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

  
Degree programme:Master Computer Science
Type of degree:FH MasterĀ“s Degree Programme
 Full-time
 Summer Semester 2022
  

Course unit titleAdvanced Topics in Big Data, Security, and Semantic Modelling
Course unit code024912020401
Language of instructionEnglish
Type of course unit (compulsory, optional)Compulsory optional
Semester when the course unit is deliveredSummer Semester 2022
Teaching hours per week4
Year of study2022
Level of course unit (e.g. first, second or third cycle)Second Cycle (Master)
Number of ECTS credits allocated6
Name of lecturer(s)Peter REITER
Armin SIMMA


Prerequisites and co-requisites

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)
Course content

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...)
Learning outcomes

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).
Planned learning activities and teaching methods
  • Advanced topics in Big Data: integrated lecture
  • Semantics and Ontologies: integrated lecture
  • Security: lecture and labs
Assessment methods and criteria

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
Comment

Security:  mandatory attendance for exercises

Recommended or required reading
  • 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.
Mode of delivery (face-to-face, distance learning)

Face-to-face

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