Finding Haystacks with Needles: Ranked Search for Data Using Geospatial and Temporal Characteristics

The past decade has seen an explosion in the number and types of environmental sensors deployed, many of which provide a continuous stream of observations. Each individual observation consists of one or more sensor measurements, a geographic location, and a time. With billions of historical observations stored in diverse databases and in thousands of datasets, scientists have difficulty finding relevant observations. We present an approach that creates consistent geospatial-temporal metadata from large repositories of diverse data by blending curated and automated extracts. We describe a novel query method over this metadata that returns ranked search results to a query with geospatial and temporal search criteria. Lastly, we present a prototype that demonstrates the utility of these ideas in the context of an ocean and coastal-margin observatory.