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Big Data Technologies–Supported Generic Visualization System in an Enterprise Cyber-Physical Environment
Published in Yassine Maleh, Mohammad Shojafar, Ashraf Darwish, Abdelkrim Haqiq, Cybersecurity and Privacy in Cyber-Physical Systems, 2019
Ferda Özdemir Sönmez, Banu Günel
A display type library is the first feature detected (Req. 1). It is designed to be in a form to store information related to display types aiming toward proper use and selection of various display types. Most of the enterprise users are not experts of display types or visualization technologies. The level of visualization knowledge is not questioned explicitly during the survey. Nevertheless, a set of display type thumbnail views were included in the survey content. Some participants asked simple questions regarding these display types, which shows that although they have expertise in the security domain, they need more support on display types. Each display type is powerful to exhibit some data classes. For example, scatter plots are more effectual to display large datasets. The reason is each point allocates small space, and this allows visualization of extensive data in a small space in scatter plots. Treemaps are more suitable to display hierarchical data. Departmental data, data coming from hierarchical network devices, may be more appropriate to be visualized with this kind of display. Circular display types allow visualization of data including what, how, when, and where forms of information, such as events and alerts occurred in specific devices/hosts. A dictionary-like platform including such information is required.
Visualizing Clusters
Published in Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka, Exploratory Data Analysis with MATLAB®, 2017
Wendy L. Martinez, Angel R. Martinez, Jeffrey L. Solka
The treemap displays hierarchical information and relationships by a series of nested rectangles. The parent rectangle (or root of the tree) is given by the entire display area. The treemap is obtained by recursively subdividing this parent rectangle, where the size of each sub-rectangle is proportional to the size of the node. The size could be representative of the size in bytes of the file or the number of employees in an organizational unit. In the case of clustering, the size would correspond to the number of observations in the cluster. We continue to subdivide the rectangles horizontally, vertically, horizontally, etc., until a given leaf configuration (e.g., number of groups in the case of clustering) is obtained.
Big Data Analysis on Smart Tools and Techniques
Published in Gautam Kumar, Dinesh Kumar Saini, Nguyen Ha Huy Cuong, Cyber Defense Mechanisms, 2020
Jabar H. Yousif, Dinesh Kumar Saini
Also, social network data can be easy to follow and understand using a cloud-based visualization method of trends and patterns apparent [12]. Designing visualization approaches in the Big Data field should help to give an overview of the data first and then allow zooming and filtering as well as providing deeply detailed data upon request. Numerous data visualization techniques can be classified based on data criteria into large data volume, data variety, and data dynamics [13]. Data visualization techniques include the following: Treemap is a visualization approach based on space-filling with different size and colors of hierarchical data.Circle Packing visualization method is relay on a hierarchy level of primitive circle shape.Sunburst visualization method uses treemap visualization in a polar coordinate system.Parallel Coordinates visualization technique is a visual analysis of data that resents multidimensional data and objects.Streamgraph is a stacked area graph over a pivotal axis displaying the organic shape of different data groups with distinct colors.Plus several techniques that include semantic network, circular network diagram, cone tree.
A Review and Qualitative Meta-Analysis of Digital Human Modeling and Cyber-Physical-Systems in Ergonomics 4.0
Published in IISE Transactions on Occupational Ergonomics and Human Factors, 2021
Gunther Paul, Nils Darwin Abele, Karsten Kluth
Human Cyber-Physical-Systems (H-CPS; CPS) have emerged as a central concept within this framework integrating Industry 4.0, Operator 4.0, and Digital Twin or DHM into Ergonomics 4.0, as can be seen in the hierarchy chart in Figure 7, which is displayed as tree map. We use a tree map to show hierarchical data as a set of nested rectangles of varying sizes, and we use rectangle size to represent the amount of coding at each node.