Milena Frtunić Gligorijević, Miloš Bogdanović, Nataša Veljković, Leonid Stoimenov

DOI Number
First page
Last page


In the Big Data era data visualization and exploration systems, as means for data perception and manipulation are facing major challenges. One of the challenges for modern visualization systems is to ensure adequate visual presentation and interaction.  Therefore, within this paper, we present a tool for interactive visualization of data with a hierarchical structure. It is a general-purpose tool that uses a graph-based approach. However, its main focus is on the visual analysis of concept lattices generated as the output of the Formal Concept Analysis algorithm. As the data grow, concept lattice can become complex and hard for visualization and analysis. In order to address this issue, functionalities important for the exploration of the large concept lattices are applied within this tool. The usage of the tool is presented in the example of visualization of concept lattices generated based on the available data on the Canadas open data portal and can be used for exploring the usage of tags within datasets.


Visualization, visual analytics, visual exploration, visualization tool, data exploration

Full Text:



B. Shneiderman. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In IEEE Symposium on Visual Languages, 1996. [Online]. Available:

N. Bikakis and G. Papastefanatos. Visual Exploration and Analytics of Big Data: Challenges and Approaches, 2016.

B. Shneiderman. Extreme Visualization: Squeezing a Billion Records into a Million Pixels. In SIGMOD, 2008. [Online]. Available:

J. Heer and S. Kandel. Interactive Analysis of Big Data. ACM Crossroads, 19(1), 2012. [Online]. Available:

K. Morton, M. Balazinska, D. Grossman, and J. D. Mackinlay. Support the Data Enthusiast: Challenges for Next-Generation Data-Analysis Systems. PVLDB, 7(6), 2014. [Online]. Available:

E. Wu, L. Battle, and S. R. Madden. The Case for Data Visualization Management Systems. PVLDB, 7(10), 2014. [Online]. Available:

P. Godfrey, J. Gryz, and P. Lasek. “Interactive Visualization of Large Data Sets.” IEEE Transactions on Knowledge and Data Engineering, vol. 28(8), pp. 2142-2157, 2016. [Online]. Available:

A. Dadzie and M. Rowe. Approaches to visualising Linked Data: A survey. Semantic Web, 2(2), 2011. [Online]. Available:

N. Marie and F. L. Gandon. Survey of Linked Data Based Exploration Systems. In IESD, 2014.

F. Alahmari, J. A. Thom, L. Magee, and W. Wong. Evaluating Semantic Browsers for Consuming Linked Data. In ADC, 2012.

S. Mazumdar, D. Petrelli, K. Elbedweihy, V. Lanfranchi, and F. Ciravegna. Affective graphs: The visual appeal of Linked Data. Semantic Web, 6(3), 2015. [Online]. Available:

N. Bikakis and T. Sellis. “Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art.” LWDM ’16, 2016.

P. Heim, S. Lohmann, and T. Stegemann. Interactive Relationship Discovery via the Semantic Web. In ESWC 2010. Lecture Notes in Computer Science, vol. 6088. Springer. 2010. [Online]. Available:

F. Benedetti, L. Po, and S. Bergamaschi. A Visual Summary for Linked Open Data sources. In ISWC, 2014.

E. Pietriga. IsaViz: a Visual Environment for Browsing and Authoring RDF Models. In WWW, 2002.

N. Bikakis, J. Liagouris, M. Krommyda, G. Papastefanatos, and T. Sellis. graphVizdb: A Scalable Platform for Interactive Large Graph Visualization. In ICDE, 2016. [Online]. Available:

T. Hastrup, R. Cyganiak, and U. Bojars. Browsing Linked Data with Fenfire. In WWW, 2008.

D. V. Camarda, S. Mazzini, and A. Antonuccio. LodLive, exploring the web of data. In I-SEMANTICS, pp. 197-200, 2012. [Online]. Available:

K. Zhang, H. Wang, D. T. Tran, and Y. Yu. ZoomRDF: semantic fisheye zooming on RDF data. In WWW, 2010

M. Bastian, S. Heymann, and M. Jacomy. Gephi: An Open Source Software for Exploring and Manipulating Networks. In ICWSM, 2009.

J. Dokulil and J. Katreniaková. Using Clusters in RDF Visualization. In Advances in Semantic Processing, 2009.

L. Deligiannidis, K. Kochut, and A. P. Sheth. RDF data exploration and visualization. In Workshop on CyberInfrastructure: Information Management in eScience, pp. 39-46, 2007. [Online]. Available:

C. Sayers. Node-centric RDF Graph Visualization, 2004. Technical Report HP Laboratories

S. Idreos, O. Papaemmanouil, and S. Chaudhuri. Overview of Data Exploration Techniques. In SIGMOD, 2015. [Online]. Available:

J. Heer and B. Shneiderman. Interactive Dynamics for Visual Analysis. Commun. ACM, 55(4), 2012. [Online]. Available:

E. G. Caldarola, and A. M. Rinaldi. “Big Data Visualization Tools: A Survey - The New Paradigms, Methodologies and Tools for Large Data Sets Visualization.” DATA (2017).

P.K. Singh, C. Aswani Kumar, G. Abdullah, “A comprehensive survey on formal concept analysis, its research trends and applications,” Int J Appl Math Comput Sci, vol. 26, no. 2, pp. 495–516, 2016. [Online]. Available:

R. Belohlavek, “Introduction to formal concept analysis,” 2008. [Online]. Available: [Accessed on November 2020].



  • There are currently no refbacks.

Print ISSN: 1820-6417
Online ISSN: 1820-6425