Details

Autor(en) / Beteiligte

• Baumann, Peter
• Hirschorn, Eric
• Maso, Joan
• Merticariu, Vlad
• Misev, Dimitar

Titel

All in One: Encoding spatio-temporal big data in XML, JSON, and RDF without information loss

Ist Teil von

• 2017 IEEE International Conference on Big Data (Big Data), 2017, p.1-10

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEEE Xplore

Beschreibungen/Notizen

• With the unprecedented availability of continuously observed and generated data there is a likewise unprecedented potential for new and timely insights; yet, benefits are not fully leveraged as of today. The plethora of formats in combination with heterogeneous services remains is an obstacle - e.g., image services prefer binary formats, SPARQL endpoints like to think in RDF triples, and browsers integrate JSON data smoothly. We propose a model-based multi-encoding approach for overcoming the limitations of individual formats while still supporting their use. Concretely, this approach is being followed by the OGC Coverage Implementation Schema (CIS) standard which establishes a concrete, interoperable data model unifying n-D spatiotemporal regular and irregular grids, point clouds, and meshes. We describe how independence from data formats is achieved, in particular for three practically relevant formats - XML, JSON, and RDF -, thereby fostering integration of hitherto rather separate application domains.