Enabling Analytics in the Cloud for Earth Science Data

Ramachandran, Rahul; Lynnes, Christopher; Bingham, Andrew W.; Quam, Brandi M.

Home/Archive/NASA NTRS

NASA NTRS · 20180002954

Enabling Analytics in the Cloud for Earth Science Data

Ramachandran, Rahul·Lynnes, Christopher·Bingham, Andrew W.·Quam, Brandi M.

View original source ↗Export metadata ↓

Abstract

The purpose of this workshop was to hold interactive discussions where providers, users, and other stakeholders could explore the convergence of three main elements in the rapidly developing world of technology: Big Data, Cloud Computing, and Analytics, [for earth science data].

Explore related subjects

Analytics Earth Science Cloud

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ramachandran, Rahul, Lynnes, Christopher, Bingham, Andrew W., Quam, Brandi M.. 2018-02-21. Enabling Analytics in the Cloud for Earth Science Data. https://ntrs.nasa.gov/citations/20180002954

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

KEEP EXPLORING

Related reports

DOE OSTI2025

An Optimization-Based HVAC Load and PV Disaggregation Methodology With Contextual Supervision

Here, this letter presents an optimization-based Heating, Ventilation, and Air Conditioning (HVAC) and PV disaggregation approach. This letter builds on the previous works of authors, which discuss HVAC disaggregation strategy for aggregated levels without requiring sub-metered information. This letter expands on previous works to discuss optimization-based HVAC disaggregation by varying PV penetration scenarios with joint disaggregation of HVAC load and PV.

Analytics↗

NASA NTRS2019

Smart Handoffs: Preserving User Context Between Tools and Services Related to NASA's EOSDIS Data Archive

NASA's Earth Observing System Data and Information System (EOSDIS) is tasked with archiving and distributing Earth Observation data across a range of disciplines, including atmospheric science, oceanography, land processes, natural hazards, solar radiance and even socioeconomic aspects relating to the environment. Given the breadth of disciplines and depth of data that EOSDIS provides, the efficient and intuitive discovery and usage of data by a scientist is of paramount importance. An effective data gathering workflow may involve switching from general use discovery tools to a more bespoke services designed specifically for the scientist's discipline. Providing concrete interoperability between such tools could vastly improve the efficiency of a scientist's workflow.

Analytics↗

NASA NTRS2019

NASA GES DISC Giovanni: Current and Future

Giovanni (Geospatial Interactive Online Visualization and Analysis Infrastructure), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), has established a reputation among NASA users for easy access, analysis, and visualization of NASA Earth science data. Currently, Giovanni supports over 1900 variables in eight disciplinary areas. Like any other enterprise application, Giovanni faces big data challenges, such as servicing increasingly large data volumes and more complex data types, while at the same time addressing the demands of a more diverse user community, e.g., placing requests for long-term time series from multiple spatially and temporally dense data records. I will present how Giovanni has been evolving from an on-premises, monolithic software application towards a cloud-enabled implementation to address these challenges.

Analytics↗