t.BA.XX.IE1.14HS (Information Engineering 1) 
Module: Information Engineering 1
This information was generated on: 19 April 2024
Information Engineering 1
Organised by


Version: 2.0 start 01 February 2018

Module coordinator:

Martin Braschler (bram)

Learning objectives:

Objectives Competences Taxonomy levels
"Still googling around, or are you finding stuff yet?"
- you learn how to extract and distill information from unstructured data
F, M K1, K3
- you get an overview of real retrieval systems (e.g. Web search/Google, domain-specific search etc.) and you acquire retrieval skills
F K1
- you get a solid introduction to the field: basics, theory, state of the art, practice and evaluation
F, M K1, K2
- you know further application areas of IR-technology (e.g. recommender services such as Netflix) and understand the underlying approaches F K1, K2
- you are capable of selecting the right technology for retrieval tasks and of managing an information retrieval project
F, M K2, K4, K6
- you are capable of conducting a small evaluation of inofmration retrieval systems
F, M K3, K6
- you have hands-on experience with analyzing a given, unkonwn data set wirt regards to its characteristics and with making it searchable
F, M K3, K6

Module contents: 

We live in a world where the collection, processing and exploitation of information and data is becoming increasingly crucial. The term "Information Engineering" denotes for us the methods and approaches to design and implement knowledge-intensive information systems. These are core components with regards to the emerging area of "Data Science", i.e., the area that studies processing and analysis of data and information of any characteristic (including Big Data).

Nowadays, the large majority of newly produced data is unstructured. The course "Information Engineering 1" investigates what information can be extracted from such data - far beyond "classical search". The course focuses on the field of "information retrieval", which is the basis for the success of companies such as Google and services such as Netflix.

- introduction to information retrieval (IR): definitions, differentiation, data - information - knowledge, search paradoxon and retrieval problem (2 weeks)
- basics: models, probability ranking principle, ranking guidelines (2 weeks)
- indexing/matching: text processing, weighting (2 weeks)
- systems/architecture: search engines, Web search (such as Google),  data structures (2 weeks)
- automatic categorization/recommender systems (such as Netflix) (2 weeks)

- IR projects supporting knowledge-intensive business processes (1 week)
- evaluation of IR systems: methodology, test collections, case studies (2 weeks)
- possibly advanced subjects (e.g. multimedia retrieval, multilingual retrieval) (1 weeks)


C. Peters, M. Braschler, P. Clough: "Multilingual Information Retrieval: From Research To Practice", Springer, 2012.
Set of slides

Supplementary literature:

R. Baeza-Yates, B. Ribeiro-Neto. Modern Information Retrieval, 2nd Ed., ACM Press, 2010
C. D. Manning, P. Raghavan, H. Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008


Fundamental computer science courses
Databases 1

Teaching language:


Module structure:

Form of instruction: Number of lessons per week:
Lecture: 14*2
Labs: 14*2
Block course:  


The regulation on graded class assignments is binding. However, it may be waived if a formal, written request is made by the lecturer in the first week of the semester.
Designation Type Form Scope Grade Weighting
Graded assignments during teaching semester Semester short project written experiment+short report   20%
End-of-semester exam End-of-semester exam written 90 minutes   80%


Part of the larger consecutive module "Information Engineering"


Course: Information Engineering 1 - Praktikum
Information Engineering 1 - Praktikum


  • No module description is available in the system for the cut-off date of 19 April 2024.
Course: Information Engineering 1 - Vorlesung
Information Engineering 1 - Vorlesung


  • No module description is available in the system for the cut-off date of 19 April 2024.