Emily Law

An automated deep space communications station

This paper describes an architecture being implemented for an autonomous Deep Space Tracking Station(DS-T). The architecture targets fully automated routine operations encompassing scheduling and resource allocation, antenna and receiver predict generation, track procedure generation from service requests, and closed loop control and error recovery for the station subsystems. This architecture is being validated by construction of a prototype DS-T station which will be demonstrated in two phases: down-link (March 98) and up-link/down-link(July 98).

The past, present, and future of ground station automation within the DSN

This paper describes an architecture for an autonomous Deep Space Tracking Station (DS-T). The architecture targets fully automated routine operations encompassing scheduling and resource allocation, antenna and receiver predict generation, track procedure generation from service requests, and closed loop control and error recovery for the station subsystems. This architecture has been validated by the construction of a prototype DS-T station, which has performed a series of demonstrations of autonomous ground station control for downlink services with NASAs Mars Global Surveyor.

PDS4: A model-driven planetary science data architecture for long-term preservation

The goal of the Planetary Data System (PDS) is the digital preservation of scientific data for long-term use by the scientific research community. After two decades of successful operation, the PDS found itself in a new era of big data, international cooperation, distributed nodes, and multiple ways of analysing and interpreting data. A project was formed to develop a disciplined architectural approach that would drive the design and implementation of a scalable data system that could evolve to meet the demands of this new era. PDS4, the next generation system, uses an explicit model-driven architectural approach coupled with modern information technologies and standards to meet these challenges in order to ensure the planetary data assets can be mined for scientific knowledge for years to come.

From Sky to Earth: Data Science Methodology Transfer.

We describe here the parallels in astronomy and earth science datasets, their analyses, and the opportunities for methodology transfer from astroinformatics to geoinformatics. Using example of hydrology, we emphasize how meta-data and ontologies are crucial in such an undertaking. Using the infrastructure being designed for EarthCube - the Virtual Observatory for the earth sciences - we discuss essential steps for better transfer of tools and techniques in the future e.g. domain adaptation. Finally we point out that it is never a one-way process and there is enough for astroinformatics to learn from geoinformatics as well.

Demonstration of LMMP automated performance testing using cloud computing architecture

The Lunar Mapping and Modeling Project (LMMP) is currently being built by NASA. The goal is to provide a single point of access to the best state of knowledge of the moons terrain, rock and crater fields, resource maps, lighting conditions and thermal conditions. The project uses cloud computing scalable infrastructure to support users. This demonstration will show how cloud computing can be used to support large scale automated testing, simulating users from around the world, in a cost effective manner.

A Scalable Planetary Science Information Architecture for Big Science Data

Research has shown that the amount of data now available often overwhelms key functions of an information system. This situation necessitates the design of information architectures that scale to meet the challenges. The Planetary Data System, a NASA funded project, has developed an information architecture for the planetary science community that addresses this and other big science data issues noted in a National Research Council report regarding architectures for big data management and analysis and end-to-end data lifecycle management across diverse disciplines. The report identified enabling technology trends including distributed systems, service-oriented architectures, ontologies, models and information representation, scalable database systems, federated data security mechanisms, and technologies for moving big data. This paper will present the PDS4 information architecture, its successful implementation in a multi-discipline big-data environment.

Architecting Scientific Data Systems in the Cloud

Scientists, educators, decision makers, students, and many others utilize scientific data produced by science instruments. They study our universe, make new discoveries in areas such as weather forecasting and cancer research, and shape policy decisions that impact nations fiscally, socially, economically, and in many other ways. Over the past 20 years or so, the data produced by these scientific instruments have increased in volume, complexity, and resolution, causing traditional computing infrastructures to have difficulties in scaling up to deal with them. This reality has led us, and others, to investigate the applicability of cloud computing to address the scalability challenges. NASA’s Jet Propulsion Laboratory (JPL) is at the forefront of transitioning its science applications to the cloud environment. Through the Apache Object Oriented Data Technology (OODT) framework, for NASA’s first software released at the open-source Apache Software Foundation (ASF), engineers at JPL have been able to scale the storage and computational aspects of their scientific data systems to the cloud – thus achieving reduced costs and improved performance. In this chapter, we report on the use of Apache OODT for cloud computing, citing several examples in a number of scientific domains. Experience, specific performance, and numbers are also reported. Directions for future work in the area are also suggested.

Demonstration of LMMP (lunar mapping and modeling) using amazon's elastic compute cloud

The Lunar Mapping and Modeling Project (LMMP) is currently being built by NASA. The goal is to provide a single point of access to the best state of knowledge of the moons terrain, rock and crater fields, resource maps, lighting conditions and thermal conditions. The architecture and design employ a variety of technologies, allowing for execution of complex models, the processing of large data sets and the distribution of the information, over the internet, to both authenticated users and the general public. The architecture supports a variety of light-weight clients including a Flash based display, an iPad/iPhone interface and a set of programmatic APIs that allow rich clients to interact with the LMMP system.

Demonstration of LMMP workflow system using cloud computing architecture

The Lunar Mapping and Modeling Project (LMMP) is currently being built by NASA. The goal is to provide a single point of access to the best state of knowledge of the moons terrain, rock and crater fields, resource maps, lighting conditions and thermal conditions. The LMMP contains a workflow system that allows us to allocate jobs to remote computing resources. We will demonstrate this workflow capability.