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 PLATOON Data Analytics toolbox is formed of all the data analytics tools that are developed and validated for the different use cases of the project. These tools will allow the extraction of value from heterogeneous data sources. There will be two main groups of data analytics tools:

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.