Advancing Critical Care in the ICU: A Human-Centered Biomedical Data Visualization Systems

The objective of this paper is to demonstrate how GIS and data visualization systems can be used to identify spatial relationships to add to our understanding of railroad accident factors. Examples are given of the spatial analysis of broken rail accidents and grade crossing accidents on GIS maps. Additionally, using the Weave data visualization system a data dashboard was constructed that shows the complex interaction between variables like track type, FRA track classification, train speed and track density with broken rail accident causes. The findings indicate that broken rail accidents occur most frequently in the Midwest. Possibly this trend is related to climate change and increased temperatures and precipitation in the United States. GIS visualizations also showed that many truck-trailer accidents at grade crossings occur in low population areas. This work indicates that GIS and data visualizations are a useful method of identifying trends in railroad accidents.

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DBMap: A Space-Conscious Data Visualization and Knowledge Discovery Framework for Biomedical Data Warehouse

IEEE Transactions on Information Technology in Biomedicine ◽

10.1109/titb.2004.832550 ◽

2004 ◽

Vol 8 (3) ◽

pp. 343-353 ◽

Cited By ~ 4

Author(s):

M. Zhang ◽

H. Zhang ◽

D. Tjandra ◽

S.T.C. Wong

Keyword(s):

Knowledge Discovery ◽

Data Warehouse ◽

Data Visualization ◽

Biomedical Data

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Trrack: A Library for Provenance Tracking in Web-Based Visualizations

10.31219/osf.io/wnctb ◽

2020 ◽

Author(s):

Zachary T Cutler ◽

Kiran Gadhave ◽

Alexander Lex

Keyword(s):

Data Visualization ◽

Ad Hoc ◽

Use Cases ◽

Software Library ◽

Web Based ◽

Provenance Data ◽

Wide Range ◽

Visualization Systems ◽

Simple Action ◽

Easy Integration

Provenance tracking is widely acknowledged as an important component of visualization systems. By tracking provenance data, visualization designers can achieve a wide variety of important functionality, ranging from action recovery (undo/redo), reproducibility, collaboration and sharing, to logging in support of quantitative and longitudinal evaluation. Yet, for web-based visualizations, there are currently no libraries that make provenance tracking easy to implement in visualization systems. The result of this is that visualization designers either develop ad-hoc solutions that are rarely comprehensive, or don't track provenance at all. In this paper, we introduce a web-based software library --- Trrack --- that is designed for easy integration in existing or future visualization systems. Trrack supports a wide range of use cases, from simple action recovery, to capturing intent and reasoning, and can be used to share states with collaborators and store provenance on a server. Trrack also includes an optional provenance visualization component that supports annotation of states and aggregation of events.

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Condensed Design Project: Promote Knowledge in Design History with Dynamic Data Visualization Systems Based on the Javascript, PHP and HTML5 Technologies

10.5151/despro-sigradi2013-0025 ◽

2013 ◽

Author(s):

José Neto de Faria ◽

Karine Itao Palos ◽

Katia Akemi Omine

Keyword(s):

Data Visualization ◽

Design History ◽

Dynamic Data ◽

Design Project ◽

Visualization Systems

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Conventional Data Science Techniques to Bioinformatics and Utilizing a Grid Computing Approach to Computational Medicine

10.31224/osf.io/h8cmw ◽

2020 ◽

Author(s):

Andrew Kamal

Keyword(s):

Parallel Processing ◽

Grid Computing ◽

Data Visualization ◽

Data Science ◽

Biomedical Data ◽

Research Opportunities ◽

Adversarial Network ◽

Visualization Software ◽

Computing Approach ◽

Processing Architecture

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

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