This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2019 by Atanas Kiryakov, the Founder and CEO of Ontotext a Semantic Web pioneer, and leading developer of semantic technology and knowledge graphs.

Abstract:

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable.

In the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets the match might not be valid in every context.

This Video Lecture introduces  Apache Spark (Architecture, Libraries), the underlining data structures (Resilient Distributed Dataset) and an Example with Scala.

This Video Lecture briefly introduces the use of Apache Spark and Hadoop for Data Science applications.