Daniel J. Crichton

GLIDE: a grid-based light-weight infrastructure for data-intensive environments

The promise of the grid is that it will enable public access and sharing of immense amounts of computational and data resources among dynamic coalitions of individuals and institutions. However, the current grid solutions make several limiting assumptions that curtail their widespread adoption in the emerging decentralized, resource constrained, embedded, autonomic, and mobile (DREAM) environments: they are designed primarily for highly com plex scientific problems, and therefore require powerful hardware and reliable network connectivity; additionally, they provide no application design sup port to grid users (e.g., scientists). To address these limitations, we present GLIDE, a prototype light-weight, data-intensive middleware infrastructure that enables access to the robust data and computational power of the grid on DREAM platforms. We illustrate GLIDE on an example file sharing application. We discuss our early experience with GLIDE and present a set of open research questions.

A Science Data System Architecture for Information Retrieval

Science research generates an enormous amount of data that is located in geographically distributed data repositories. The data generated by these efforts are often captured and managed without reference to any standard principles of information architecture. Interoperability and efficient search and retrieval of data products across disparate data systems is difficult because users are often required to connect to each individual data system and deal with dissimilar and often unfamiliar interfaces and semantics. It makes the development of software systems that work across organizational and disciplinary boundaries challenging if the organizing principles that construct the information architecture are not explicitly defined. Clustering data results across multiple information systems is challenging without a system architecture that provides both the data and distributed systems architecture and standards.

Sharing Satellite Observations with the Climate-Modeling Community: Software and Architecture

The disparate communities of climate modeling and remote sensing are finding economic, political, and societal benefit from the direct comparisons of climate model outputs to satellite observations, using these comparisons to help tune models and to provide ground truth in understanding the Earths climate processes. In the context of the Intergovernmental Panel on Climate Change (IPCC) and its upcoming 5th Assessment Report (AR5), the authors have been working with principals in both communities to build a software infrastructure that enables these comparisons. This infrastructure must overcome several software engineering challenges, including bridging heterogeneous data file formats and metadata formats, transforming swath-based remotely sensed data into globally gridded datasets, and navigating and aggregating information from the largely distributed ecosystem of organizations that house these climate model outputs and satellite data. The authors focus in this article is on the description of software tools and services that meet these stringent challenges, and on informing the broader communities of climate modelers, remote sensing experts, and software engineers on the lessons learned from their experience so that future systems can benefit and improve upon their existing results.

Experiments with Storage and Preservation of NASA's Planetary Data via the Cloud

The computing and storage demands force to optimize and manage complex and often conflicting software engineering challenges. Several domain-specific, independent software solutions have been developed to manage large amounts of data, including grid computing platforms- specifically, data-grid software packages such as the Globus Toolkit, DSpace, and OODT (ObjectOriented Data Technology). In addition, several computationally focused software products are geared toward executing large numbers of jobs, including workflow technologies such as Condor and Pegasus, and batch submission systems like the Portable Batch System (PBS) and Torque. The use of cloud computing in NASAs Planetary Data System for large-volume data storage and preservation illustrates how clouds can help researchers meet modern data backup demands, which are approaching the petabyte scale.

Intelligent resource discovery using ontology-based resource profiles

Successful resource discovery across heterogeneous repositories is highly dependent on the semantic and syntactic homogeneity of the associated resource descriptions in each repository. Ideally, consistent resource descriptions are easily extracted from each repository, expressed using standard syntactic and semantic structures, and managed and accessed within a distributed, flexible, and scalable software framework. In practice however, seldom do all three of these elements exist. To help address this situation, the Object Oriented Data Technology (OODT) project at the Jet Propulsion Laboratory has developed an extensible, standards-based resource description scheme that provides the necessary description and management facilities for the discovery of resources across heterogeneous repositories. The OODT resource description scheme can be used across scientific domains to describe any resource. It uses a small set of generally accepted, broadly-scoped descriptors while also providing a mechanism for the inclusion of domain-specific descriptors. In addition, the OODT scheme can be used to capture hierarchical, relational and recursive relationships between resources. In this paper we expand on prior work and describe an intelligent resource discovery framework that consists of separate software and data architectures focusing on the standard resource description scheme. We illustrate intelligent resource discovery using a case study that provides efficient search across distributed repositories using common interfaces and a hierarchy of resource descriptions derived from a complex, domain-specific ontology.

Understanding Open Source Software at NASA

To provide a framework for comparing and understanding open source software at NASA, the authors describe a set of relevant dimensions and decision points that NASA and other government agencies can use in formulating an open source strategy.

Understanding architectural tradeoffs necessary to increase climate model intercomparison efficiency

NASAs Jet Propulsion Laboratory, in partnership with Lawrence Livermore National Laboratory, has been leading an effort to allow remote sensing data available from NASA satellites to be easily compared with climate model outputs available from the DOE-funded Earth System Grid, a national asset in climate science. This partnership is timely with the looming Intergovernmental Panel on Climate Change (IPCC)s 5th Assement Report (AR5) in active discussion, and the metrics to better understand Earths climate under formulation. JPLs project, titled the Climate Data eXchange (CDX) provides an easy-to-use software framework for cimate scientists to rapidliy integrate and evaluate the efficacy of observational data as applied to climate models.

Scientific digital libraries, interoperability, and ontologies

Scientific digital libraries serve complex and evolving research communities. Justifications for the development of scientific digital libraries include the desire to preserve science data and the promises of information interconnectedness, correlative science, and system interoperability. Research [1] suggests single shared ontologies are fundamental to fulfilling these promises. We present a tool framework, a set of principles, and a real world case study where shared ontologies are used to develop and manage science information models and subsequently guide the implementation of scientific digital libraries. The tool framework, based on an ontology modeling tool as illustrated in Figure 1, was configured to develop, manage, and keep shared ontologies relevant within changing domains and to promote the interoperability, interconnectedness, and correlation desired by scientists.

The EDRN knowledge environment: an open source, scalable informatics platform for biological sciences research

We describe here the Early Detection Research Network (EDRN) for Cancer’s knowledge environment. It is an open source platform built by NASA’s Jet Propulsion Laboratory with contributions from the California Institute of Technology, and Giesel School of Medicine at Dartmouth. It uses tools like Apache OODT, Plone, and Solr, and borrows heavily from JPL’s Planetary Data System’s ontological infrastructure. It has accumulated data on hundreds of thousands of biospecemens and serves over 1300 registered users across the National Cancer Institute (NCI). The scalable computing infrastructure is built such that we are being able to reach out to other agencies, provide homogeneous access, and provide seamless analytics support and bioinformatics tools through community engagement.

From stars to patients: Lessons from space science and astrophysics for health care informatics

Big Data are revolutionizing nearly every aspect of the modern society. One area where this can have a profound positive societal impact is the field of Health Care Informatics (HCI), which faces many challenges. The key idea behind this study is: can we use some of the experience and technical and methodological solutions from the fields that have successfully adapted to the Big Data era, namely astronomy and space science, to help accelerate the progress of HCI? We illustrate this with examples from the Virtual Observatory framework, and the NCI EDRN project. An effective sharing and reuse of tools, methods, and experiences from different fields can save a lot of effort, time, and expense. HCI can thus benefit from the proven solutions to big data challenges from other domains.