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Big Data [clear filter]
Thursday, January 9
 

12:00pm EST

Datacubes for Analysis-Ready Data: Standards & State of the Art
This workshop session will follow up on the OGC Coverage Analytics sprint, focusing specifically on advanced services for spatio-temporal datacubes. In the Earth sciences datacubes are accepted as an enabling paradigm for offering massive spatio-temporal Earth data analysis-ready, more generally: easing access, extraction, analysis, and fusion. Also, datacubes homogenizes APIs across dimensions, allowing unified wrangling of 1-D sensor data, 2-D imagery, 3-D x/y/t image timeseries and x/y/z geophysics voxel data, and 4-D x/y/z/t climate and weather data.
Based on the OGC datacube reference implementation we introduce datacube concepts, state of standardization, and real-life 2D, 3D, and 4D examples utilizing services from three continents. Ample time will be available for discussion, and Internet-connected participants will be able to replay and modify many of the examples shown. Further, key datacube activities worldwide, within and beyond Earth sciences, will be related to.
Session outcomes could take a number of forms: ideas and issues for OGC, ISO, or ESIP to consider; example use cases; challenges not yet addressed sufficiently, and entirely novel use cases; work and collaboration plans for future ESIP work. Outcomes of the session will be reported at the next OGC TC meeting's Big Data and Coverage sessions. How to Prepare for this Session: Introductory and advanced material is available from http://myogc.org/go/coveragesDWG

Presentations
https://doi.org/10.6084/m9.figshare.11562552.v1

View Recording: https://youtu.be/82WG7soc5bk

Takeaways
  • Abstract coverage construct defines the base which can be filled up with a coverage implementation schema. Important as previously implementation wasn’t interoperable with different servers and clients. 
  • Have embedded the coordinate system retrieved from sensors reporting in real time into their xml schema to be able to integrate the sensor data into the broader system. Can deliver the data in addition to GML but JSON, and RDF which could be used to link into semantic web tech. 
  • Principle is send HTTP url-encoded query to server and get some results that are extracted from datacube, e.g., sources from many hyperspectral images.

Speakers

Thursday January 9, 2020 12:00pm - 1:30pm EST
White Flint