017). Visualizing high dimensional data: Advances in the past decade. IEEE Transactions on Visualization and Computer Graphics (TVCG), 23(3).

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT, Vellore, Tamil Nadu, 632014, India

    G. Divya Zion

  2. School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, 632014, India

    B. K. Tripathy

Authors
  1. G. Divya Zion
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  2. B. K. Tripathy
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Correspondence to B. K. Tripathy .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    S. Margret Anouncia

  2. Department of Computer Science, University of Houston-Victoria, Victoria, TX, USA

    Hardik A. Gohel

  3. Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Subbiah Vairamuthu

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Divya Zion, G., Tripathy, B.K. (2020). Comparative Analysis of Tools for Big Data Visualization and Challenges. In: Anouncia, S., Gohel, H., Vairamuthu, S. (eds) Data Visualization. Springer, Singapore. https://doi.org/10.1007/978-981-15-2282-6_3

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  • DOI: https://doi.org/10.1007/978-981-15-2282-6_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2281-9

  • Online ISBN: 978-981-15-2282-6

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