Semantic Support for Complex Ecosystem Research Environments

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Presented at the AGU Fall meeting 2015


As ecosystems come under increasing stresses from diverse sources, there is growing interest in research efforts aimed at monitoring, modeling, and improving understanding of ecosystems and protection options. We aimed to provide a semantic infrastructure capable of representing data initially related to one large aquatic ecosystem research effort – the Jefferson project at Lake George. This effort includes significant historical observational data, extensive sensor-based monitoring data, experimental data, as well as model and simulation data covering topics including lake circulation, watershed runoff, lake biome food webs, etc. The initial measurement representation has been centered on monitoring data and related provenance. We developed a human-aware sensor network ontology (HASNetO) that leverages existing ontologies (PROV-O, OBOE, VSTO*) in support of measurement annotations. We explicitly support the human-aware aspects of human sensor deployment and collection activity to help capture key provenance that often is lacking. Our foundational ontology has since been generalized into a family of ontologies and used to create our human-aware data collection infrastructure that now supports the integration of measurement data along with simulation data. Interestingly, we have also utilized the same infrastructure to work with partners who have some more specific needs for specifying the environmental conditions where measurements occur, for example, knowing that an air temperature is not an external air temperature, but of the air temperature when windows are shut and curtains are open. We have also leveraged the same infrastructure to work with partners more interested in modeling smart cities with data feeds more related to people, mobility, environment, and living.

We will introduce our human-aware data collection infrastructure, and demonstrate how it uses HASNetO and its supporting SOLR-based search platform to support data integration and semantic browsing. Further we will present learnings from its use in three relatively diverse large ecosystem research efforts and highlight some benefits and challenges related to our semantically-enhanced foundation.


DateCreated ByLink
December 16, 2015
Patrick WestDownload

Related Projects:

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.

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
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: , eScience