Visual Analytics in the Web of Data

  • Maryam M. Al-Shehhi

Student thesis: Master's Thesis


One of the technologies underpinning the future vision of a web as huge database is Linked Data (LD). LD provides structured data over the web that is understandable by machines therefore enabling smart queries and visiting links between different datasets over the web as a big data graph. Effective Visual Analytics (VAs) techniques will become necessary to efficiently extract and visualize the desired information from these data graphs. In this thesis, we design and develop a theoretical VAs framework for LD data as an approach for the automatic suggestion of information visualization graphs. The main aim of the framework is to amplify user perception by suggesting the best visual representation such as bar chart, map, and timeline of the queried data based on the semantic information within the data. The core of the framework is a well-defined knowledge based information visualization and analysis ontology for automating the visualization process and managing the flow between the framework components. The ontology enables the automation selection of the best visual representation of the selected queried data based on knowledge from the data and visualization (charts) characteristics. Moreover, the framework enhanced with semantic web extracted information from the graph to enable further features, for example the retrieval of more data on demand or the automatic recognition of geo location and time hierarchy entities. This VAs framework will help in the analysis process to produce accurate decisions, paving the road for next generation visual analytics tools for the semantic web data. Indexing Terms: Visual Analytics, Sematic Web, Information Visualization, Automation, Linked Data, and Knowledge Based Model.
Date of AwardDec 2015
Original languageAmerican English
SupervisorBenjamin Hirsch (Supervisor)


  • Visual Analytics
  • Sematic Web
  • Information Visualization
  • Automation
  • Linked Data
  • and Knowledge Based Model.

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