3D data visualization: The advantages of volume graphics and big data to support geologic interpretation

2015 ◽  
Vol 3 (3) ◽  
pp. SX29-SX39 ◽  
Author(s):  
Carl Byers ◽  
Andrew Woo
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Visualization ◽  
3D Visualization ◽  
Data Types ◽  
Volume Graphics ◽  
3D Data ◽  
Geologic Interpretation ◽  
Diverse Data

The ability to integrate diverse data types from multiple live and simulated sources, manipulate them dynamically, and deploy them in integrated, visual formats and in mobile settings provides significant advantages. We have reviewed some of the benefits of volume graphics and the use of big data in the context of 3D visualization case studies, in which inherent features, such as representation efficiencies, dynamic modifications, cross sectioning, and others, could improve interpretation processes and workflows.

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

scholarly journals NG-Circos: next-generation Circos for data visualization and interpretation

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Ya Cui ◽  
Zhe Cui ◽  
Jianfeng Xu ◽  
Dapeng Hao ◽  
Jiejun Shi ◽  
...  
Keyword(s):  
Data Visualization ◽  
Reproducible Research ◽  
Visualization Tool ◽  
Functional Modules ◽  
Next Generation ◽  
Biomedical Sciences ◽  
Data Types ◽  
Genome Visualization ◽  
Limited Support ◽  
Diverse Data

Abstract Circos plots are widely used to display multi-dimensional next-generation genomic data, but existing implementations of Circos are not interactive with limited support of data types. Here, we developed next-generation Circos (NG-Circos), a flexible JavaScript-based circular genome visualization tool for designing highly interactive Circos plots using 21 functional modules with various data types. To our knowledge, NG-Circos is the most powerful software to construct interactive Circos plots. By supporting diverse data types in a dynamic browser interface, NG-Circos will accelerate the next-generation data visualization and interpretation, thus promoting the reproducible research in biomedical sciences and beyond. NG-Circos is available at https://wlcb.oit.uci.edu/NG-Circos and https://github.com/YaCui/NG-Circos.

scholarly journals Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

2021 ◽  
Author(s):  
Sehi L'Yi ◽  
Qianwen Wang ◽  
Fritz Lekschas ◽  
Nils Gehlenborg
Keyword(s):  
Data Visualization ◽  
Data Types ◽  
Interactive Rendering ◽  
Web Based ◽  
Multi Scale ◽  
Data Formats ◽  
Wide Range ◽  
Visualization Techniques ◽  
Diverse Data ◽  
Data Visualizations

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling—a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js and its source code are available at https://gosling.js.org.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

A Perioperative Care Display for Understanding High Acuity Patients

2021 ◽  
Vol 12 (01) ◽  
pp. 164-169
Author(s):  
Laurie Lovett Novak ◽  
Jonathan Wanderer ◽  
David A. Owens ◽  
Daniel Fabbri ◽  
Julian Z. Genkins ◽  
...  
Keyword(s):  
Data Visualization ◽  
Design Process ◽  
Cognitive Task ◽  
Development Project ◽  
Clinical Informatics ◽  
Data Display ◽  
Data Types ◽  
Iterative Design ◽  
High Acuity ◽  
Depth Interviews

Abstract Background The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.

Toward 3D data visualization using virtual reality tools

2021 ◽  
Vol 92 (3) ◽  
pp. 033528
Author(s):  
J. L. Kline ◽  
P. L. Volegov
Keyword(s):  
Virtual Reality ◽  
Data Visualization ◽  
3D Data

scholarly journals Big Data Analytic Framework for Organizational Leverage

2021 ◽  
Vol 11 (5) ◽  
pp. 2340
Author(s):  
Sanjay Mathrani ◽  
Xusheng Lai
Keyword(s):  
Big Data ◽  
Web Sites ◽  
Big Data Analytics ◽  
Building Blocks ◽  
Application Service ◽  
Data Application ◽  
Other Information ◽  
Big Data Application ◽  
Diverse Data ◽  
Framework System

Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.

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