Engineering topics

Schnase, John L.

Publications and source records attributed to Schnase, John L..

14 records

NASA NTRS2018

System and Method for Providing a Climate Data Analytic Services Application Programming Interface

A system, method and computer-readable storage devices for providing a climate data analytic services application programming interface. The system includes a programming library that enables client device software to invoke the capabilities of a climate data analytics system through requests to various services supported by the climate data analytics system, and also includes a client-side communications interface that enables the programming library's methods to interact with a climate data analytics system's server interface to obtain access to the capabilities of the system. In one implementation, the programming library is implemented in the Python programming language. The programming library can include basic utilities that call a single, server-side method implemented by one of the various services supported by the climate data analytics system, and extended utilities that call a series of basic utilities and/or other extended utilities that have been placed under programmatic control in order to create client-side convenience methods and workflows.

Schnase, John L.↗

NASA NTRS2018

System and Method for Providing a Climate Data Persistence Service

A system, method and computer-readable storage devices for providing a climate data persistence service. A system configured to provide the service can include a climate data server that performs data and metadata storage and management functions for climate data objects, a compute-storage platform that provides the resources needed to support a climate data server, provisioning software that allows climate data server instances to be deployed as virtual climate data servers in a cloud computing environment, and a service interface, wherein persistence service capabilities are invoked by software applications running on a client device. The climate data objects can be in various formats, such as International Organization for Standards (ISO) Open Archival Information System (OAIS) Reference Model Submission Information Packages, Archive Information Packages, and Dissemination Information Packages. The climate data server can enable scalable, federated storage, management, discovery, and access, and can be tailored for particular use cases.

Schnase, John L.↗

NASA NTRS2018

Climatespark: an In-Memory Distributed Computing Framework for Big Climate Data Analytics

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple- dimensional, array-based datasets in various geoscience domains.

Hu, Fei↗

NASA NTRS2016

System and Method for Providing a Climate Data Analytic Services Application Programming Interface Distribution Package

A system, method and computer-readable storage devices for providing a climate data analytic services application programming interface distribution package. The example system can provide various components. The system provides a climate data analytic services application programming interface library that enables software applications running on a client device to invoke the capabilities of a climate data analytic service. The system provides a command-line interface that provides a means of interacting with a climate data analytic service by issuing commands directly to the system's server interface. The system provides sample programs that call on the capabilities of the application programming interface library and can be used as templates for the construction of new client applications. The system can also provide test utilities, build utilities, service integration utilities, and documentation.

Schnase, John L.↗

NASA NTRS2016

A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (10 speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

big data↗

NASA NTRS2016

Climate Analytics as a Service

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.

big data↗

NASA NTRS2014

RECOVER: An Automated Cloud-Based Decision Support System for Post-fire Rehabilitation Planning

RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.

cloud computing↗

NASA NTRS2014

Climate Analytics as a Service

Climate science is a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). CAaaS combines high-performance computing and data-proximal analytics with scalable data management, cloud computing virtualization, the notion of adaptive analytics, and a domain-harmonized API to improve the accessibility and usability of large collections of climate data. MERRA Analytic Services (MERRA/AS) provides an example of CAaaS. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of key climate variables. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, CAaaS is providing the agility required to meet our customers' increasing and changing data management and data analysis needs.

big data↗

NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗

NASA NTRS2013

A New Application to Facilitate Post-Fire Recovery and Rehabilitation in Savanna Ecosystems

The U.S. government spends an estimated $3billion per year to fight forest fires in the United States. Post-fire rehabilitation activities represent a small but essential portion of that total. The Rehabilitation Capability Convergence for Ecosystem Recovery (RECOVER) system is currently under development for Savanna ecosystems in the western U.S. The prototype of this system has been built and will have realworld testing during the summer 2013 fire season. When fully deployed, the RECOVER system will provide the emergency rehabilitation teams with critical and timely information for management decisions regarding stabilization and rehabilitation strategies.

post-fire↗

NASA NTRS2012

Preliminary Evaluation of MapReduce for High-Performance Climate Data Analysis

MapReduce is an approach to high-performance analytics that may be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. We are particularly interested in the potential of MapReduce to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we are prototyping a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. Our initial focus has been on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. Preliminary results suggest this approach can improve efficiencies within data intensive analytic workflows.

Duffy, Daniel Q.↗

NASA NTRS2012

The Invasive Species Forecasting System (ISFS): An iRODS-Based, Cloud-Enabled Decision Support System for Invasive Species Habitat Suitability Modeling

The Invasive Species Forecasting System (ISFS) is an online decision support system that allows users to load point occurrence field sample data for a plant species of interest and quickly generate habitat suitability maps for geographic regions of interest, such as a national park, monument, forest, or refuge. Target customers for ISFS are natural resource managers and decision makers who have a need for scientifically valid, model- based predictions of the habitat suitability of plant species of management concern. In a joint project involving NASA and the Maryland Department of Natural Resources, ISFS has been used to model the potential distribution of Wavyleaf Basketgrass in Maryland's Chesapeake Bay Watershed. Maximum entropy techniques are used to generate predictive maps using predictor datasets derived from remotely sensed data and climate simulation outputs. The workflow to run a model is implemented in an iRODS microservice using a custom ISFS file driver that clips and re-projects data to geographic regions of interest, then shells out to perform MaxEnt processing on the input data. When the model completes, all output files and maps from the model run are registered in iRODS and made accessible to the user. The ISFS user interface is a web browser that uses the iRODS PHP client to interact with the ISFS/iRODS- server. ISFS is designed to reside in a VMware virtual machine running SLES 11 and iRODS 3.0. The ISFS virtual machine is hosted in a VMware vSphere private cloud infrastructure to deliver the online service.

Gill, Roger↗

NASA NTRS2012

The Virtual Climate Data Server (vCDS): An iRODS-Based Data Management Software Appliance Supporting Climate Data Services and Virtualization-as-a-Service in the NASA Center for Climate Simulation

Scientific data services are becoming an important part of the NASA Center for Climate Simulation's mission. Our technological response to this expanding role is built around the concept of a Virtual Climate Data Server (vCDS), repetitive provisioning, image-based deployment and distribution, and virtualization-as-a-service. The vCDS is an iRODS-based data server specialized to the needs of a particular data-centric application. We use RPM scripts to build vCDS images in our local computing environment, our local Virtual Machine Environment, NASA s Nebula Cloud Services, and Amazon's Elastic Compute Cloud. Once provisioned into one or more of these virtualized resource classes, vCDSs can use iRODS s federation capabilities to create an integrated ecosystem of managed collections that is scalable and adaptable to changing resource requirements. This approach enables platform- or software-asa- service deployment of vCDS and allows the NCCS to offer virtualization-as-a-service: a capacity to respond in an agile way to new customer requests for data services.

Schnase, John L.↗

NASA NTRS2005

A Tamarisk Habitat Suitability Map for the Continental US

This paper presents a national-scale map of habitat suitability for a high-priority invasive species, Tamarisk (Tamarisk spp., salt cedar). We successfully integrate satellite data and tens of thousands of field sampling points through logistic regression modeling to create a habitat suitability map that is 90% accurate. This interagency effort uses field data collected and coordinated through the US Geological Survey and nation-wide environmental data layers derived from NASA s MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate the utilization of the map by ranking the lower 48 US states (and the District of Columbia) based upon their absolute, as well as proportional, areas of highly likely and moderately likely habitat for Tamarisk. The interagency effort and modeling approach presented here could be applied to map other harmful species in the US and globally.

Morisette, Jeffrey T.↗