Visual Text Analytics Technologies for Real-Time Big Data: Chronological Evolution and Issues
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Visual Text Analytics Technologies for Real-Time Big Data: Chronological Evolution and Issues

Authors: Siti Azrina B. A. Aziz, Siti Hafizah A. Hamid


New approaches to analyze and visualize data stream in real-time basis is important in making a prompt decision by the decision maker. Financial market trading and surveillance, large-scale emergency response and crowd control are some example scenarios that require real-time analytic and data visualization. This situation has led to the development of techniques and tools that support humans in analyzing the source data. With the emergence of Big Data and social media, new techniques and tools are required in order to process the streaming data. Today, ranges of tools which implement some of these functionalities are available. In this paper, we present chronological evolution evaluation of technologies for supporting of real-time analytic and visualization of the data stream. Based on the past research papers published from 2002 to 2014, we gathered the general information, main techniques, challenges and open issues. The techniques for streaming text visualization are identified based on Text Visualization Browser in chronological order. This paper aims to review the evolution of streaming text visualization techniques and tools, as well as to discuss the problems and challenges for each of identified tools.

Keywords: Information visualization, visual analytics, text mining, visual text analytics tools, big data visualization.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 953


[1] P. Chandarana and M. Vijayalakshmi, “Big Data analytics frameworks,” 2014 Int. Conf. Circuits, Syst. Commun. Inf. Technol. Appl., pp. 430–434, Apr. 2014.
[2] P. O. Box and A. Ain, “Real-Time Big Data Analytics : Applications and Challenges,” pp. 305–310, 2014.
[3] C. Rohrdantz, D. Oelke, M. Krstajic, and F. Fischer, Real-time visualization of streaming text data: tasks and challenges, no. October. 2011.
[4] L. Zhang, A. Stoffel, M. Behrisch, S. Mittelstadt, T. Schreck, R. Pompl, S. Weber, H. Last, and D. Keim, “Visual analytics for the big data era—A comparative review of state-of-the-art commercial systems,” in Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, 2012, pp. 173–182.
[5] J. R. Harger and P. J. Crossno, “Comparison of open-source visual analytics toolkits,” in IS&T/SPIE Electronic Imaging, 2012, p. 82940E–82940E–10.
[6] A. Šilić and B. Bašić, “Visualization of text streams: A survey,” Knowledge-Based Intell. Inf. …, pp. 1–12, 2010.
[7] S. Liu, W. Cui, Y. Wu, and M. Liu, “A survey on information visualization: recent advances and challenges,” Vis. Comput., Jan. 2014.
[8] K. Kucher and A. Kerren, “Text Visualization Browser: A Visual Survey of Text Visualization Techniques,” Poster Abstr. IEEE VIS 2014, 2014.
[9] A. Kabán and M. A. Girolami, “A Dynamic Probabilistic Model to Visualise Topic Evolution in Text Streams,” J. Intell. Inf. Syst., vol. 18, no. 2–3, pp. 107–125, Mar. 2002.
[10] C. Albrecht-Buehler, B. Watson, and D. A. Shamma, “TextPool: Visualizing Live Text Streams,” IEEE Symp. Inf. Vis., 2004.
[11] J. R. Benson, P. Lafleur, D. Crist, B. Watson, and N. Carolina, “Agent-based Visualization of Streaming Text,” Inf. Vis. Conf., 2008.
[12] M. Krstajic, F. Mansmann, A. Stoffel, M. Atkinson, and D. a. Keim, “Processing online news streams for large-scale semantic analysis,” 2010 IEEE 26th Int. Conf. Data Eng. Work. (ICDEW 2010), pp. 215–220, 2010.
[13] F. Mansmann, M. Krstajic, F. Fischer, and E. Bertini, “StreamSqueeze: a dynamic stream visualization for monitoring of event data,” p. 829404, Dec. 2011.
[14] M. Krstajić, E. Bertini, and D. A. Keim, “CloudLines: compact display of event episodes in multiple time-series.,” IEEE Trans. Vis. Comput. Graph., vol. 17, no. 12, pp. 2432–9, Dec. 2011.
[15] J. Alsakran, Y. Chen, Y. Zhao, J. Yang, and D. Luo, “STREAMIT: Dynamic visualization and interactive exploration of text streams,” 2011 IEEE Pacific Vis. Symp., pp. 131–138, Mar. 2011.
[16] W. Dou, X. Wang, D. Skau, W. Ribarsky, and M. X. Zhou, “LeadLine: Interactive visual analysis of text data through event identification and exploration,” 2012 IEEE Conf. Vis. Anal. Sci. Technol., pp. 93–102, Oct. 2012.
[17] J. Alsakran, Y. Chen, D. Luo, Y. Zhao, J. Yang, W. Dou, and S. Liu, “Real-time visualization of streaming text with a force-based dynamic system.,” IEEE Comput. Graph. Appl., vol. 32, no. 1, pp. 34–45, 2012.
[18] W. Cui, H. Qu, H. Zhou, W. Zhang, and S. Skiena, “Watch the Story Unfold with TextWheel,” ACM Trans. Intell. Syst. Technol., vol. 3, no. 2, pp. 1–17, Feb. 2012.
[19] M. Krstajic, C. Rohrdantz, M. Hund, and A. Weiler, “Getting there first: Real-time detection of real-world incidents on twitter,” pp. 2–5, 2012.
[20] F. Fischer, F. Mansmann, and D. A. Keim, “Real-time visual analytics for event data streams,” in Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC ’12, 2012, p. 801.
[21] C. Rohrdantz, M. C. Hao, U. Dayal, L.-E. Haug, and D. A. Keim, “Feature-Based Visual Sentiment Analysis of Text Document Streams,” ACM Trans. Intell. Syst. Technol., vol. 3, no. 2, pp. 1–25, Feb. 2012.
[22] E. R. Gansner, Y. Hu, and S. C. North, “Interactive Visualization of Streaming Text Data with Dynamic Maps,” J. Graph Algorithms Appl., vol. 17, no. 4, pp. 515–540, 2013.
[23] E. Gansner, Y. Hu, and S. North, “Visualizing Streaming Text Data with Dynamic Maps,” arXiv Prepr. arXiv1206.3980, Jun. 2012.
[24] H. Bosch, D. Thom, F. Heimerl, E. Püttmann, S. Koch, R. Krüger, M. Wörner, and T. Ertl, “ScatterBlogs2: real-time monitoring of microblog messages through user-guided filtering.,” IEEE Trans. Vis. Comput. Graph., vol. 19, no. 12, pp. 2022–31, Dec. 2013.
[25] M. Krstajic, M. Najm-Araghi, F. Mansmann, and D. A. Keim, “Story Tracker: Incremental visual text analytics of news story development,” Inf. Vis., vol. 12, pp. 308–323, 2013.
[26] T. Kraft, D. X. Wang, J. Delawder, W. Dou, and W. Ribarsky, “Less After-the-Fact: Investigative visual analysis of events from streaming twitter,” in 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), 2013, pp. 95–103.
[27] D. Archambault, D. Greene, and P. Cunningham, “TwitterCrowds: Techniques for Exploring Topic and Sentiment in Microblogging Data,” pp. 1–19, Jun. 2013.
[28] X. Liu, Y. Hu, S. North, and H.-W. Shen, “CompactMap: A mental map preserving visual interface for streaming text data,” in Big Data, 2013 IEEE International Conference on, 2013, pp. 48–55.
[29] M. C. Hao, C. Rohrdantz, H. Janetzko, D. A. Keim, U. Dayal, L. e. Haug, M. Hsu, and F. Stoffel, “Visual sentiment analysis of customer feedback streams using geo-temporal term associations,” Inf. Vis., vol. 12, no. 3–4, pp. 273–290, Jun. 2013.