Examples of Data Transformation


Earlier versions of this training module have been developed within the context of the NatureSDIplus project, 2010, ( and of the smeSpire project, 2013 (
Authors: Giacomo Martirano, Fabio Vinci, Stefania Morrone (EPSILON ITALIA). The material is provided under Creative Commons Attribution Share-Alike License (

This self-learning module provides examples of transformations of a source dataset into a dataset compliant to the technical requirements of the applicable Implementing Rules and Technical Guidelines of INSPIRE.

It shows, step by step, an schema transformation process, starting from the analysis of the source dataset and of its data model and the study of the applicable INSPIRE Data Specification.

The module shows the use of the matching table as useful tool to document the mapping process between the elements of the source dataset and the INSPIRE data model elements and explains how to identify and solve some common matching problems.

Through the use of a selected tool, the transformation process is practically explained, showing also the “live” validation of the mapping being performed against the relevant INSPIRE application schema. At the end, a demonstration is given of how to generate a harmonized GML dataset.

  1. Analysis of the source data model
  2. Identification and analysis of the target data model
  3. Use of the matching table
  4. Analysis and solution of the matching problems
  5. Execution of the transformation
  6. Export of the transformed data
Learning outcomes

After the module, the participant will be able to identify and understand the source and target data models, to fill in a matching table, to perform a data transformation from a non-harmonized source dataset into an harmonized one and to export a harmonized GML dataset.

Intended Audience
GIS and ICT professionals aiming to harmonize their datasets against INSPIRE Data Specifications.

Basic knowledge of INSPIRE.

LINKVIT module:Procedures for Data and Metadata Harmonization”.

PDF documents, presentations, Weblecture. The module is a self-learning module.
Expected workload
2 hours