The process of providing cross-border public services across EU Member States is complex, due to the heterogeneity of the actors, information and services of the different Member States. The complexity of exchanging data may lead to semantic interoperability conflicts. The Core Vocabularies can be used to reduce these semantic conflicts in two ways:
Linked Open Data is a pragmatic approach for realizing the Semantic Web vision of making the Web a global, distributed, semantics-based information system. Linked Data technology matured significantly in the past few years. Automatic linking, extraction, mapping, and visualization of RDF data became mainstream technology provided by mature open-source software components. A vast number of small and large Linked Data resources, including DBPedia, now amounts to over 50 billion triples in total.
The aim of this work is to present the DCAT Application Profile (DCAT-AP) standard which in turn is derived from the W3C DCAT standard. DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web and also represent a way to provide context or metadata for datasets.
Ova lekcija opisuje softversko okruženje za obradu velikih količina podataka Apache Hadoop, njegove komponente koji upotpunjavaju i proširuju mogućnosti Hadoop-a, kao i njegovo korištenje u praksi. Da bismo u potpunosti razumeli prednosti Hadoop-a, potrebno je sagledati najpre razliku između paralelnog i distribuiranog računanja, načine čuvanja (HDFS arhitekturu), upravljanje resursima i procesiranja podataka, princip horizontalne skalabilnosti, itd.
The European electricity system undergoes significant changes driven by the European Union (EU) common rules for the internal market for electricity, as well as by the climate action agenda. The European Green Deal is also an opportunity for modernizing the energy system in order to make it competitive and sustainable with regard to the environment.
Data in the energy domain grows at unprecedented rates and is usually generated by heterogeneous energy systems.
Despite the great potential that big data-driven technologies can bring to the energy sector, general adoption is still lagging. Several challenges related to controlled data exchange and data integration are still not wholly achieved. As a result, fragmented applications are developed against energy data silos, and data exchange is limited to few applications.
With the aim of improving ecological interest, the share of renewable energy sources (RES) in energy production has to be increased. Nonetheless, that growth adversely influences the grid’s instability, as a result of the dependency between the RES production and weather conditions.
This introductory lecture discusses the Big Data processing pipeline and the Big Data Landscape from the following perspectives
Big Data Analytics is a crucial component of the Big data paradigm and refers to the process of extracting useful knowledge from large datasets or streams of data. Due to enormity, high dimensionality, heterogeneous, and distributed nature of data, traditional techniques of data mining may be unsuitable to work with big data.
Specific intrusion detection systems (IDSs) are needed to secure modern supervisory control and data acquisition (SCADA) systems due to their architecture, stringent real-time requirements, network traffic features and specific application layer protocols. This lecture aims to contribute to assess the state-of-the-art, identify the open issues and provide an insight for future study areas. To achieve these objectives, we start from the factors that impact the design of dedicated intrusion detection systems in SCADA networks and focus on network-based IDS solutions.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 809965.
