This presentation shows the challenges a large engineering contractor has with the diversity of the data handled in constructing process plants. Fluor discusses how they have joined a cooperation of 80 companies to design a data model, and to help author an ISO standard based on semantic web technologies. It clearly shows the advantages of semantic web over relational databases, the innovation involved, and the lessons learned. Possibilities are listed that offers opportunity for open source and commercial software companies.
FLUOR
http://www.fluor.com/
Onno Paap is an experienced engineer and software developer, who was involved in innovative engineering software projects for his entire career. Within the Fluor company he is the Fellow in Semantic Technologies and Data Interoperability. He has led software teams for in-house development of all engineering disciplines.
The Semantic Web provides a graph-based organisation of knowledge that has become popular in enterprises under the term enterprise knowledge graphs. It allows a lot of flexibility for modeling as well as combining data sets by linking graphs together, which has the potential to solve enterprise data heterogeneity problems in a bottom-up and flexible manner. When processing and linking together graph data in enterprises on a large scale, ETL (Extract-Transform-Load) processes supporting Semantic Web standards are used for automation.
CTO
Semantic Web Company
http://www.semantic-web.at/
Robert David (m, born 1976) studied Software Engineering & Internet Computing and Informatikmanagement at the Technical University of Vienna. He wrote his master thesis on the reengineering of web applications using architectural approaches, in which he developed methods to transform legacy systems into modern web applications.
With the significant growth in RDF datasets, application developers demand their online availability to meet the end users' expectations. Various interfaces are available for querying RDF data using SPARQL query language. Studies show that SPARQL endpoints may provide high query runtime performance at the cost of low availability. For example, it has been observed that only 32.2% of public endpoints have a monthly uptime of 99-100%. One possible reason for this low availability is the high workload experienced by these SPARQL endpoints.
