SDA is currently funded with the following regional, national and European research projects.
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. Read more about BigDataEurope.
The main objective of the BigDataOcean project is to enable maritime big data scenarios for EU-based companies, organizations and scientists, through a multi-segment platform that will combine data of different velocity, variety and volume under an inter-linked, trusted, multilingual engine to produce a big-data repository of value and veracity back to the participants and local communities. Read more about BigDataOcean.
The biggest European initiative in Big Data for Industry 4.0. With a 20M€ budget and leveraging 100M€ of private investment, Boost 4.0 will lead the construction of the European Industrial Data Space to improve the competitiveness of Industry 4.0 and will guide the European manufacturing industry in the introduction of Big Data in the factory, providing the industrial sector with the necessary tools to obtain the maximum benefit of Big Data. Read more about Boost4.0.
The CLEOPATRA ITN, a Marie Skłodowska-Curie Innovative Training Network aims to make sense of the massive digital coverage generated by the intense disruption in Europe over the past decade – including appalling terrorist incidents and the dramatic movement of refugees and economic migrants. Read more about Cleopatra.
GEISER is a European project that develops a holistic open-source platform for benchmarking sensor data to Internet-based Geo-Services. Read more about GEISER.
HOBBIT is a European project that develops a holistic open-source platform and industry-grade benchmarks for benchmarking big linked data. Read more about HOBBIT.
QROWD is a European project that will deliver innovative solutions to improve transport and mobility in European cities combining the power of the Qrowd and RDF. Read more about QROWD.
A textual & graphical domain-specific language for semantic data analytics workflows. Read more about Simple-ML.
SLIPO is a European project that develops a Scalable Linking and Integration of Big POI data. Read more about SLIPO.
Open Source Projects
SDA is currently working on high-impact R&D OpenSource projects.
AskNow is a Question Answering (QA) system for RDF datasets. Read more about AskNow.
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 on the web. Read more about DeFacto
DL-Learner is a tool for learning concepts in Description Logics (DLs) from user-provided examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects. The goal of DL-Learner is to support knowledge engineers in constructing knowledge and learning about the data they created. Read more about DL-Learner
MEX Vocabulary: A Light-Weight Interchange Format for Machine Learning Experiments. Read more about MEX Vocabulary
An open-source platform for distributed batch data processing for RDF large-scale datasets. Read more about SANSA Stack
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-comparisons across languages as this requires data conversion processes. Read more about SML-Bench
Experimental Analysis of Class CS Problems
Experimental Analysis of Class CS Problems.
Smoothed Analysis ML
Smoothed Analysis of Structured Machine Learning Algorithms from Knowledge Graphs.
Tensor Factorisation and Visualization for Knowledge Graphs
Tensor Factorisation and Visualization for Knowledge Graphs.