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Scalable Uncertainty Management, Summer 2011

Lecturer: Dr. Rainer Gemulla
Teaching assistants: Luciano del Corro, Maximilian Dylla


Lecture notes for the 2012 version of this course are publicly available.


This lecture covers techniques to manage massive amounts of uncertain and inconsistent data (e.g., data obtained from potentially distributed, heterogeneous, and conflicting sources on the Web). The lecture focuses on modeling, semantics, and efficient algorithms. We will touch a number of different research areas, including databases, semantic web, machine learning, and artificial intelligence.

List of topics (tentative):
  • Incomplete data
  • Inconsistent data
  • Probabilistic data
  • Query evaluation
  • Complexity of query evaluation
  • Approximate query evaluation
  • Data mining on uncertain data
  • Probabilistic graphical models
  • Distributed processing
  • Applications



All necessary concepts and techniques will be introduced in the lecture. Basic knowledge of database systems and probability theory is advantageous.

Requirements for the certificate

Details about the exam can be found here. Please select time slots suitable for you immediately. The deadline for registration is July 8, 2011.

Lecture notes

  • 00: Organization (pdf)
  • 01: Introduction (pdf)
  • 02: Incomplete databases (pdf); last updated: May 5, 14:00
  • 03: Datalog & provenance (pdf); last updated: June 2, 18:00
  • 04: Probabilistic databases (pdf); last updated: June 6, 10:00
  • 05: Query evaluation on probabilistic databases (pdf); last updated: July 1, 13:00
  • 06: Markov logic (pdf); last updated: July 15, 16:30


  • 01: Relational algebra & representation Systems (pdf)
  • 02: Queries on incomplete databases (pdf)
  • 03: Datalog and provenance (pdf)
  • 04: Finite probability (pdf)
  • 05: Probabilistic databases (pdf); bugs fixed in Exercise 5d and 6
  • 06: Query evaluation (pdf); updated with new exercises
  • 07: Exact intensional query evaluation (pdf)
  • 08: Approximate intensional query evaluation & Markov logic (pdf)
  • 09: Probabilistic graphical models (pdf); this exercise is covered in the next two exercise groups

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