Abstract
Analyses of products during manufacturing are essential to
guarantee their quality. In complex industrial settings, such analyses
require to use data coming from many different and highly heterogeneous
machines, and thus are affected by the data integration challenge. In this
work, we show how this challenge can be addressed by relying on semantic
data integration, following the Virtual Knowledge Graph approach. For
this purpose, we propose the SIB Framework, in which we semantically
integrate Bosch manufacturing data, and more specifically the data nec-
essary for the analysis of the Surface Mounting Process (SMT) pipeline.
In order to experiment with our framework, we have developed an ontol-
ogy for SMT manufacturing data, and a set of mappings that connect
the ontology to data coming from a Bosch plant. We have evaluated SIB
using a catalog of product quality analysis tasks that we have encoded
as SPARQL queries. The results we have obtained are promising, both
with respect to expressivity (i.e., the ability to capture through queries
relevant analysis tasks) and with respect to performance.