A Taxonomy of Treemap Visualization Techniques

Scheibel, Willy; Trapp, Matthias; Limberger, Daniel; Döllner, Jürgen

doi:10.5220/0009153902730280

Cited by 24 publications

(14 citation statements)

References 60 publications

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“…However, some of these are too specific or not related to our purpose. For example, researchers in [28] propose a taxonomy specifically for static (i.e., non interactive) visualizations, whilst other specific taxonomies have been proposed for dynamic graph visualizations [29], [30] and treemap visualizations [31]. The taxonomy proposed by Guimarães et al [21] is closely related to our work.…”

Section: ) Use Of Existing Taxonomies For Analyzing the Extracted Datamentioning

confidence: 72%

Systematic Literature Review on Cyber Situational Awareness Visualizations

Jiang¹,

Jayatilaka²,

Nasim³

et al. 2021

Preprint

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The dynamics of cyber threats are increasingly complex, making it more challenging than ever for organizations to obtain in-depth insights into their cyber security status. Therefore, organizations rely on Cyber Situational Awareness (CSA) to support them in better understanding the threats and associated impacts of cyber events. Due to the heterogeneity and complexity of cyber security data, often with multidimensional attributes, sophisticated visualization techniques are often needed to achieve CSA. However, there have been no attempts to systematically review and analyze scientific literature on CSA visualizations until now. In this paper, we have systematically selected and reviewed 54 publications that discuss visualizations to support CSA. We extracted data from these papers to identify key stakeholders, information types, data sources, and visualization techniques. Furthermore, we analyze the level of CSA supported by the visualizations, maturity of the visualizations, challenges, and practices related to CSA visualizations to prepare a full analysis of the current state of CSA in the organizational context. Our results reveal certain gaps in CSA visualizations. For instance, the most focus is on operational-level staff and there is a clear lack of visualizations targeting other types of stakeholders such as managers, higher-level decision makers, and non-expert users. Most papers focus on threat information visualization and there is a lack of papers that visualize impact information, response plans, and information shared within teams. Interestingly, only a few studies proposed visualizations to facilitate up to projection level (i.e. the highest level of CSA) whereas most studies facilitated perception level (i.e. the lowest level of CSA). Most of the studies provide evidence of the proposed visualizations through toy examples and demonstrations, while only a few visualizations have been employed in industrial practice. Based on the results that highlight the important concerns in CSA visualizations, we recommend a list of future research directions.

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Section: ) Use Of Existing Taxonomies For Analyzing the Extracted Datamentioning

confidence: 72%

Systematic Literature Review on Cyber Situational Awareness Visualizations

Jiang¹,

Jayatilaka²,

Nasim³

et al. 2021

Preprint

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“…A visualisation called a tree map [ 92 ] was created expressly to make it easier to explore data that are organised hierarchically and in trees. The research trend, debate gaps, and related fields that would be of interest as research areas could all be identified using this data.…”

Section: Bibliometric Analysis and Resultsmentioning

confidence: 99%

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis

Bonkra

Bhatt

Rosak-Szyrocka

et al. 2023

IJERPH

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Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011–2022. In order to conduct the study, the Bibliometrix suite’s VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

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“…Including visualizations that focus on object-oriented software quality and comprehension, the SeeSoft [32] tool visualization maps lines of code into thin rows, uses colors to display information about the timeframe where the code has changed. Scheibel et al, [33] propose a treemap visualization depicting software data using areas, colors and nesting. Additionally, Lanza's et al, present CodeCrawler [14] that supports the understanding of program structure via polymeric views.…”

Section: Related Workmentioning

confidence: 99%

A New Generation of CLASS BLUEPRINT

Agouf

Ducasse

Etien

et al. 2022

2022 Working Conference on Software Visualization (VISSOFT)

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In object-oriented programming, classes are the primary abstraction mechanism used by and exposed to developers. Understanding classes is key for the development and evolution of object-oriented applications. The fundamental problem faced by developers is that while classes are intrinsically structured entities, in IDEs they are represented as a blob of text. The idea behind the original CLASS BLUEPRINT visualization was to represent the internal structure of classes in terms of fields, their accesses, and the method call flow. Additional information was depicted using colors. The thus created visualization proved to be an effective means to support program comprehension. However, a number of omissions rendered it only partially useful.We propose CLASS BLUEPRINT V2 (in short BLUEPRINTV2), which in addition to the information depicted by CLASS BLUEPRINT also supports dead code identification, methods under tests, and calling relationships between class and instance level methods. In addition, BLUEPRINTV2 enhances the understanding of fields by showing how fields of super/subclasses are accessed. We present the enhanced visualization and report on a first validation with 26 developers and 18 projects.

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A Taxonomy of Treemap Visualization Techniques

Cited by 24 publications

References 60 publications

Systematic Literature Review on Cyber Situational Awareness Visualizations

Systematic Literature Review on Cyber Situational Awareness Visualizations

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis

A New Generation of CLASS BLUEPRINT

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