Water and Species: A Scientist's Field Guide to Combining Datasets

Printer-friendly version

Presented at the AGU Fall Meeting 2012

Authors:Patrice Seyed

Watch on: You Tube

Related Projects:

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.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts: eScience
Semantic eScience
Lead Professor: Peter Fox
Description:
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience