Search NASASearch

NASA NTRS · 20240002820

VEDA Visualization Exploration & Data Analysis

Abstract

The source did not provide an abstract. Follow the original record for more information.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brian Matthew Freitag, Manil Maskey, Jonas Sølvsteen, Slesa Adhikari, Jerika Hope Christman. VEDA Visualization Exploration & Data Analysis. https://ntrs.nasa.gov/citations/20240002820

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science

Automatic Anomaly Detection with Machine Learning

This presentation will discuss the following topics: Understanding the benefits of automatic anomaly detection Identifying precursors with algorithms. Overcoming the challenges of data issues in automation.

Data Science

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science