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Interactive Visualization of Big Data: Student Experiences on Tableau

Year 2020, Issue: 18, 262 - 271, 15.04.2020
https://doi.org/10.31590/ejosat.659823

Abstract

Data visualization describes the use of visual elements to better express the significance of massive datasets and to uncover hidden data patterns. Data visualization can take the form of charts, graphs, maps, tables, or different elements. However, interactive data visualization enables direct actions on a plot to change elements and link between multiple plots. Data visualization makes it possible for decision makers, particularly those without a background in statistical analysis or computer science, to quickly and effectively comprehend analytical data. In this study, a case study on interactive visualization of big data by using Tableau within the scope of “Introduction to Big Data Analysis” course given in Computer Engineering Department at Kocaeli University in 2018-2019 Spring semester was discussed. Experiences such as extracting valuable information on different datasets, linking plots with each other, grouping, filtering and action implementation on graphics, multiple visual placement on the dashboard and preparing interactive resumes have been gained. According to the discussion after the lesson, it was seen that some students had the desire to work in the field of business intelligence.

References

  • Buzan, T., & Griffiths, C. (2013). Mind Maps for Business 2nd edn: Using the ultimate thinking tool to revolutionise how you work. Pearson UK.
  • Büschel, W., Vogt, S., & Dachselt, R. (2018). Investigating link attributes of graph visualizations in mobile augmented reality. In Proceedings of the CHI 2018 Workshop on Data Visualization on Mobile Devices. MobileVis (Vol. 18).
  • Ders Materyali (2019) Büyük Veri Analizine Giriş Ders kaynakları. Erişim Adresi: https://piazza.com/kocaeli_university/spring2019/blm442/resources
  • Caldarola, E. G., Picariello, A., & Castelluccia, D. (2015). Modern enterprises in the bubble: Why big data matters. ACM SIGSOFT Software Engineering Notes, 40(1), 1-4.
  • Caldarola, E. G., & Rinaldi, A. M. (2017). Big Data Visualization Tools: A Survey-The New Paradigms, Methodologies and Tools for Large Data Sets Visualization. In DATA (pp. 296-305).
  • Eken, S., & Kumru, P. Y. (2014). Haritalar Üzerinde Suç Verilerinin Görüntülenmesi ve Analizinin Sağlanması: Kocaeli İli Örneği. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 2014(3), 67-72.
  • Eken, S., Türkoğlu, S., & Sayar, A. (2012). Integration of OpenGL Graphic Libraries with Spatial Database as an Analysis and Visualization Tool. Selçuk-Teknik Dergisi, 11(3), 110-123.
  • Fox, P., & Hendler, J. (2011). Changing the equation on scientific data visualization. Science, 331(6018), 705-708.
  • Friendly, M. (2008). A brief history of data visualization. In Handbook of data visualization (pp. 15-56). Springer, Berlin, Heidelberg.
  • Gapminder (2019). Erişim Adresi: https://www.gapminder.org/tools/
  • Gartnet BI Report (2019). Erişim Adresi: https://www.gartner.com/reviews/market/analytics-business-intelligence-platforms
  • Herman, L., Juřík, V., Stachoň, Z., Vrbík, D., Russnák, J., & Řezník, T. (2018). Evaluation of User Performance in Interactive and Static 3D Maps. ISPRS International Journal of Geo-Information, 7(11), 415.
  • Kaggle (2019). Erişim Adresi: https://www.kaggle.com/jessicali9530/honey-production
  • Lee, B., Srinivasan, A., Stasko, J., Tory, M., & Setlur, V. (2018, May). Multimodal interaction for data visualization. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces (p. 11). ACM.
  • Ma, X. (2017). Linked Geoscience Data in practice: Where W3C standards meet domain knowledge, data visualization and OGC standards. Earth Science Informatics, 10(4), 429-441.
  • Mou, X., Jamil, H. M., & Ma, X. (2017, April). Visflow: A visual database integration and workflow querying system. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (pp. 1421-1422). IEEE.
  • Murray, S. (2017). Interactive data visualization for the web: an introduction to designing with. " O'Reilly Media, Inc.".
  • Novak, J. D., & Cañas, A. J. (2006). The theory underlying concept maps and how to construct them. Florida Institute for Human and Machine Cognition, 1, 2006-2001.
  • Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc..
  • Tableau Classroom Training (2019). Erişim Adresi: https://www.tableau.com/learn/classroom
  • Tamayo, J. L. R., Hernández, M. B., & Gómez, H. G. (2018). Digital Data Visualization with Interactive and Virtual Reality Tools. Review of Current State of the Art and Proposal of a Model. Revista ICONO14 Revista científica de Comunicación y Tecnologías emergentes, 16(2), 40-65.
  • Tansley, S., & Tolle, K. M. (2009). The fourth paradigm: data-intensive scientific discovery (Vol. 1). A. J. Hey (Ed.). Redmond, WA: Microsoft research.
  • The 50 best public dataset (2019). Erişim Adresi: https://medium.com/towards-artificial-intelligence/the-50-best-public-datasets-for-machine-learning-d80e9f030279
  • Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Cheshire, CT: Graphics press.
  • Ulvi, M., Eken, S., & Sayar, A. (2013). Service Oriented Visual Interpretation Tool for Times Series Data. Anadolu University of Sciences & Technology-A: Applied Sciences & Engineering, 14(3).
  • Veriseti 1 (2019). Erişim Adresi: https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
  • Veriseti 2 (2019). Erişim Adresi: https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
  • Veriseti 3 (2019). Erişim Adresi: https://public.tableau.com/en-us/s/interactive-resume-gallery
  • Washington Post, A World Apart (2019). Erişim Adresi: https://www.washingtonpost.com/sf/local/2013/11/09/washington-a-world-apart
  • Zudilova-Seinstra, E., Adriaansen, T., & Van Liere, R. (2009). Overview of interactive visualisation. In Trends in Interactive Visualization (pp. 3-15). Springer, London.

Büyük Verinin İnteraktif Görselleştirilmesi: Tableau Üzerine Öğrenci Deneyimleri

Year 2020, Issue: 18, 262 - 271, 15.04.2020
https://doi.org/10.31590/ejosat.659823

Abstract

Büyük veri görselleştirme, büyük veri setlerinin önemini daha iyi ifade etmek ve veriler içindeki gizli desenleri ortaya çıkarmak için görsel öğelerin kullanımını tanımlar. Veri görselleştirme; grafikler, çizgeler, haritalar, tablolar veya çeşitli öğeler şeklinde olabilir. İnteraktif görselleştirme ise birden çok görsel arasında bağlantı kurmak ve bir görsel üzerinde direk işlem yapılmasını sağlar. Veri görselleştirme; karar vericilerin özellikle istatistiksel analiz veya bilgisayar biliminde geçmişi olmayanların, analitik verileri hızlı ve etkili bir şekilde kavramasını mümkün kılar. Bu çalışmada 2018-2019 Bahar döneminde Kocaeli Üniversitesi Bilgisayar Mühendisliği Bölümü’nde verilen “Büyük Veri Analizine Giriş” dersi kapsamında yapılan Tableau kullanarak büyük verilerin interaktif olarak görselleştirilmesi durum çalışmasından bahsedilmiştir. Farklı veri setleri üzerinde değerli bilgilerin çıkarılması, görsellerin birbirleriyle ilişkilendirilmesi, grafikler üzerinde gruplama, filtreleme ve aksiyon gerçekleme, pano üzerinde birden fazla görsel yerleştirme ve etkileşimli özgeçmiş hazırlama gibi tecrübeler elde edilmiştir. Ders sonrası yapılan tartışmaya göre bazı öğrencilerin iş zekâsı alanında çalışma yönelimleri oluştuğu görülmüştür.

References

  • Buzan, T., & Griffiths, C. (2013). Mind Maps for Business 2nd edn: Using the ultimate thinking tool to revolutionise how you work. Pearson UK.
  • Büschel, W., Vogt, S., & Dachselt, R. (2018). Investigating link attributes of graph visualizations in mobile augmented reality. In Proceedings of the CHI 2018 Workshop on Data Visualization on Mobile Devices. MobileVis (Vol. 18).
  • Ders Materyali (2019) Büyük Veri Analizine Giriş Ders kaynakları. Erişim Adresi: https://piazza.com/kocaeli_university/spring2019/blm442/resources
  • Caldarola, E. G., Picariello, A., & Castelluccia, D. (2015). Modern enterprises in the bubble: Why big data matters. ACM SIGSOFT Software Engineering Notes, 40(1), 1-4.
  • Caldarola, E. G., & Rinaldi, A. M. (2017). Big Data Visualization Tools: A Survey-The New Paradigms, Methodologies and Tools for Large Data Sets Visualization. In DATA (pp. 296-305).
  • Eken, S., & Kumru, P. Y. (2014). Haritalar Üzerinde Suç Verilerinin Görüntülenmesi ve Analizinin Sağlanması: Kocaeli İli Örneği. Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi, 2014(3), 67-72.
  • Eken, S., Türkoğlu, S., & Sayar, A. (2012). Integration of OpenGL Graphic Libraries with Spatial Database as an Analysis and Visualization Tool. Selçuk-Teknik Dergisi, 11(3), 110-123.
  • Fox, P., & Hendler, J. (2011). Changing the equation on scientific data visualization. Science, 331(6018), 705-708.
  • Friendly, M. (2008). A brief history of data visualization. In Handbook of data visualization (pp. 15-56). Springer, Berlin, Heidelberg.
  • Gapminder (2019). Erişim Adresi: https://www.gapminder.org/tools/
  • Gartnet BI Report (2019). Erişim Adresi: https://www.gartner.com/reviews/market/analytics-business-intelligence-platforms
  • Herman, L., Juřík, V., Stachoň, Z., Vrbík, D., Russnák, J., & Řezník, T. (2018). Evaluation of User Performance in Interactive and Static 3D Maps. ISPRS International Journal of Geo-Information, 7(11), 415.
  • Kaggle (2019). Erişim Adresi: https://www.kaggle.com/jessicali9530/honey-production
  • Lee, B., Srinivasan, A., Stasko, J., Tory, M., & Setlur, V. (2018, May). Multimodal interaction for data visualization. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces (p. 11). ACM.
  • Ma, X. (2017). Linked Geoscience Data in practice: Where W3C standards meet domain knowledge, data visualization and OGC standards. Earth Science Informatics, 10(4), 429-441.
  • Mou, X., Jamil, H. M., & Ma, X. (2017, April). Visflow: A visual database integration and workflow querying system. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (pp. 1421-1422). IEEE.
  • Murray, S. (2017). Interactive data visualization for the web: an introduction to designing with. " O'Reilly Media, Inc.".
  • Novak, J. D., & Cañas, A. J. (2006). The theory underlying concept maps and how to construct them. Florida Institute for Human and Machine Cognition, 1, 2006-2001.
  • Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc..
  • Tableau Classroom Training (2019). Erişim Adresi: https://www.tableau.com/learn/classroom
  • Tamayo, J. L. R., Hernández, M. B., & Gómez, H. G. (2018). Digital Data Visualization with Interactive and Virtual Reality Tools. Review of Current State of the Art and Proposal of a Model. Revista ICONO14 Revista científica de Comunicación y Tecnologías emergentes, 16(2), 40-65.
  • Tansley, S., & Tolle, K. M. (2009). The fourth paradigm: data-intensive scientific discovery (Vol. 1). A. J. Hey (Ed.). Redmond, WA: Microsoft research.
  • The 50 best public dataset (2019). Erişim Adresi: https://medium.com/towards-artificial-intelligence/the-50-best-public-datasets-for-machine-learning-d80e9f030279
  • Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Cheshire, CT: Graphics press.
  • Ulvi, M., Eken, S., & Sayar, A. (2013). Service Oriented Visual Interpretation Tool for Times Series Data. Anadolu University of Sciences & Technology-A: Applied Sciences & Engineering, 14(3).
  • Veriseti 1 (2019). Erişim Adresi: https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
  • Veriseti 2 (2019). Erişim Adresi: https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
  • Veriseti 3 (2019). Erişim Adresi: https://public.tableau.com/en-us/s/interactive-resume-gallery
  • Washington Post, A World Apart (2019). Erişim Adresi: https://www.washingtonpost.com/sf/local/2013/11/09/washington-a-world-apart
  • Zudilova-Seinstra, E., Adriaansen, T., & Van Liere, R. (2009). Overview of interactive visualisation. In Trends in Interactive Visualization (pp. 3-15). Springer, London.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Süleyman Eken 0000-0001-9488-908X

Publication Date April 15, 2020
Published in Issue Year 2020 Issue: 18

Cite

APA Eken, S. (2020). Büyük Verinin İnteraktif Görselleştirilmesi: Tableau Üzerine Öğrenci Deneyimleri. Avrupa Bilim Ve Teknoloji Dergisi(18), 262-271. https://doi.org/10.31590/ejosat.659823

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