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Guided Visual Analytics—A Visual Analytics Guidance Approach for Systematic Reviews in Research

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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1126))

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Abstract

Visual Analytics systems are often complex expert systems that require high expertise. The simplification of the interaction with such systems in order to make them usable for novices is one subject of current research works. One way to ease the user’s interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a stepwise guidance approach for Visual Analytics. For that, we use the domain of literature search and exploration exemplary. The underlying system allows researchers to visually search and explore scientific publications and automatically generate systematic review protocols. To accomplish this, we present a stepwise visual guidance system approach that combines automatic steps and manual user validation to unify the systematic literature review (SLR) creation process. Based on a design study we conducted, we present our proposed AI-based assistant (MAIA) that assists users in the various steps required to create systematic literature reviews. According to the PRISMA statement, we describe the process of SLR creation exemplary and present the different screens that guide the user through SLR creation.

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Acknowledgements

We thank Michael Löhn from our course Visual Trend Analytics at the Technische Universität Darmstadt, Susanne Clara, and Pascal Kisker from our course Information Visualization at Darmstadt University of Applied Sciences for their contributions to guidance research. This work was conducted within the research group on Human-Computer Interaction and Visual Analytics at the Darmstadt University of Applied Sciences (https://vis.h-da.de).

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Authors and Affiliations

  1. Human-Computer Interaction and Visual Analytics, Darmstadt University of Applied Sciences, 64295, Darmstadt, Germany

    Lennart B. Sina, Cristian A. Secco, Midhad Blazevic & Kawa Nazemi

Authors
  1. Lennart B. Sina
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  2. Cristian A. Secco
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  3. Midhad Blazevic
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  4. Kawa Nazemi
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Corresponding author

Correspondence to Lennart B. Sina .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Central Washington University, Ellensburg, WA, USA

    Boris Kovalerchuk

  2. Darmstadt University of Applied Sciences, Darmstadt, Germany

    Kawa Nazemi

  3. Dept. of Computer Science, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

  4. ISEL, Polytechnic Institute of Lisbon, Lisboa, Portugal

    Nuno Datia

  5. Department of Informatics, London South Bank University, London, UK

    Ebad Bannissi

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Sina, L.B., Secco, C.A., Blazevic, M., Nazemi, K. (2024). Guided Visual Analytics—A Visual Analytics Guidance Approach for Systematic Reviews in Research. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Bannissi, E. (eds) Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-031-46549-9_11

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