Most of the data published on the web is unstructured or does not follow a standard. This makes it harder to retrieve and interchange information between different data sources. This work uses Linked Open Data (LOD) technologies and applies them in a scenario that deals with a large amount of computer network measurement data. The goal is to make the data more structured, hence easier to be retrieved, analyzed, and more interoperable. We discuss the challenges of processing large amount of data to: transform it into a standard format (RDF); link it to other data sources; and analyze and visualize the transformed data. Moreover, an ontology that aims to minimize the number of triples is proposed and a discussion of how ontologies may impact performance is presented. In addition, both the advantages of having the data in RDF format and the obstacles that the LOD community still faces are analyzed within the use cases on the scenario of the project. Show Partial Abstract