Deborah L. McGuinness Project Participation

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Deborah L. McGuinness

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Project PI

CHEAR Project LogoChild Health Exposure Analysis Repository (CHEAR)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Kristin Bennett
Description: Child Health Exposure Analysis Repository Data Science Semantics
DQSS Project LogoCognitive Assistant that Learns and Organizes (CALO)
Principal Investigator: Deborah L. McGuinness
Description: The goal of the project CALO, for Cognitive Assistant that Learns and Organizes, is to create cognitive software systems, that is, systems that can reason, learn from experience, be told what to do, explain what they are doing, reflect on their experience, and respond robustly to surprise. Rensselaer is leading the explanation efforts.
DataONE Semantics LogoDataONE Semantics (D1-Semantics)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Xixi Luo and Mark Schildhauer
Description: Semantic search on measurements will enable precise data discovery by helping users identify relevant content from the massive and heterogeneous catalog in DataONE, thereby improving efficiency and opportunities for researchers and other data consumers.
Jefferson Project at Lake George Project LogoE-Science Jefferson Project on Lake George (Jefferson Project)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Paulo Pinheiro
Description: The Jefferson Project at Lake George is building one of the world’s most sophisticated environmental monitoring and prediction systems, which will provide scientists and the community with a real-time picture of the health of the lake. Launched in June 2013, the project aims to understand and manage multiple complex factors—including road salt incursion, storm water runoff, and invasive species—all threatening one of the world’s most pristine natural ecosystems and an economic cornerstone of the New York tourism industry. The project is a three-year, multimillion-dollar collaboration between Rensselaer Polytechnic Institute, IBM, and The FUND for Lake George. The collaboration partners expect that the world-class scientific and technology facility at the Rensselaer Darrin Fresh Water Institute at Lake George will create a new model for predictive preservation and remediation of critical natural systems in Lake George, in New York, and ultimately around the world.
First Responders logoFirst Responders Requirements Metholodology (FirstResponders)
Principal Investigator: Deborah L. McGuinness
Co Investigator: John S. Erickson
Description: The purpose of this project is to design and prototype a requirements-gathering methodology driven by the first responders community. The methodology will include examining the current state of collecting and synthesizing responder requirements, assessing the effectiveness of that process, evaluating existing candidate platforms for use within this community, and producing a roadmap that can be used by NIST and others to achieve a solution enabling the responder community to more effectively dialogue with key stakeholders. A prototype implementation of the methodology will be developed using the roadmap and will be available for testing and evaluation and requirements gathering.
FUSE LogoForesight and Understanding from Scientific Exposition (FUSE)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Jim Hendler
Description: Technical emergence refers to the process whereby innovative ideas, capabilities, applications, and even entirely new fields of study arise, are tested, mature, and, if conditions are favorable, demonstrate feasibility and impact. IARPA’s Foresight and Understanding from Scientific Exposition (FUSE) Program is sponsoring advanced research and development (R&D) to develop automated systems that aid in the systematic, continuous, and comprehensive assessment of technical emergence using information derived from the published scientific, technical, and patent literature.
Generalized Integrated Learning Architecture (GILA)
Principal Investigator: Jim Hendler and Deborah L. McGuinness
Description: The Generalized Integrated Learning Architecture [GILA] is a general-purpose integrated multi-agent platform that solves domain problems by learning from a problem-solution pair submitted by a human expert. One of the key purposes of GILA is to learn how humans solve complex problems and apply this knowledge to future problems. A complex problem domain known as the Airspace Control Scenario has been chosen to drive the development of GILA and evaluate its performance. The objective of this problem domain is to resolve conflicts in a collection of airspace allocations for aircrafts.
Health Data Challenge (HealthData)
Principal Investigator: Deborah L. McGuinness and Jim Hendler
Co Investigator: Alvaro Graves, Tim Lebo, and James McCusker
Description: An infrastructure for large-scale collaboration around aggregation, generation, and publication of health-related Linked Data.
Health on the Web
Principal Investigator: Deborah L. McGuinness and Joanne S. Luciano
Description: The Tetherless World Constellation's Health on the Web's primary goal is to explore the next generation web technology needed to improve health.
Inference Web Project LogoInference Web
Principal Investigator: Deborah L. McGuinness
Description: The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust. Provenance - if users (humans and agents) are to use and integrate data from unknown, uncertain, or multiple sources, they need provenance metadata for evaluation Interoperability - more systems are using varied sources and multiple information manipulation engines, thus increasing interoperability requirements Explanation/Justification - if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available Trust - if some sources are more trustworthy than others, trust ratings are desired The Inference Web consists of two important components: Proof Markup Language (PML) Ontology - Semantic Web based representation for exchanging explanations including provenance information - annotating the sources of knowledge justification information - annotating the steps for deriving the conclusions or executing workflows trust information - annotating trustworthiness assertions about knowledge and sources IW Toolkit - Web-based and standalone tools that facilitate human users to browse, debug, explain, and abstract the knowledge encoded in PML.
DCO-DS LogoLinking Open Government Data (LOGD)
Principal Investigator: Jim Hendler and Deborah L. McGuinness
Description: The LOGD project investigates the role of Semantic Web technologies, especially Linked Data, in producing, enhancing and utilizing government data published on and other websites.
Mobile Health Project LogoMobile Health
Principal Investigator: Deborah L. McGuinness
Description: The Mobile Health project aims to bring semantic representations of medical data collected from a variety of consumer and medical grade devices and integrate those data on an individual's mobile smartphone. Combined with the reasoning capabilities of semantic web and technologies such as IBM Watson, this project plans to enable personalized health care through the instrumented self.
Nanomine LogoOntology-Enabled Polymer Nanocomposite Open Community Data Resource (Nanomine)
Principal Investigator: Linda Schadler, Deborah L. McGuinness, Cate Brinson, and Wei Chen
Description: Our evolving semantics=driven data resource, named NanoMine, is an open access, user friendly, living, growing, data resource for the polymer nanocomposites community that is scalable and enables improved understanding of processing – structure - property relationships and thus facilitates faster nanocomposite design and insertion into advanced applications. By bringing together the data that is scattered throughout the public literature and private files and creating a protocol for recording and tagging data, this resource is an unprecedented compilation of information that is accessible. Tools within the resource allow users to visualize complex data, analyze images from their work, and design new polymer nanocomposites materials. For NanoMine to realize broad community acceptance and address scientific questions at the forefront of technology, it marries cutting edge cyber infrastructure with a robust set of data and tools.
PopSciGrid LogoPopulation Science Grid (PopSciGrid)
Principal Investigator: Deborah L. McGuinness
Description: The National Cancer Institute’s (NCI) PopSciGrid Community Health Portal is an evolving platform demonstrating how health behavior, policy, and demographic data can be integrated, visualized, and communicated to empower communities and support new avenues of research and policy for cancer prevention and control. As a proof of concept for cyber-enabled population health research, the PopSciGrid Portal is designed to encourage trans-disciplinary collaboration, data harmonization, and development of new computational methods for disparate health related data.
Repurposing Drugs with Semantics (ReDrugS)
Principal Investigator: Deborah L. McGuinness and Jonathan Dordick
Description: We aim to find new effective treatments for disease using existing drugs. Our approach is to gather and integrate existing data using semantic technologies to help discover promising drug repurposing.
SemantEco Annotator Project LogoSemantEco Annotator
Principal Investigator: Deborah L. McGuinness
Co Investigator: Patrice Seyed
Description: Generating useful RDF linked data is not a straightforward process for scientists using today's tools. In this project we introduce the SemantEco Annotator, a semantic web application that leverages community-based vocabularies and ontologies during the translation process itself to ease the process of drawing out implicit relationships in tabular data so that they may be immediately available for use within the LOD cloud. Our goal for the SemantEco Annotator is to make advanced RDF translation techniques available to the layperson.
SemNExT LogoSemantic Numeric Exploration Technology (SemNExT)
Principal Investigator: Deborah L. McGuinness and Kristin Bennett
Description: SemNExT combines numeric analysis of data with semantic understanding and explanation technologies to provide a holistic means of exploring robust datasets.
SVF LogoSemantic Vernaculars for Fungi (SVF)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Nathan Wilson
Description: Fungi are typically referred to by either scientific or common names. Neither of these terminologies meets the need for well-defined, persistent definitions of groups of fungi who exhibit similar macroscopic qualities, but may be dissimilar genetically. We propose a community-developed vocabulary that can be used to identify mushrooms based on properties that can be observed in the field (without microscopic or genomic examination). We show how an ontology can be used to develop and organize the terms and definitions and to enable applications based on the vocabulary.
SemantAQUA LogoSemantic Water Quality Portal (SemantAQUA)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Joanne S. Luciano
Description: We present a semantic technology-based approach to emerging environmental information systems. We used our linked data approach in the Tetherless World Constellation Semantic Water Quality Portal (TWC-SWQP). Our integration scheme uses a core domain ontology and integrates water data from different authoritative sources along with multiple regulation ontologies to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach also captures and leverages provenance to improve transparency. In addition, semantic water quality portal features provenance-based facet generation, query answering and data validation over the integrated data via SPARQL. We introduce the approach and the water portal, and highlight some of its potential impacts for the future of environmental monitoring systems.
TW LogoStreaming Data Characterization (SDC)
Principal Investigator: Deborah L. McGuinness and Mark Greaves
Description: This project aims to develop a flexible window management strategies and algorithms for stream reasoning. We have proposed a stack of technologies including sequential stream reasoning architecture, the notion of semantic importance. Project Poster link: Project Slides link:
TW LogoStreaming Hypothesis Reasoning (Shyre)
Principal Investigator: William Smith and Deborah L. McGuinness
Description: AIM will advance streaming reasoning techniques to overcome a limitation in contemporary inference that performs analysis only over data in a fixed cache or a moving window. This research will lead to methods that continuously shed light on proposed hypotheses as new knowledge arrives from streams of propositions, with a particular emphasis on the effect that removing the expectation of completeness has on the soundness and performance of symbolic deduction platforms.
TW LogoTWC Web Observatory (WebObservatory)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Jim Hendler
Description: The Web Science Research Center at TWC RPI is working with other members of the Web Science Trust to create a global "Web Observatory". The global movement toward Open Data and transparency have successfully motivated the release of very large institutional and commercial data sets describing social phenomena, economic indicators and geographic trends. This proliferation of data represents great opportunity for researchers and industry but this data abundance also threatens to make it ever more difficult to locate, analyse, compare and interpret useful information in a consistent and reliable way; a situation which can only get worse unless we can help stakeholders perform useful analysis rather than drowning in a sea of data. A global Web Observatory will offer an institutional framework to promote the use of W3C and other standards in the development of Semantic Catalogues to globally locate existing data sets, Collection Systems to gather new global data sets, and Analytics Tools and methodologies to analyse these data sets.
TAMI LogoTransparent and Accountable Datamining Initiative (TAMI)
Principal Investigator: Jim Hendler and Deborah L. McGuinness
Description: The TAMI Project is creating technical, legal, and policy foundations for transparency and accountability in large-scale aggregation and inferencing across heterogeneous information systems.
Web Science Research Center (WSRC)
Principal Investigator: Deborah L. McGuinness
Co Investigator: John S. Erickson
Description: Web Science is the study of the World Wide Web and its impact on both society and technology, positioning the Web as an object of scientific study unto itself. Web Science recognizes the Web as a transformational, disruptive technology; its practitioners study the Web, its components, facets and characteristics. Ultimately, Web Science is about understanding the Web and anticipating how it might evolve in the future.
Wineagent LogoWine Agent
Principal Investigator: Deborah L. McGuinness
Description: The Wine Agent represents knowledge of wines and foods and is a demonstration platform for a large variety of Semantic Web technologies in a rich domain and is derived from previous work in the field of reasoning systems.

Project CO-PI

COSMIC LogoCloud-Oriented Social Media Inference And Counteraction (COSMIC)
Principal Investigator: V. Subrahmanian and Jim Hendler
Co Investigator: Deborah L. McGuinness
Description: COSMIC will combine novel algorithms from sentiment analysis, probabilistic temporal learning of diffusion of messages/opinion in social networks, forecasting and prediction of the reach of messages in social media, and game-theoretic methods to counteract diffusion of messages, into a highly modular, loosely-coupled framework and software platform to address the objectives of DARPA’s Social Media in Strategic Communication (SMISC) Program.
SKIF Project LogoScientific Knowledge Integration Framework (SKIF)
Principal Investigator: Peter Fox
Co Investigator: Deborah L. McGuinness
Description: The overall goal of this effort is to bring together key aspects of intelligent systems, namely data mining / knowledge extraction and semantic knowledge representation, and to prove the benefit of this approach by applying it to a science problem that is representative of NASA Science Mission Directorate research interests.
SPCDIS Project LogoSemantic Provenance Capture in Data Ingest Systems (SPCDIS)
Principal Investigator: Peter Fox
Co Investigator: Deborah L. McGuinness
Description: The goal of this project is to develop at the RPI Tetherless World Constellation, based within the NCAR High Altitude Observatory and in collaboration with the University of Texas at El Paso, the University of Michigan and McGuinness Associates a semantically-enabled data ingest capability.
SSIII LogoSemantic Sea Ice Interoperability Initiative (SSIII)
Principal Investigator: Siri Jodha Singh Khalsa, Mark Parsons, and Ruth Duerr
Co Investigator: Peter Fox and Deborah L. McGuinness
Description: SSIII is a National Science Foundation (NSF) funded effort to enhance the interoperability of sea ice data to establish a network of practitioners working to enhance semantic interoperability of all Arctic data. SSIII is a collaborative project between NSIDC and the Rensselaer Polytechnic Institute (RPI) Tetherless World Constellation project. We seek to build on the work initiated under the International Polar Year (IPY) and create a community of practice working to improve interoperability within the Polar Information Commons (PIC), the Sustained Arctic Observing Network (SAON), and broader global systems.
SeSF Project LogoSemantic eScience Framework (SeSF)
Principal Investigator: Peter Fox
Co Investigator: Jim Hendler and Deborah L. McGuinness
Description: Over the past few years, semantic technologies have evolved and new tools are appearing. Part of the effort in this project will be to accommodate these advances in the new framework and lay out a sustainable software path for the (certain) technical advances. In addition to a generalization of the current data science interface, we will include an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.
SESDI Project LogoSemantically-Enabled Science Data Integration (SESDI)
Principal Investigator: Peter Fox
Co Investigator: Deborah L. McGuinness
Description: The vast majority of explorations of the Earth system are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. In many cases, syntax-only interoperability IS the state-of-the-art. In order for scientists and non-scientists to discover, access, and use data from unfamiliar sources, they are forced to learn details of the data schema, other people¿s naming schemes and syntax decisions. Our work is aimed at providing scientists with the option of describing what they are looking for in terms that are meaningful and natural to them, instead of in a syntax that is not. The missing element in enabling the higher-level interconnections is the technology of ontologies, ontology-equipped tools, and semantically aware interfaces between science components. Ontologies fill a major technology gap in machine-to-machine communication across multiple disciplines to advance Earth system science by enabling data integration without the need for human intervention. This project, the Semantically-Enabled Science Data Integration (SESDI), will demonstrate how ontologies implemented within existing distributed technology frameworks will provide essential, re-useable, and robust, support for an evolution to science measurement processing systems (or frameworks) as well as for data and information systems (or framework) support for NASA Science Focus Areas and Applications.
SDC LogoStreaming Data Characterization (SDC)
Co Investigator: Deborah L. McGuinness
Description: This project aims to leverage the novel notion of semantic importance to characterize the importance among the boundless streaming data, so as to provide better query results in terms of accuracy or recall, as well as improve the system response time.
HADATAC LogoThe Human-Aware Data Acquisition Framework (HADatAc)
Principal Investigator: Paulo Pinheiro
Co Investigator: Deborah L. McGuinness
DCO-DS LogoVirtual Solar Terrestrial Observatory (VSTO)
Principal Investigator: Peter Fox
Co Investigator: Deborah L. McGuinness
Description: VSTO is a collaborative project between the High Altitude Observatory and Scientific Computing Division of the National Center for Atmospheric Research and McGuinness Associates. VSTO is funded by a grant from the National Science Foundation, Computer and Information Science and Engineering (CISE) in the Shared Cyberinfrastructure (SCI) division.

Project Collaborator

Description: Tasks for various TWC projects related to data access and the OPeNDAP software products.