Abstract
Research on statistical data visualization emphasizes the need for systems that assist in decision-making and visual analysis. Having found problems in chart construction by novice users, we researched the following question: How can we support novice users to create efficient visualizations with statistical data? To address this question, we proposed ViSC, a recommender system that supports the interactive construction of charts to visualize statistical data. It explores a visualization ontology to recommend a set of graphs that help to answer information-based questions related to the current graph data. By traversing the recommended graphs through their related questions, the user implicitly acquires knowledge both of the domain and of visualization resources that represent the domain concepts of interest well. We report here a qualitative study conducted to evaluate ViSC using two methods: the Semiotic Inspection Method (SIM) and a Retrospective Communicability Evaluation (RCE). We first analyze how the questions influence the users’ traversal through the graph and then address the broader question. We concluded the questions were important to generate efficient visualizations and thus, an efficient solution to help novice users in chart constructions.
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de Sousa, T.A.F., Barbosa, S.D.J. (2014). Recommender System to Support Chart Constructions with Statistical Data. In: Kurosu, M. (eds) Human-Computer Interaction. Theories, Methods, and Tools. HCI 2014. Lecture Notes in Computer Science, vol 8510. Springer, Cham. https://doi.org/10.1007/978-3-319-07233-3_58
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DOI: https://doi.org/10.1007/978-3-319-07233-3_58
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