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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

GeneLab: A Systems Biology Platform for Omics Analysis: Disseminate and Reuse Data, Tools, and Samples Post-Project

NASA's GeneLab includes an open-access repository of some 200 plus omics datasets generated by biological experiments relevant to spaceflight (including simulated cosmic radiation and microgravity). In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab is now transforming the data in the repository into actual biological and physiological knowledge of the genetic and proteomic signatures found in these samples. This processed data is being derived by establishing standard data analysis workflows vetted by 114 scientists who are members of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). AWG members from institutes spanning the U.S. and four other countries participate on a voluntary basis. The AWGs meet monthly to discuss data mining, compare results and interpretations, and test forthcoming releases of the GeneLab Data Systems (GLDS). GLDS version 3.0 has been available to the general public since October 1st 2018, and has been providing a professional state-of-the-art bioinformatics platform for everyone in the space biology community to upload their data into a space biology omics data commons, to process their data with vetted standard workflows and to compare to existing analyses. The user interface for the platform is being designed to be accessible to a broad variety of users including those with limited bioinformatics experience, including high school and college students who can use it to learn about omics data analysis and space biology. As such, Genelab will constitute a powerful general public outreach capability of NASA and the Space Biology community at large. Data mining of the GeneLab database by the AWG has already started generating very interesting findings, including reports linking specific spaceflight conditions such as radiation, microgravity or carbon dioxide levels to molecular changes seen across various species. In this presentation, we will report on the current and future objectives for GeneLab, and review recent studies reported by the various AWGs relating molecular changes observed in various animal models and tissue with microgravity, radiation, circadian rhythm, hydration and carbon dioxide conditions.

Omics↗
NASA NTRS2012

Improving Spacecraft Data Visualization Using Splunk

EPOXI, like all spacecraft missions, receives large volumes of telemetry data from its spacecraft, DIF. It is extremely important for this data to be updated quickly and presented in a readable manner so that the flight team can monitor the status of the spacecraft. Existing DMD pages for monitoring spacecraft telemetry, while functional, are limited and do not take advantage of modern search technology. For instance, they only display current data points from instruments on the spacecraft and have limited graphing capabilities, making it difficult to see historical data. The DMD pages have fixed refresh rates so the team must often wait several minutes to see the most recent data, even after it is received on the ground. The pages are also rigid and require an investment of time and money to update. To more easily organize and visualize spacecraft telemetry, the EPOXI team has begun experimenting with Splunk, a commercially-available data mining system. Splunk can take data received from the spacecraft's different data channels, often in different formats, and index all the data into a common format. Splunk allows flight team members to search through the different data formats from a single interface and to filter results by time range and data field to make finding specific spacecraft events quick and easy. Furthermore, Splunk provides functions to create custom interfaces which help team members visualize the data in charts and graphs to show how the health of the spacecraft has changed over time.One of the goals of my internship with my mentor, Victor Hwang, was to develop new Splunk interfaces to replace the DMD pages and give the spacecraft team access to historical data and visualizations that were previously unavailable. The specific requirements of these pages are discussed in the next section.

spacecraft missions↗
NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗
NASA NTRS2004

NASA Technology Takes Center Stage

In today's fast-paced business world, there is often more information available to researchers than there is time to search through it. Data mining has become the answer to finding the proverbial "needle in a haystack," as companies must be able to quickly locate specific pieces of information from large collections of data. Perilog, a suite of data-mining tools, searches for hidden patterns in large databases to determine previously unrecognized relationships. By retrieving and organizing contextually relevant data from any sequence of terms - from genetic data to musical notes - the software can intelligently compile information about desired topics from databases.

Source record↗
NASA NTRS2008

Estimating Software-Development Costs With Greater Accuracy

COCOMOST is a computer program for use in estimating software development costs. The goal in the development of COCOMOST was to increase estimation accuracy in three ways: (1) develop a set of sensitivity software tools that return not only estimates of costs but also the estimation error; (2) using the sensitivity software tools, precisely define the quantities of data needed to adequately tune cost estimation models; and (3) build a repository of software-cost-estimation information that NASA managers can retrieve to improve the estimates of costs of developing software for their project. COCOMOST implements a methodology, called '2cee', in which a unique combination of well-known pre-existing data-mining and software-development- effort-estimation techniques are used to increase the accuracy of estimates. COCOMOST utilizes multiple models to analyze historical data pertaining to software-development projects and performs an exhaustive data-mining search over the space of model parameters to improve the performances of effort-estimation models. Thus, it is possible to both calibrate and generate estimates at the same time. COCOMOST is written in the C language for execution in the UNIX operating system.

Baker, Dan↗
NASA NTRS2017

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

route advisories↗
NASA NTRS2017

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

operational acceptability↗
NASA NTRS2006

Using Dissimilarity Metrics to Identify Interesting Designs

A computer program helps to blend the power of automated-search software, which is able to generate large numbers of design solutions, with the insight of expert designers, who are able to identify preferred designs but do not have time to examine all the solutions. From among the many automated solutions to a given design problem, the program selects a smaller number of solutions that are worthy of scrutiny by the experts in the sense that they are sufficiently dissimilar from each other. The program makes the selection in an interactive process that involves a sequence of data-mining steps interspersed with visual displays of results of these steps to the experts. At crucial points between steps, the experts provide directives to guide the process. The program uses heuristic search techniques to identify nearly optimal design solutions and uses dissimilarity metrics defined by the experts to characterize the degree to which solutions are interestingly different. The search, data-mining, and visualization features of the program were derived from previously developed risk-management software used to support a risk-centric design methodology

Feather, Martin↗
NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
NASA NTRS2001

Use of Soft Computing Technologies for a Qualitative and Reliable Engine Control System for Propulsion Systems

The problem to be addressed in this paper is to explore how the use of Soft Computing Technologies (SCT) could be employed to improve overall vehicle system safety, reliability, and rocket engine performance by development of a qualitative and reliable engine control system (QRECS). Specifically, this will be addressed by enhancing rocket engine control using SCT, innovative data mining tools, and sound software engineering practices used in Marshall's Flight Software Group (FSG). The principle goals for addressing the issue of quality are to improve software management, software development time, software maintenance, processor execution, fault tolerance and mitigation, and nonlinear control in power level transitions. The intent is not to discuss any shortcomings of existing engine control methodologies, but to provide alternative design choices for control, implementation, performance, and sustaining engineering, all relative to addressing the issue of reliability. The approaches outlined in this paper will require knowledge in the fields of rocket engine propulsion (system level), software engineering for embedded flight software systems, and soft computing technologies (i.e., neural networks, fuzzy logic, data mining, and Bayesian belief networks); some of which are briefed in this paper. For this effort, the targeted demonstration rocket engine testbed is the MC-1 engine (formerly FASTRAC) which is simulated with hardware and software in the Marshall Avionics & Software Testbed (MAST) laboratory that currently resides at NASA's Marshall Space Flight Center, building 4476, and is managed by the Avionics Department. A brief plan of action for design, development, implementation, and testing a Phase One effort for QRECS is given, along with expected results. Phase One will focus on development of a Smart Start Engine Module and a Mainstage Engine Module for proper engine start and mainstage engine operations. The overall intent is to demonstrate that by employing soft computing technologies, the quality and reliability of the overall scheme to engine controller development is further improved and vehicle safety is further insured. The final product that this paper proposes is an approach to development of an alternative low cost engine controller that would be capable of performing in unique vision spacecraft vehicles requiring low cost advanced avionics architectures for autonomous operations from engine pre-start to engine shutdown.

Trevino, Luis↗
NASA NTRS2014

Effects of Long Duration Spaceflight on Venous and Arterial Compliance in Astronants

1. Project Overview Visual impairment and intracranial pressure (VIIP) is a spaceflight-associated medical condition affecting at least a third of American astronauts who have flown International Space Station (ISS) missions. VIIP is defined primarily by visual acuity deficits and anatomical changes to eye structures. In some astronauts, eye-related changes do not revert back to the preflight state upon return to Earth. Our team will study some of the possible causes for this syndrome. This will be achieved by reviewing previous astronaut data for factors that may predispose astronauts to higher rates of developing this syndrome or greater severity of symptoms. Additionally, we will conduct 3 separate experiments that will characterize vessels in the head and neck and measure the effects of the experimental conditions on ocular structures and function. 2. Technical Summary The primary objective of this study is to determine whether vascular compliance is altered by spaceflight and whether such adaptations are related to the incidence of the VIIP. In particular, we will measure ocular parameters and vascular compliance in vessels of the head and neck in astronauts who have no spaceflight experience (Ground), in astronauts before, during, and after spaceflight (Flight), and in bed rest subjects with conditions similar to spaceflight (Bed Rest). Additionally, we will analyze astronaut data from the Lifetime Surveillance of Astronaut Health (LSAH) archives to determine which factors might be predictive of the development of VIIP (Data Mining). The project will be conducted in four separate, but related parts. Hypothesis The central hypothesis of this proposal is that exposure to the spaceflight environment aboard the ISS may lead to development of the VIIP syndrome (increased intracranial pressure and impaired visual acuity) and that this may be related to alterations in venous and/or arterial compliance in the head and neck. Specific Aims 1. To determine whether noninvasive measures of venous and arterial compliance are altered by long-duration spaceflight exposure in ISS astronauts and whether these changes are related to the development of the VIIP syndrome. (Flight) 2. To determine whether previous spaceflight experience predispose astronauts to lower venous compliance and/or the development of the VIIP syndrome. (Ground + Flight) 3. To use a 14-day, 6deg head-down-tilt bed rest as a model of spaceflight, to evaluate the effect of aging on vascular compliance using a subject population similar to younger (25-35 yr) and older (45-55 yr) astronaut cohorts. (Bed Rest) 4. To determine what factors contribute to lower venous compliance and/or the development of the VIIP syndrome in astronauts. (Data Mining) 3. Earth Applications This research may inform the mechanisms that regulate blood/fluid flow in and out of the brain in the head and neck. This information may help with understanding of the mechanisms behind idiopathic intracranial hypertension. 4. Link to NASA Taskbook Entry Not Yet Available

Platts, Steven↗
NASA NTRS1998

Mining for Data

AbTech Corporation used an F-18 HARV (High Alpha Research Vehicle) simulation developed by NASA to create an interactive computer-based prototype of the MQ (Model Quest) SV (System Validator) tool. Dryden Flight Research Center provided support to develop, test, and rapidly reprogram the validation function. AbTech's ModelQuest Enterprises highly automated and outperforms other modeling techniques to quickly discover meaningful relationships, patterns, and trends in databases. Applications include technical and business professionals in finance, marketing, business, banking, retail, healthcare, and aerospace.

Source record↗
NASA NTRS2002

Mining Marketing Data

MarketMiner(R) Products, a line of automated marketing analysis tools manufactured by MarketMiner, Inc., can benefit organizations that perform significant amounts of direct marketing. MarketMiner received a Small Business Innovation Research (SBIR) contract from NASA's Johnson Space Center to develop the software as a data modeling tool for space mission applications. The technology was then built into the company current products to provide decision support for business and marketing applications. With the tool, users gain valuable information about customers and prospects from existing data in order to increase sales and profitability. MarketMiner(R) is a registered trademark of MarketMiner, Inc.

Source record↗
NASA NTRS2005

Tools and Data Services from the NASA Earth Satellite Observations for Climate Applications

Climate science and applications require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation to data reduction, combination and data product production. This time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions. In order to address these issues the Goddard Space Flight Center (GSFC) Earth Sciences (GES), Data and Information Service Center (DISC) Distributed Active Archive Center (DAAC) has made great progress in facilitating science and applications research by developing innovative tools and data services applied to the Earth sciences atmospheric and climate data. The GES/DISC/DAAC has successfully implemented and maintained a long-term climate satellite data archive and developed tools and services to a variety of atmospheric science missions including AIRS, AVHRR, MODIS, SeaWiFS, SORCE, TOMS, TOVS, TRMM, and UARS and Aura instruments providing researchers with excellent opportunities to acquire accurate and continuous atmospheric measurements. Since the number of climate science products from these various missions is steadily increasing as a result of more sophisticated sensors and new science algorithms, the main challenge for data centers like the GES/DISC/DAAC is to guide users through the variety of data sets and products, provide tools to visualize and reduce the volume of the data and secure uninterrupted and reliable access to data and related products. This presentation will describe the effort at the GES/DISC/DAAC to build a bridge between multi-sensor data and the effective scientific use of the data, with an emphasis on the heritage satellite observations and science products for climate applications. The intent is to inform users of the existence of this large collection of data and products; suggest starting points for cross-platform science projects and data mining activities and provide data services and tools information. More information about the GES/DISC/DAAC satellite data and products, tools, and services can be found at http://daac.gsfc.nasa.gov.

Vicente, Gilberto A.↗