Big Geospatial Data Workshop

A co-located event of the European Data Forum 2015 (EDF2015)

November 18th, 2015, Luxemburg
Registration is open and free.
Check out the programme.

Workshop description

The semantic Web standards, tools, ontologies and related technologies have considerably grown in maturity in the recent years and the quantity of published Linked Data continues to increase. Numerous EU funded projects are developing methods and tools to address issues such as the transformation of data to RDF, large scale searching and querying, reasoning, and the applications to specific domains like health and agriculture. However, applications that exploit Linked Data are not yet widespread and the reuse and integration of tools needs to be promoted and advanced further.

This workshop is organised by a group of EU funded projects that will present their contributions to this area and showcase solutions along the whole lifecycle of linked data production, integration, publication and use. The workshop is organized for a second time and continues the tradition of a very successful one from last year.

The focus of the workshop this year is on Big Geospatial Data. Geospatial data is an important type of data that we find often in applications e.g., remote sensing, agriculture, transportation etc. The objective is to provide a focused venue for academic and industrial discussions on the readily available arsenal of tools, systems and best practices that support the entire lifecycle of Big Geospatial Linked Data and to demonstrate specific use cases in data intensive domains.

This workshop is open for all interested EDF2015 participants as well as for interested audience from Luxembourg. We will open discussions, interchange ideas and explore synergies for further collaboration.

Who should participate

  • Industry:

    • Solution providers

    • Data producers and consumers: come discuss your use case with us, see if there is value for you in these project’s outcomes

  • Academia:

    • Data management researchers

    • Data producers and consumers: data-intensive methods in various sciences