Search NASA⌕ Search

SEARCH · Search NASA

Results for “Analyst”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Women in Science: Fermilab computing analyst Margherita Vittone-Wiersma

With February 11 marking the International Day of Women and Girls in Science, female physicists, engineers and computer scientists from CERN and Fermilab share their experiences of building careers in science. Margherita Vittone-Wiersma came to Fermilab as a physicist in February 1985. Together with a group of researchers from Italy's Institute of Nuclear Physics, she began a new experiment called E687. But she soon learned that she preferred working on data acquisition and the many methods to store and access the huge amount of data coming out of science experiments. What was once mere megabytes of data is now terabytes of information - and experimental physicists need to be able to retrieve stored data and monitor data coming from the detectors in real-time. "It's been an incredible change in scale of the amount of data that is handled by computers and has to be analyzed by the physicists," she says. Margherita migrated from physics to her new role in computing through lots of hands-on learning and classes in different programming languages and software development offered at Fermilab. She officially joined the lab's computing division in 1989. "It's a great experience to work with the physicists," she says. Together physicists and computing experts iterate over tools "to make sure that everything is working the way they expect. It's very rewarding." Credits: Video production - Fermilab Creative Services Music Copyright: Universal Production Music Astral Light Chevalier Idea originally conceived by Tanya Levshina, Fermilab and Maria Dimou, CERN.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Use Case Realization Report: UCR-03.02 Refines Event

This architecturally significant use case describes how the Analyst refines an event hypothesis. The Analyst checks waveform quality (see 'Determines Waveform Data Quality' UC). For waveforms of sufficient quality, the Analyst enhances signals and suppresses noise on waveforms for relevant stations (see 'Enhances Signals' UC), adds and associates missing detections, and modifies or unassociates detections already associated with the event hypothesis (see 'Detects Signals' UC). The Analyst rejects event hypotheses that are invalid. For valid event hypotheses, the Analyst measures signal features associated with the detections (see 'Measures Signal Features' UC) and evaluates the moment tensor ('Evaluates Moment Tensor' UC). The Analyst uses these signal features to refine the location (see 'Refines Event Location' UC) and magnitude (see 'Refines Event Magnitude' UC) of the event hypothesis. The Analyst compares events to determine how similar events were constructed (see 'Compares Events' UC). The Analyst repeats these steps until satisfied with the results. Analysts may provide feedback for previous Analysts during any of these steps (see 'Provides Analyst Feedback' UC). This use case is architecturally significant because it captures the interplay between all of the Analyst activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Data Intensive Architecture for Scalable Cyber Analytics

Cyber analysts are tasked with the identification and mitigation of network exploits and threats. These compromises are difficult to identify due to the characteristics of cyber communication, the volume of traffic, and the duration of possible attack. In this paper, we describe a prototype implementation designed to provide cyber analysts an environment where they can interactively explore a month’s worth of cyber security data. This prototype utilized On-Line Analytical Processing (OLAP) techniques to present a data cube to the analysts. The cube provides a summary of the data, allowing trends to be easily identified as well as the ability to easily pull up the original records comprising an event of interest. The cube was built using SQL Server Analysis Services (SSAS), with the interface to the cube provided by Tableau. This software infrastructure was supported by a novel hardware architecture comprising a Netezza TwinFin® for the underlying data warehouse and a cube server with a FusionIO drive hosting the data cube. We evaluated this environment on a month’s worth of artificial, but realistic, data using multiple queries provided by our cyber analysts. As our results indicate, OLAP technology has progressed to the point where it is in a unique position to provide novel insights to cyber analysts, as long as it is supported by an appropriate data intensive architecture.

Olsen, Bryan K.↗
DOE OSTI2019

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than two orders of magnitude over the first round, where multiple analysis methods are allowed. This article highlights many pitfalls in conducting 'routine' degradation analysis, and it addresses some of the factors that must be considered when comparing degradation results reported by different analysts or methods.

14 SOLAR ENERGY↗