GEISER develops an open cloud-based platform for integrating geospatial data with sensor data from cyberphysical systems based on semantic and Big Data technologies.
DL-Learner is a framework for supervised Machine Learning in OWL, RDF and Description Logics. The framework contains several algorithms for supervised machine learning using structured data as background knowledge, can use various RDF and OWL formats as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends the ideas of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow to analyse data in knowledge bases or help in constructing knowledge bases from data.
LITMUS Benchmark Suite, is an open extensible framework for benchmarking a plethora of cross-domain DMSs. Apart from automating the tedious process of benchmarking, it also offers: (i) An efficient way for replicating existing benchmarks (e.g., BSBM, WAT-DIV). (ii) A wide set of performance evaluation measures/indicators tailored specifically for needs and, (iii) Present a custom visualization via custom charts, graphs and tabular data of the benchmark results for a faster insight.
SLIPO aim is to transfer the research output generated by our work in project GeoKnow, to the specific challenge of POI data, introducing validated and cost-effective innovations across their value chain.
SANSA-Stack’s core is a processing data flow engine that provides data distribution, and fault tolerance for distributed computations over RDF large-scale datasets.
HOBBIT is a European project that develops a holistic open-source platform and industry-grade benchmarks for benchmarking big linked data.
DeFacto (Deep Fact Validation) is an algorithm for validating statements by finding confirming sources for it on the web. It takes a statement (such as “Jamaica Inn was directed by Alfred Hitchcock”) as input and then tries to find evidence for the truth of that statement by searching for information in the web.
The ultimate goal of SML-Bench is to foster research in machine learning from structured data as well as increase the reproducibility and comparability of algorithms in that area. This is important, since a) the preparation of machine learning tasks in that area involves a significant amount of work and b) there are hardly any cross comparisions across languages as this requires data conversion processes.
Smart infrastructures and citizens’ participation in the digital society are increasingly data-driven. Sharing, connecting, managing, analysing and understanding data on the web will enable better services for citizens, communities and industry. However, turning web data into successful services for the public and private sector requires skilled web and data scientists as well as further research in the field. WDAqua aims to advance the field of data-driven question answering through a combination of training, research and innovation. Question answering is relevant to a diverse range of end users, and we will demonstrate this in settings including e-commerce, public sector information, publishing and smart cities.
Big Data Europe will undertake the foundational work for enabling European companies to build innovative multilingual products and services based on semantically interoperable, large-scale, multi-lingual data assets and knowledge, available under a variety of licenses and business models
QROWD offers local government and transportation businesses innovative solutions to improve mobility, reduce traffic congestion and make navigation safer and more efficient. QROWD will integrate different sources of data – maximizing the value of Big Data in planning and managing urban traffic and mobility.