Richard M. Keller

A bookmarking service for organizing and sharing URLs

Abstract Web browser bookmarking facilities predominate as the method of choice for managing URLs. In this paper, we describe some deficiencies of current bookmarking schemes, and examine an alternative to current approaches. We present WebTaggerTM, an implemented prototype of a personal bookmarking service that provides both individuals and groups with a customizable means of organizing and accessing Web-based information resources. In addition, the service enables users to supply feedback on the utility of these resources relative to their information needs, and provides dynamically-updated ranking of resources based on incremental user feedback. Individuals may access the service from anywhere on the Internet, and require no special software. This service greatly simplifies the process of sharing URLs within groups, in comparison with manual methods involving email. The underlying bookmark organization scheme is more natural and flexible than current hierarchical schemes supported by the major Web browsers, and enables rapid access to stored bookmarks.

Long-term manipulations of intact microbial mat communities in a greenhouse collaboratory: simulating earth's present and past field environments.

Photosynthetic microbial mat communities were obtained from marine hypersaline saltern ponds, maintained in a greenhouse facility, and examined for the effects of salinity variations. Because these microbial mats are considered to be useful analogs of ancient marine communities, they offer insights about evolutionary events during the >3 billion year time interval wherein mats co-evolved with Earths lithosphere and atmosphere. Although photosynthetic mats can be highly dynamic and exhibit extremely high activity, the mats in the present study have been maintained for >1 year with relatively minor changes. The major groups of microorganisms, as assayed using microscopic, genetic, and biomarker methodologies, are essentially the same as those in the original field samples. Field and greenhouse mats were similar with respect to rates of exchange of oxygen and dissolved inorganic carbon across the mat-water interface, both during the day and at night. Field and greenhouse mats exhibited similar rates of efflux of methane and hydrogen. Manipulations of salinity in the water overlying the mats produced changes in the community that strongly resemble those observed in the field. A collaboratory testbed and an array of automated features are being developed to support remote scientific experimentation with the assistance of intelligent software agents. This facility will permit teams of investigators the opportunity to explore ancient environmental conditions that are rare or absent today but that might have influenced the early evolution of these photosynthetic ecosystems.

Semanticorganizer: a customizable semantic repository for distributed NASA project teams

Semantic Organizer is a collaborative knowledge management system designed to support distributed NASA projects, including multidisciplinary teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work products relevant to multiple projects of differing types. Semantic Organizer is one of the earliest and largest semantic web applications deployed at NASA to date, and has been used in varying contexts ranging from the investigation of Space Shuttle Columbias accident to the search for life on other planets. Although the underlying repository employs a single unified ontology, access control and ontology customization mechanisms make the repository contents appear different for each project team. This paper describes Semantic Organizer, its customization facilities, and a sampling of its applications. The paper also summarizes some key lessons learned from building and fielding a successful semantic web application across a wide-ranging set of domains with disparate users.

Investigation Organizer: the development and testing of a Web-based tool to support mishap investigations

Investigation Organizer (IO) is a collaborative Web-based system designed to support the conduct of mishap investigations. IO provides a common repository for a wide range of mishap related information, and allows investigators to make explicit, shared, and meaningful links between evidence, causal models, findings and recommendations. It integrates the functionality of a database, a common document repository, a semantic knowledge network, a rule-based inference engine, and causal modeling and visualization. Thus far, IO has been used to support four mishap investigations within NASA, ranging from a small property damage case to the loss of the Space Shuttle Columbia. This paper describes how the functionality of IO supports mishap investigations and the lessons learned from the experience of supporting two of the NASA mishap investigations: the Columbia Accident Investigation and the CONTOUR loss investigation.

A knowledge-based prototyping environment for construction of scientific modeling software

Over the past 30 years, scientific software models have played an increasingly prominent role in the conduct of science. Unfortunately, scientific models can be difficult and time-consuming to implement, and there is little software engineering support specifically available for constructing scientific models. Because these models are not easily specified to scientifically-naive programmers, and because the scientist requires intimate knowledge of the code to conduct experiments, many scientists implement their own models. This coding activity takes valuable time away from their primary scientific mission. We have developed a knowledge-based software development tool that assists scientists in prototyping scientific models. With a specialized graphical user interface, the scientist constructs a high-level visual specification that captures the essential computational dependencies in the desired model. The system uses its scientific domain knowledge to ensure that the model being built is consistent and coherent. The final product is an executable prototype of a scientific model. Our tool accelerates the model-building process and eliminates the scientists need to program in a formal language. Furthermore, the models developed with this tool are easier to understand and reuse than typical low-level scientific modeling code. At present, models developed with our system are restricted to those involving non-coupled algebraic and first order ordinary differential equations. Research is ongoing to lessen this restriction and support models with simultaneous equations.

A knowledge-based software development environment for scientific model-building

The authors describe a knowledge-based software development environment for scientific modeling. The SIGMA system provides scientist-users with a high-level data flow language for specifying scientific models. This language is intended to be both easier to use and easier to understand than low-level scientific code. Thus, the language and the software tool should facilitate sharing among users. SIGMA makes use of an extensive store of background domain knowledge to ease the specification acquisition process and to maintain the consistency of an evolving specification. In addition, SIGMA uses its background knowledge to aid in the execution of specified models. The availability of background knowledge enables the system to automatically infer portions of the scientific model specification that normally would be tediously specified by the user. The approach is illustrated with examples from the scientific domain of planetary atmospheric modeling.<<ETX>>

Semantic representation and scale-up of integrated air traffic management data

Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.

Ontologies for aviation data management

Managing complex aviation data can be a significant challenge for any enterprise - whether a government agency, airline, airframe manufacturer, or aviation service provider. To handle this challenge, data models are typically developed to characterize and manage the data generated, used, and stored by a given enterprise. Unfortunately, different data providers employ qualitatively different data models, and this gives rise to problems exchanging data across organizational boundaries. Over the past decade, these problems have motivated data producers and consumers to look toward standardized data exchange models to address data interoperability. In this paper we examine some of these standardized data exchange models and compare them with a new type of data model based on ontologies. Ontology models have emerged in recent years from a confluence of research in the artificial intelligence, semantic web, and information science communities. This paper introduces ontology models, provides several use cases for ontologies relevant to aviation data management, and summarizes state-of-the-art aviation prototype applications that utilize ontologies.

Geospatial Information Integration for Science Activity Planning at the Mars Desert Research Station

NASA’s Mobile Agents project leads coordinated planetary exploration simulations at the Mars Desert Research Station. Through ScienceOrganizer, a Web-based tool for organizing and providing contextual knowledge for scientific datasets, remote teams of scientists access and annotate datasets, images, documents, and other forms of scientific information, applying pre-defined semantic links or meta-data using a Web browser. We designed and developed an experimental geographic information server that integrates remotely-sensed images of scientific activity areas with information regarding activity plans, actors, and data that had been characterized semantically using ScienceOrganizer. The server automatically obtains remotely-sensed photographs of geographic survey sites at various resolutions and combines these images with scientific survey data to generate “context maps” illustrating the paths of survey actors, and the sequence and types of data collected during simulated surface “extra-vehicular activities.” The remotely located scientific team found the context maps were extremely valuable for achieving and conveying activity plan consensus.

Aviation Data Integration System

During the analysis of flight data and safety reports done in ASAP and FOQA programs, airline personnel are not able to assess relevant aviation data for a variety of reasons. This report discusses the Aviation Data Integration System (ADIS), a software system that provides integrated heterogeneous data to support safety analysis. Types of data available on ADIS include weather, D-ATIS, RVR, radar data, and Jeppesen charts, and flight data. Three versions of ADIS were developed to support airlines. The first version has been developed to support ASAP teams. A second version supports FOQA teams, and it integrates aviation data with flight data while keeping identification information inaccessible. Finally, a prototype was developed to demonstrate the integration of aviation data into flight data analysis programs. The initial feedback from airlines is that ADIS is very useful in FOQA and ASAP analysis.