Keywords
Model integration, semantic workflows, ontologies, reasoning, automated planning, machine learning, model metadata, data catalogs, model catalogs
Start Date
26-6-2018 10:40 AM
End Date
26-6-2018 12:00 PM
Abstract
Major societal and environmental challenges require forecasting how natural processes and human activities affect one another. Model integration across natural and social science disciplines to study these problems requires resolving semantic, spatio-temporal, and execution mismatches, which are largely done by hand today and may take more than two years of human effort. We are developing the Model INTegration (MINT) framework that incorporates extensive knowledge about models and data, with several innovative components: 1) New principle-based ontology generation tools for modeling variables, used to describe models and data; 2) A novel workflow system that selects relevant models from a curated registry and uses abductive reasoning to hypothesize new models and data transformation steps; 3) A new data discovery and integration framework that finds and categorizes new sources of data, learns to extract information from both online sources and remote sensing data, and transforms the data into the format required by the models; 4) New knowledge-guided machine learning algorithms for model parameterization to improve accuracy and estimate uncertainty; 5) A novel framework for multi-modal scalable workflow execution. We are beginning to annotate models and datasets using standard ontologies, and to compose and execute workflows of models that span climate, hydrology, agriculture, and economics. We are building on many previously existing tools, including CSDMS, BMI, GSN, WINGS, Pegasus, Karma, and GOPHER. Rapid model integration would enable efficient and comprehensive coupled human and natural system modeling.
MINT: Model INTegration Through Knowledge-Powered Data and Process Composition
Major societal and environmental challenges require forecasting how natural processes and human activities affect one another. Model integration across natural and social science disciplines to study these problems requires resolving semantic, spatio-temporal, and execution mismatches, which are largely done by hand today and may take more than two years of human effort. We are developing the Model INTegration (MINT) framework that incorporates extensive knowledge about models and data, with several innovative components: 1) New principle-based ontology generation tools for modeling variables, used to describe models and data; 2) A novel workflow system that selects relevant models from a curated registry and uses abductive reasoning to hypothesize new models and data transformation steps; 3) A new data discovery and integration framework that finds and categorizes new sources of data, learns to extract information from both online sources and remote sensing data, and transforms the data into the format required by the models; 4) New knowledge-guided machine learning algorithms for model parameterization to improve accuracy and estimate uncertainty; 5) A novel framework for multi-modal scalable workflow execution. We are beginning to annotate models and datasets using standard ontologies, and to compose and execute workflows of models that span climate, hydrology, agriculture, and economics. We are building on many previously existing tools, including CSDMS, BMI, GSN, WINGS, Pegasus, Karma, and GOPHER. Rapid model integration would enable efficient and comprehensive coupled human and natural system modeling.
Stream and Session
A4: Model Integration Frameworks: A Discussion of Typologies, Standards, Languages, and Platforms
Organizers: Andre Dozier, Olaf David