Abstract:Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge—experience, skills, insights, intuition, judgment and know-how—into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in know… Show more
“…User and layout section of the dashboard meta‐model, including the visualization goals, supported tasks and user characteristics. Source : (Vázquez‐Ingelmo, García‐Holgado, García‐Peñalvo, & Therón, 2020)…”
Section: Methodsmentioning
confidence: 99%
“…Components' section of the dashboard meta‐model, in which information visualizations' fine‐grained features and relationships are represented. Source : (Vázquez‐Ingelmo, García‐Holgado, et al, 2020)…”
The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine-grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri-variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.
“…User and layout section of the dashboard meta‐model, including the visualization goals, supported tasks and user characteristics. Source : (Vázquez‐Ingelmo, García‐Holgado, García‐Peñalvo, & Therón, 2020)…”
Section: Methodsmentioning
confidence: 99%
“…Components' section of the dashboard meta‐model, in which information visualizations' fine‐grained features and relationships are represented. Source : (Vázquez‐Ingelmo, García‐Holgado, et al, 2020)…”
The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine-grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri-variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.
“…Both experiments are focused on the automatic visualization generation. The generator use code templates based on a meta-model to define dashboards [2][3][4] and a Python script in which the different parameters are tuned to get a set of visualizations. The script processes the dataset changing a set of characteristics and provide a HTML and JavaScript file with the visualizations.…”
Section: Identification Of Featuresmentioning
confidence: 99%
“…Following this approach, a dashboard meta-model was developed in previous studies, obtaining a set of abstract elements and relationships to define specific products [2][3][4]. A fragment of the dashboard meta-model is shown in Fig.…”
Visual explanations are powerful means to convey information to large audiences. However, the design of information visualizations is a complex task, because a lot of factors are involved (the audience profile, the data domain, etc.). The complexity of this task can lead to poor designs that could make users reach wrong conclusions from the visualized data. This work illustrates the process of identifying features that could make an information visualization confusing or even misleading with the goal of arranging them into a meta-model. The metamodel provides a powerful resource to automatically generate information visualizations and dashboards that take into account not only the input data, but also the audience's characteristics, the available data domain knowledge and even the data context.
Information visualization tools are widely used to better understand large and complex datasets. However, to make the most out of them, it is necessary to rely on proper designs that consider not only the data to be displayed, but also the audience and the context. There are tools that already allow users to configure their displays without requiring programming skills, but this research project aims at exploring the automatic generation of information visualizations and dashboards in order to avoid the configuration process, and select the most suitable features of these tools taking into account their contexts. To address this problem, a domain engineering, and machine learning approach is proposed.
CCS CONCEPTS• Software and its engineering; • Reusability; • Humancentered computing; • Visualization toolkits;
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