Challenges for Transforming Big data into Knowledge: Precision Medicine- A Use Case

This is akeynote lecture from prof. Maria-Esther Vidal Scientific Data Management Group TIB

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This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2019 by Prof. Dr. Sören Auer.

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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:

Big data is a reality and it is being generated and handled in almost all digitised scenarios. This chapter covers the history of Big data and discusses prominent related terminologies. The significant technologies including architectures and tools are reviewed. Finally, the lecture reviews big knowledge graphs, that attempt to address the challenges (e.g. heterogeneity, interoperability, variety) of big data through their specialised representation format. This chapter aims to provide an overview of the existing terms and technologies related to big data.

The size of knowledge graphs has reached the scale where centralised analytical approaches have become infeasible. Recent technological progress has enabled powerful distributed in-memory analytics that have been shown to work well on simple data structures. However, the application of such distributed analytics approaches on semantic knowledge graphs lags significantly behind. To advance both scalability and accuracy of large-scale knowledge graph analytics to a new level, foundational research on methods leveraging distributed in-memory computing and semantic technologies in combination w

Big data plays a relevant role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Semantic web technologies have also experienced great progress, and scientific communities and practitioners have contributed to the problem of big data management with ontological models, controlled vocabularies, linked datasets, data models, query languages, as well as tools for transforming big data into knowledge from which decisions can be made.

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