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Strabon
| Website | http://strabon.di.uoa.gr/ |
| Supported versions | latest |
| Current responsible(s) | George Papadakis @ University of Athens -- [email protected] |
| Docker image(s) | bde2020/docker-strabon |
| More info | http://link.springer.com/chapter/10.1007/978-3-642-35176-1_19 |
| http://strabon.di.uoa.gr/publications | |
Strabon is a semantic spatiotemporal RDF store. You can use it to store linked geospatial data that changes over time and pose queries using two popular extensions of SPARQL. Strabon supports spatial datatypes enabling the serialization of geometric objects in OGC standards WKT and GML. It also offers spatial and temporal selections, spatial and temporal joins, a rich set of spatial functions similar to those offered by geospatial relational database systems and support for multiple Coordinate Reference Systems. Strabon can be used to model temporal domains and concepts such as events, facts that change over time etc. through its support for valid time of triples, and a rich set of temporal functions. Strabon is built by extending the well-known RDF store Sesame and extends Sesame’s components to manage thematic, spatial and temporal data that is stored in the backend RDBMS. Currently, Strabon is the only fully implemented RDF store with rich spatial and temporal functionalities, as its main query language, stSPARQL, allows for querying time-evolving geospatial data. Strabon also supports the querying of static geospatial data expressed in RDF using a subset of the recent OGC standard, GeoSPARQ.
First, you have to install PostGis:
docker pull bde2020/postgis
Then, install Strabon:
docker pull bde2020/strabon
Set PostGis running and listening to the port 5432:
docker run -p 5432:5432 --name postgis bde2020/postgis
Then, run Strabon and link it to PostGis:
docker run -p 9999:8080 --link postgis:postgis bde2020/strabon
Finally, open your browser to:
localhost:9999/strabon
To ingest data into Strabon using the UI, the default credentials are "endpoint"/"3ndpo1nt".
Strabon is currently one of the most efficient RDF stores for time-evolving geospatial data. In the near future, its scalability will be further improved by adapting its query language to the MapReduce framework.