Information on individual educational components (ECTS-Course descriptions) per semester

Advanced Topics in Big Data:

• Introduction to Data Mining Algorithms / Methods and the tool WEKA
• relational databases and SQL
• basic concepts of statistics.    

Semantic Web:

• Programming experience
• Mathematial logik

Security:

• Fundamentals of applied cryptology: symmetric vs. asymmetrical; hashing; signatures, certificates etc.
• basic skills in handling the Linux console.
• basic knowledge and skills of kerberos;
• basic skills in handling a windows System
• basic knowledge of computer networking
• basic knowledge of security protocols (e.g. TLS)

Part: Advanced Topics in Big Data

• Big data analytics (e.g. analysis, (b) observation problems, outlier detection)
• Spark ecosystem for data analysis tasks Introduction to the differences / similarities of Hadoop and Spark, as well as possibilities to integrate both platforms.
• First experiences in the application of Hadoop and Spark for data analysis tasks.
• Requirements for the documentation of big data projects and communication of the project results.  

Part: Semantics and Ontologies

• Restrictions of the Web
• Layers of semantic Web
• Semantic modeling and ontologies
• Knowledge bases
• Linked open data Representation of facts using the Resource Description Framework (RDF)
• Different notations in RDF and the representation of statements
• The model theory behind RDFS
• Problematic constructs in RDF
• Modeling semantics using RDF schema
• Logic calculi as the basis for semantic deduction
• Description-logic and decision-making
• The Web Ontology Language (OWL) and its features
• Semantic web engineering  

Part: Security

• Motivation Infrastructure security: basic "hygiene"
• authentication and authorization
• data protection
• auditing
• identity and access management
• Mandatory Access Control (MAC), Role Based Access Control
• Uncertain data sources: problems, solutions e.g. Functional encryption
• attribute-based encryption
• Cloud security
• Information security analytics: IDS, IPS, SIEM, aggregated logs
• Security of che Hadoop (Sentry, Kerberos, Knox, Ranger...)

Students are able to

• carry out different analysis tasks on structured / unstructured data and document the results.
• assess big data processing platforms with regard to security aspects and implement solutions for security problems.
• scale data horizontally (merge data from different data sources, domains, data models, and structures).
• describe analytical methods for different problems, assess the advantages / disadvantages and make a selection based on performance criteria and implement these using the appropriate tools (Spark, MLlib, MADLib, Weka).
• explain the concepts of semantic web and interpret statements with RDF, RDFS, OWL.
• create and expand knowledge databases.
• explain the importance of semantics in the context of big data projects, especially when combining data from different data sources, domains, data models, and structures.
• exchange data between different knowledge databases. explain the principles of knowledge databases and linked open data and explain the Open World Approach.
• interrogate knowledge databases and interact with such a knowledge databases using a framework.
• classify the principles of OWL ontologies, the different types of relationships and the implications of the first-order predicate logic used.
• use semantic web tools, e.g. apply Protégé.
• classify the importance of the security of the underlying infrastructure for data analysis and apply technologies for the improvement of big data systems.
• select and implement a method for authentication and authorization from multiple methods.
• explain the problems of unsafe data sources and select and propose solutions based on cryptology techniques, e.g. functional encryption.
• reflect the problems of using public clouds and propose solutions.
• use the potential of data analytics to improve the security of complex networked systems and to analyze heterogeneous data from distributed sensors (e.g. IDS, SIEM).

• Advanced topics in Big Data: integrated lecture
• Semantics and Ontologies: integrated lecture
• Security: lecture and labs

Continual assessment.

• Advanced Topics in Big Data: assessment of exercises
• Semantics and Ontologies: written exam, assessment of exercises and project results
• Security: written exam; mandatory attendance for exercises

Security:  mandatory attendance for exercises

• Allemang, Dean ; Hendler, James A (2011): Semantic web for the working ontologist effective modeling in RDFS and OWL, second edition. Waltham, Mass.: Morgan Kaufmann.   
• Antoniou, G ; Groth, Paul ; Van Harmelen, Frank (2012): A Semantic Web primer, third edition. Cambridge, Mass.: MIT Press.   
• Dietrich, David u. a. (Hrsg.) (2015): Data science & big data analytics: discovering, analyzing, visualizing and presenting data. Indianapolis, IN: Wiley.   
• DuCharme, Bob (2013): Learning SPARQL.   
• Fensel, Dieter (2003): Spinning the semantic Web: bringing the World Wide Web to its full potential. Cambridge, Mass.: MIT Press.   
• Hebeler, John (2009): Semantic Web programming Includes index. - Description based on print version record. Indianapolis, Ind.: Wiley Pub..   
• Hitzler, Pascal (2008): Semantic web: Grundlagen. Berlin: Springer Berlin.   
• Hitzler, Pascal ; Krötzsch, Markus ; Rudolph, Sebastian (2010): Foundations of Semantic Web technologies. Boca Raton: CRC Press.   
• Karau, Holden  u. a. (2015): Learning Spark. First edition. Beijing; Sebastopol: O’Reilly.   
• Pellegrini, Tassilo; Blumauer, Andreas (Hrsg.) (2006): Semantic Web: die vernetzte Wissensgesellschaft. Berlin: Springer. 
• Ryza, Sandy  u. a. (2015): Advanced analytics with Spark. First edition. Beijing; Sebastopol, CA: O’Reilly.   
• Segaran, Toby ; Taylor, Jamie, Evans, Colin (2009): Programming the Semantic Web. Beijing; Sebastopol, CA: O’Reilly.   
• Spivey, Ben ; Echeverria, Joey (2015): Hadoop security. First edition. Beijing; Sebastopol: O’Reilly.   
• Talabis, Mark (2014): Information security analytics: finding security insights, patterns and anomalies in big data. 1st edition. Waltham, MA: Elsevier.   
• White, Tom (2015): Hadoop: the definitive guide. Fourth edition. Beijing: O’Reilly.   
• Witten, I. H. ; Frank, Eibe ; Hall, Mark A. (2011): Data mining: practical machine learning tools and techniques. 3rd ed. Burlington, MA: Morgan Kaufmann.  (= Morgan Kaufmann series in data management systems).   
• Yu, Liyang (2011): A developer’s guide to the semantic web. Heidelberg; New York: Springer.

Face-to-face