Linked data is a new research area which studies how one can make RDF data available on the Web, and interconnect it with other data with the aim of increasing its value for users. The resulting “Web of data” has recently started being populated with geospatial data. Given the current amount and rate of increase of linked geospatial data, we expect that we will soon have petabytes (PBs) of such data available on the Web. However, there are currently no scalable techniques and systems that can be used to deal with this particular kind of “big data”.

The main objective of SCARE is to develop the theoretical principles and software systems that will close this gap in research and technological development. The detailed technical objectives of SCARE are the following:

1. To develop the formal foundations of reasoning and query processing for linked geospatial data. In particular, to develop a formal semantics for the query language GeoSPARQL recently proposed by the Open Geospatial Consortium as a standard for querying geospatial data expressed in RDF. Also, to study formally the computational complexity of reasoning and query processing for the query language GeoSPARQL using established techniques from knowledge representation and database theory.
2. To develop techniques that exploit recent advances in computer hardware (multicore processors, clusters and clouds) and enable us to deal efficiently with the scale of linked geospatial data that we envision. In particular, to develop parallel algorithms for GeoSPARQL reasoning and query processing in clusters and clouds. Given the details of GeoSPARQL, a major goal of SCARE in this area is the development of parallel constraint-based qualitative spatial reasoning techniques that will scale to big data.
3. To integrate the developed algorithms in a prototype system that we will make available to the research community as open source software.