SurveySurvey on Visualization and Visual Analytics pipeline-based models: Conceptual aspects, comparative studies and challenges
Introduction
Visualization is the study of interactive visual representations of abstract data to reinforce human cognition [1]. Abstract data includes both numeric and non-numeric data. The field of visualization has emerged from research on Human–Computer Interaction, computer science graphics, visual design psychology, and business methods [1], [2], [3], [4]. It is increasingly integrated as an essential component in scientific research. Visualization assumes that visual representations’ forms and interaction methods profit from the human eyes broad bandwidth pathway into the mind to permit users seeing, exploring and comprehending vast amounts of information [1], [5], [6]. Visualization research focuses on the proposal of methods, techniques and frameworks for transmitting abstract information and analysing data. It is related to the cognitive abilities of human analysts allowing discovering unstructured exploitable insights, limited only by human imagination and creativity. Analyst does not need to learn any sophisticated methods to interpret visualizations [7]. Visualization is also considered as hypothesis generation scheme that can be tracked by more formal analysis [8].
The field of visualization has evolved into a prosperous research field in the recent 30 years [3]. It has progressed into three main branches: (1) Scientific Visualization that concerns scientific data, typically physically based [1], (2) Information Visualization that concerns nonnumeric, non-spatial, and high-dimensional data [1] [9], and (3) Visual Analytics [10] that supports users to make analytical reasoning using interactive visual human–computer interfaces.
As our study is not limited to a specific data type or application domain, we merge Scientific Visualization with Information Visualization. Data analysis makes the data useful by automatically identifying the most interesting aspects of its structure. The integration of this discipline in Information Visualization gave birth to Visual Analytics (cf. Fig. 1). This allowed generating graphical representations highlighting relationships difficult to capture by direct data analysis.
Several Information Visualization models and applications were proposed and published in literature. An outgrowth of this field is the Visual Analytics (cf. Fig. 1), which focuses on the analytical reasoning facilitated by interactive visual interfaces [10]. It is the key technology to deal with and understand massive amounts of complex data that are streaming into organizations, to discover relationships between pieces of data, to build knowledge and to make appropriate decisions [11], [12]. Visual Analytics includes analysts who have long-term and strategic views [2], [8]. Its primary target is the close association of human reasoning and automated methods.
Numerous surveys on visualization models and processes exist. In 1998, Geisler performed a survey on Information Visualization applications and techniques based on data type [13]. In 2008, Zudilova and her colleagues [14] discussed interactive visualization by presenting a large range of topics such as data representation and user interface. In 2013, Moreland presented the most prevalent features of basic visualization pipelines [15]. In 2014, Liu and his colleagues [16] examined the research trends concerning the empirical methodologies, user interactions, visualization frameworks, and applications. Furthermore, some surveys mainly focused on the Visual Analytics techniques and applications and generally limited to a single data type or a specific application domain. For example, Andrienko and Andrienko [17] who reviewed visual-analytics techniques supporting the movement data analysis, and West et al. [18] who conducted a systematic literature review of Visual Analytics approaches dealing with complex clinical data. Other surveys emphasized on specific kinds of visualization, such as graph visualization [19], software visualization [20] and visualization construction tools [21]. In 2016, Wang et al. reviewed previous works presenting Visual Analytics pipelines from different perspectives (i.e. data, visualization, model and knowledge) [4]. In addition, more recent surveys, such as [22], [23] and [24], partially deal with Visual Analytics pipelines.
Most of the previously presented literature surveys focused on reviewing the state-of-art in a certain direction in-depth. To the best of our knowledge, they do not show the scientific community’s shift from Information Visualization to Visual Analytics. In this work, we aim to conduct a comprehensive survey that takes into account all of the most cited and latest Information Visualization and Visual Analytics literatures as a whole. The ultimate purpose of this survey is to draw a complete picture of the progression of the Information Visualization and the Visual Analytics pipeline-based models by investigative the related works. We underline in particular their versions and extensions in order to explore, take advantage and capitalize the evolution in this field, without being limited to a specific data domain or domain application.
In this article an organized comprehensive overview is introduced. It discusses the Information Visualization and Visual Analytics pipeline-based models to investigate their contribution for modelling decision support systems, the commonalities between them, and what conceptual aspects have been proposed/extended. This study describes their different evolution, comparing studies, and discussing challenges and research opportunities in the field of visualization.
The remainder of this paper is organized as follows: it first presents the used literature analysis methodology to explain how the visualization literature is analysed. Then, it describes the evolution of the Information Visualization models through their versions. Next, it reviews the various significant extensions related to the Visual Analytics. Finally, three summaries are provided at the end of the paper to present synthesis discussions and several challenges and research opportunities.
Section snippets
Literature analysis methodology
To have an overview of the research progress of Information Visualization and Visual Analytics until 2019, we examined and reviewed related literatures in the field of visualization. We started our survey from key literature sources in the field: (1) [25] and [5] for Information Visualization and (2) [26] and [27] for Visual Analytics. We covered premier conferences and journals on visualization, which are IEEE Information Visualization (IEEE InfoVis), IEEE Visual Analytics Science and
Survey on Visualization pipeline-based models
In this section, we present our survey on visualization research papers focusing on pipeline-based models. We classify them into two categories: visualization pipeline-based models and Visual Analytics pipeline-based models.
Summaries
We propose three complementary summaries that are described in succession. The first deals with the conceptual aspects of the Information Visualization and Visual Analytics pipeline-based models. The second presents comparative studies of the visualization and Visual Analytics pipeline-based models in literature. The third identifies some key themes for future visualization and Visual Analytics research.
Related work: Positioning compared with other surveys
At the end of this paper, we investigate what this study brings compared with other recent surveys examining visualization pipeline-based models.
Table 6 classifies these surveys based on the objectives set a priori. In fact, the title of this paper sums up the objectives we set ourselves: performed a survey on Information Visualization and Visual Analytics pipeline-based models, their conceptual aspects, comparative studies and challenges.
On the basis of the classification shown in Table 6, we
Conclusion
Visualization is considered as a powerful tool to assist users (decision-makers) in understanding data and recognizing patterns, especially in situations where human reasoning is indispensable. This field is used in many research and engineering areas. It allows transforming a set of raw and often complex data into one or more visual representation(s) in order to facilitate the understanding of what data means.
The field of visualization started with Information Visualization and evolved into
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.
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