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

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. 

Big Data technologies are often used in domains where data is generated, stored and processes with rates that cannot be efficiently processed by one computer. One of those domains is definitely the domain of energy. Here, the processes of energy generation, transmission, distribution and use have to be concurrently monitored and analyzed in order to assure system stability without brownouts or blackouts. The transmission systems (grids) that transport electric energy are in general very large and robust infrastructures that are accompanied with an abundance of monitoring equipment.

This module will discuss the topic of extraction for Knowledge Graphs. We will focus on web data extraction in this module. Web data extraction is essential to make information available on the web accessible and usable by Knowledge Graphs. We provide a thorough introduction to the topic. This will feature both Oxford’s Vadalog and OXPath systems.

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Knowledge Graphs (KGs) are one of the key trends among the next wave of technologies. Many defnitions exist of what a Knowledge Graph is, and in this chapter, we are going to take the position that precisely in the multitude of definitions lies one of the strengths of the area. We will choose a particular perspective, which we will call the layered perspective and three views on Knowledge Graphs:

• KGs as Knowledge Representation Tools
• KGs as Knowledge Management Systems
• KGs as Knowledge Application Services