Extending the LWS Data Environment: Distributed Data Processing and Analysis

The final stages of this work saw changes to the original framework, as well as the completion and integration of several data processing services. Initially, it was thought that a peer-to-peer architecture was necessary to make this work possible. The peer-to-peer architecture provided many benefits including the dynamic discovery of new services that would be continually added. A prototype example was built and while it showed promise, a major disadvantage was seen in that it was not easily integrated into the existing data environment. While the peer-to-peer system worked well for finding and accessing distributed data processing services, it was found that its use was limited by the difficulty in calling it from existing tools and services. After collaborations with members of the data community, it was determined that our data processing system was of high value and that a new interface should be pursued in order for the community to take full advantage of it. As such; the framework was modified from a peer-to-peer architecture to a more traditional web service approach. Following this change multiple data processing services were added. These services include such things as coordinate transformations and sub setting of data. Observatory (VHO), assisted with integrating the new architecture into the VHO. This allows anyone using the VHO to search for data, to then pass that data through our processing services prior to downloading it. As a second attempt at demonstrating the new system, a collaboration was established with the Collaborative Sun Earth Connector (CoSEC) group at Lockheed Martin. This group is working on a graphical user interface to the Virtual Observatories and data processing software. The intent is to provide a high-level easy-to-use graphical interface that will allow access to the existing Virtual Observatories and data processing services from one convenient application. Working with the CoSEC group we provided access to our data processing tools from within their software. This now allows the CoSEC community to take advantage of our services and also demonstrates another means of accessing our system

From Science to e-Science to Semantic e-Science: A Heliosphysics Case Study

The past few years have witnessed unparalleled efforts to make scientific data web accessible. The Semantic Web has proven invaluable in this effort; however, much of the literature is devoted to system design, ontology creation, and trials and tribulations of current technologies. In order to fully develop the nascent field of Semantic e-Science we must also evaluate systems in real-world settings. We describe a case study within the field of Heliophysics and provide a comparison of the evolutionary stages of data discovery, from manual to semantically enable. We describe the socio-technical implications of moving toward automated and intelligent data discovery. In doing so, we highlight how this process enhances what is currently being done manually in various scientific disciplines. Our case study illustrates that Semantic e-Science is more than just semantic search. The integration of search with web services, relational databases, and other cyberinfrastructure is a central tenet of our case study and one that we believe has applicability as a generalized research area within Semantic e-Science. This case study illustrates a specific example of the benefits, and limitations, of semantically replicating data discovery. We show examples of significant reductions in time and effort enable by Semantic e-Science; yet, we argue that a "complete" solution requires integrating semantic search with other research areas such as data provenance and web services

On the Role of Context and Subjectivity on Scientific Information Systems

The explicit representation of context and subjectivity enables an information system to support multiple interpretations of the data it records. This is a crucial aspect of learning and innovation within scientific information systems. We present an ontology-based framework for context and subjectivity that integrates two lines of research: data provenance and ontological foundations of the Semantic Web. Data provenance provides a set of constructs for representing data history. We extend the definition of these constructs in order to describe multiple viewpoints or interpretations held within a domain. The W7 model, the Toulmin model, and the Proof Markup Language (PML) provide the Interlingua for creating multiple viewpoints of data in a machine-readable and sharable form. Example use cases in space sciences are used to demonstrate the feasibility and value of our approach

The Semantic Web in Federated Information Systems: A Space Physics Case Study

This paper presents a new theoretical contribution that provides a middle-of-the-road approach to formal ontologies in federated information systems. NASA’s space physics domain, like many other domains, is relatively unfamiliar with the emerging Semantic Web. This work offers a new framework that garners the benefits of formal logic yet shields participants and users from the details of the technology. Moreover, the results of a case study involving the utilization of the Semantic Web within NASA’s space physics domain are presented. A real-world search and retrieval system, relying on relational database technology, is compared against a near identical system that incorporates a formal ontology. The efficiency, efficacy, and implementation details of the Semantic Web are compared against the established relational database technology

An Ontology Pattern for Oceanographic Cruises: Towards an Oceanographer\u27s Dream of Integrated Knowledge Discovery

EarthCube is a major effort of the National Science Foundation to establish a next-generation knowledge architecture for the broader geosciences. Data storage, retrieval, access, and reuse are central parts of this new effort. Currently, EarthCube is organized around several building blocks and research coordination networks. OceanLink is a semantics enabled building block that aims at improving data retrieval and reuse via ontologies, Semantic Web technologies, and Linked Data for the ocean sciences. Cruises, in the sense of research expeditions, are central events for ocean scientists. Consequently, information about these cruises and the involved vessels has to be shared and made retrievable. For example, the ability to find cruises in the vicinity of physiographic features of interest, e.g., a hydrothermal vent field or a fracture zone, is of primary interest for oceanographers. In this paper, we use a design pattern-centric strategy to engineer ontologies for OceanLink. We provide a formal axiomatization of the introduced patterns and ontologies using the Web Ontology Language, explain design choices, discuss the re-usability of our models, and provide lessons learned for the future geo-ontologies

Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.Comment: Heliophysics 2050 White Pape

Crowdsourcing Semantics for Big Data in Geoscience Applications

The interleaving of human, machine, and semantics have the potential to overcome some of the issues currently surrounding Big Data. Semantic technologies, in particular, have been shown to adequately address data integration when dealing with data size, variety, and complexity of data sources – the very definition of Big Data. Yet, for some tasks, semantic algorithms do not reach a level of accuracy that many production environments require. In this position paper, we argue that augmenting such algorithms with crowdsourcing is a viable solution. In particular, we examine Big Data within the geosciences and describe outstanding questions regarding the merger of crowdsourcing and semantics. We present our ongoing work in this area and discuss directions for future research

A Scheme for finding the Front Boundary of an Interplanetary Magnetic Cloud

We developed a scheme for finding the front boundary of an interplanetary magnetic cloud (MC) based on criteria that depend on the possible existence of any one or all of six specific solar wind features. The features that the program looks for, within +/- 2 hours of a preliminarily determined time for the front boundary, estimated either by visual inspection or by an automatic MC identification scheme, are: (1) a sufficiently large directional discontinuity in the interplanetary magnetic field (IMF), (2) existence of a magnetic hole, (3) a significant proton plasma beta drop, (4) a significant proton temperature drop, (5) a marked increase in the IMF's intensity, and (6) a significant decrease in a normalized root-mean-square deviation (RMS)of the magnetic field - where the scheme was tested using 5, 10, 15, and 20 minute averages of the relevant physical quantities, in order to find the optimum average (and RMS) to use. Other criteria, besides these six, were examined and dismissed as not reliable, e.g., plasma speed. The scheme was developed specifically for aiding in forecasting the strength and timing of a geomagnetic storm due to the passage of an interplanetary MC in real-time, but can be used in post ground-data collection for imposition of consistency in choosing a MC's front boundary. The scheme has been extensively tested, first using 80 bona fide MCs over about 9 years of WIND data, and also for 121 MC-like structures as defined by a program that automatically identifies such structures over the same period. Optimum limits for various parameters in the scheme were found by statistical studies of the WIND MCs. The resulting limits can be user-adjusted for other data sets, if desired. Final testing of the 80 MCs showed that for 50 percent of the events the boundary estimates occurred within +/-10 minutes of visually determined times, 80 percent occurred within +/-30 minutes, and 91 percent occur within +/-60 minutes, and three or more individual boundary tests were passed for 88 percent of the total MCs. The scheme and its testing will be described