The explorative and iterative nature of developing and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Recent improvements in both the performance and scalability of shared-nothing,
transactional, in-memory NewSQL databases have reopened the research question of …

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its
sources are inadequately interlinked with topological relations in the Linked Open Data …

Body area networks (BANs), cloud computing, and machine learning are platforms that can
potentially enable advanced healthcare outside the hospital. By applying distributed sensors …

As machine learning systems are being used in an increasing number of applications-from
analysis of satellite sensory data and health-care analytics to smart virtual assistants and …

We present Strabo 2, a distributed geospatial RDF store able to process GeoSPARQL
queries over massive RDF datasets. Strabo 2 is based on robust technologies, able to scale …

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its
sources are insufficiently interlinked with topological relations in the Linked Open Data …

Versatility of NewSQL databases is to achieve low latency constrains as well as to reduce
cost commodity nodes. Out work emphasize on how big data is addressed through top …

Machine learning (ML) presents new challenges for reproducible software engineering, as
the artifacts required for repeatably training models are not just versioned code, but also …

Copernicus is the European programme for monitoring the Earth. It consists of a set of
systems that collect data from satellites and in-situ sensors, process this data and provide …