Workflow Based Tools for Integrated Spatiotemporal Research

Keywords: spatiotemporal, workflow, KNIME
Abstract Type: Paper Abstract

Abstract

In the era of the 4th industrial revolution, geolocation became ubiquitous, place and time are embedded in data generated by the Internet of Things. Geospatial and temporal data are everywhere, from Global Navigation Satellite Systems (GNSS), earth observation satellites, smart phones, clothes, cars, homes, cities, and more. However, how to effectively use these data in research to solve global problems remains a challenge. Conventional spatial data services often have high development cost and slow implementation cycles, require professional skills to maintain, lack the flexibility for supporting customizable inquiries and changing research themes, and are difficult to share with researchers from different fields with different skill levels.

The Spatial Data Lab project (SDL) is aimed at solving the above problems by applying workflow based tools such as KNIME (an open-source system for workflow development) for the integration of heterogeneous data, replication of analytical procedures and simulation models, automation of visualization updates, web-based access to high performance computing, and making spatiotemporal research reproducible, replicable and expandable. The integrated solution includes data, tools, models, visualizable results, documentation and publications, packaged as case studies, allowing researchers of diverse backgrounds to find, learn, use, modify, improve, and re-contribute back to the Lab. This talk will present some recent development of the SDL project with KNIME as an exploratory effort towards an integrated solution for data services, analytical support, teaching & learning, and collaborative research applications, showcase the Geospatial Analytics Extension in KNIME, and discuss future directions of the project and collaboration opportunities.