Authors:
Enrico G. Caldarola
1
;
Antonio Picariello
2
;
Antonio M. Rinaldi
3
and
Marco Sacco
4
Affiliations:
1
University of Naples Federico II and National Research Council, Italy
;
2
University of Napoli Federico II, Italy
;
3
University of Naples Federico II, Italy
;
4
National Research Council, Italy
Keyword(s):
Graph Database, Big Data, NoSQL, Data Visualization, DBpedia, Neo4J.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
Visual Data Mining and Data Visualization
Abstract:
Increasingly, the data and information visualization is becoming strategic for the exploration and explanation of large data sets. The Big Data paradigm pushes for new ways, new technological solutions to deal with the big volume and the big variety of data today. Not surprisingly, a plethora of new tools have emerged, each of them with pros and cons, but all espousing the cause of "Bigness of Data". In this paper, we take one of this emerging tools, namely Neo4J, and stress its capabilities in order to import, query and visualize data coming from a \emph{big} case study: DBpedia. We will describe each step in this study focusing on the used strategies for overcoming the different problems mainly due to the intricate nature of the case study and its volume. We confront with both the intensional schema of DBpedia and its extensional part in order to obtain the best result in its visualization. Finally, an attempt to define some criteria to simplify the large-scale visualization of DBp
edia will be made, providing some examples and considerations which have arisen. The ultimate goal of this work is to investigate techniques and approaches to get more insights from the visual representation and analytics of large graph databases.
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