A large amount of data are generated every moment by connected objects creating Internet
of Things (IoT). These data are difficult to handle using traditional databases leading to use …

This paper proposes an approach to tackle the problem of querying large volume of
statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the …

Scientific experiments involve complex interactions between geographically distributed
researchers, who act as units and require a substantial volume of data and services. This …

The evolution of heterogeneous data residing in various kinds of data sources (eg,
relational, XML, document stores, etc.) raises the need to provide a unified view over them …

[Motivation] The science has evolved giving space to the open and data-intensive science.
In this context, information sharing, and data integration are essential issues.[Problem] Data …

RDF-based data integration is often hampered by the lack of methods to translate data
locked in heterogeneous silos into RDF representations. In this paper, we tackle the …

Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer
processes and improve effectiveness and efficiency. It is taking over many industries whose …

The problem of knowledge heterogeneity in the Semantic Web or in the context of
information systems remains a major challenge for the scientific community, in particular …

Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various
semantic and other applications requires Data Management Systems (DMSs) to efficiently …

Purpose This study aims to provide a system capable of static searching on a large number
of unstructured texts directly on the Web domain while kee** costs to a minimum. The …