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Big Data Analytics

392157/392158 Schönhuth Summer 2020 Thu 10:15-12:00 with a break (V) in ZOOM


The lecture Big Data Analytics develops competencies in performing data mining tasks on very large amounts of data that cannot be stored in main memory. The lecture provides the key ideas of similarity search using minhashing and locality-sensitive hashing, of data stream processing where data arrives so fast that it has to be processed immediately or is otherwise lost, of Web-related algorithms such as Google's PageRank, of algorithms for mining frequent itemsets, association rules and frequent subgraphs, of algorithms to analyze the structure of large graphs such as social network graphs, and of the map-reduce principle to design parallel algorithms.

  1. Finding Similar Items
  2. Stream Data Analysis
  3. PageRank
  4. MapReduce
  5. Mining Frequent Itemsets
  6. Mining Frequent Subgraphs
  7. Mining Social Network Graphs
  8. Recommender Systems

This class will be taught online, through video lectures.


  • A. Silberschatz, H. F. Korth, S. Sudarshan, „Database System Concepts“, 5th edition, McGraw Hill, 2006.
  • R. Elmasri und S.B. Navathe, „Fundamentals of Database Systems“, 5th edition, Pearson/Addison Wesley, 2007.
  • William H. Inmon, “Building the Data Warehouse”, John Wiley & Sons, 1996.
  • Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, “Mining of Massive Datasets”, 2nd Edition, Cambridge University Press, 2014.
  • Tom White, “Hadoop: The Definitive Guide Storage and Analysis at Internet Scale”, 3rd edition, O'Reilly.
  • Viktor Mayer-Schönberger , Kenneth Cukier , “ Big Data: A Revolution That Will Transform How We Live, Work and Think”, John Murray, 2013.
  • Eric Redmond , Jim R. Wilson, “Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement”, O' Reilly, 2012.
  • Peter Gulutzan, Trudy Pelzer , “SQL Performance Tuning”, Addison Wesley, 2002.

Time table

Date Topic
23.04.2020 Introduction / Organization / Schedules ( slides )
30.04.2020 Finding Similar Items I ( slides)
07.05.2020 Finding Similar Items II ( slides)
14.05.2020 Map Reduce I ( slides)
21.05.2020 no lecture
28.05.2020 Map Reduce II ( slides)
04.06.2020 Map Reduce III / Mining Data Streams I ( slides)
11.06.2020 no lecture
18.06.2020 Mining Data Streams II / Link Analysis I ( slides)
25.06.2020 Link Analysis II ( slides)
02.07.2020 Frequent Itemsets I ( slides)
09.07.2020 Recommendation Systems ( slides)
16.07.2020 Mining Data Streams III / Frequent Itemsets II (slides)

Examination dates

1st Exam: Written exam on Thursday, September 03, 2020 08:00-11:00 in the Stadthalle

2nd Exam: tentatively on Monday, September 28, 2020