Using Chained Views and Follow-up Queries to Assist the Visual Exploration of the Web of Big Linked Data - Archive ouverte HAL Access content directly
Journal Articles International Journal of Human-Computer Interaction Year : 2022

Using Chained Views and Follow-up Queries to Assist the Visual Exploration of the Web of Big Linked Data

Abstract

The Web of Linked Open Data (LOD) provides access to a great number of dynamic datasets containing valuable information to support decision-making processes in diverse application domains while being publicly accessible and up-to-date. While information visualization techniques are useful to explore, analyze, and explain relationships within LOD data, the existing tools are limited to visualizing a single dataset at a time and, often, use static and preprocessed data. In this paper, we leverage the linked aspect of LOD to support the dynamic integration of data into a visualization system by connecting views and distributed LOD datasets using the socalled follow-up queries. We demonstrate how our approach uses dynamic SPARQL queries to integrate external data into the exploration flow through visualization techniques and enrich the ongoing analysis. We ran a semi-structured interview to assess the usefulness of our approach, which results were encouraging while showing its relevance to explore big linked data.
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Dates and versions

hal-03518845 , version 1 (10-01-2022)

Identifiers

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Aline Menin, Minh Nhat Do, Carla Dal Sasso Freitas, Olivier Corby, Catherine Faron, et al.. Using Chained Views and Follow-up Queries to Assist the Visual Exploration of the Web of Big Linked Data. International Journal of Human-Computer Interaction, 2022, ⟨10.1080/10447318.2022.2112529⟩. ⟨hal-03518845⟩
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