Masterstudium Business Informatics - Fakultät für Informatik, TU Wien

some eld research, writing of a scientic paper assessing related work and concepts, and
presentations and discussions in the seminar.
Courses of Module:
3.0/2.0 VU End User Programming
3.0/2.0 SE End User Programming
SIT/IRV Advanced Information Retrieval & Visualization
ECTS-Credits: 6.0
Summary: This module teaches methods, concepts, and techniques of Information Retrieval
(IR) as well as Information Visualization (InfoVis) and Visual Analytics (VA) to
deal with the problem of information overload. Information Retrieval enables managing
large collections of unstructured text and multimedia content (such as typically found in
digital libraries and the web) and extracting selective content relevant to users' current
information needs. With ever-growing information repositories, fast and scalable retrieval
systems are key in helping people to complete their information tasks eciently.
Visualization can help to make sense of data (potentially returned by an IR engine), explore
complex information spaces or spot patterns and relationships by taking advantage
of the powerful human perceptual system that is extremely ecient in processing visual
input,. Information Visualization is an interdisciplinary eld of research that is concerned
with interactive visualizations of mainly abstract data and has its roots primarily in
computer science. Interactivity or the possibility to engage in an active discourse with
the representation lies at the core of Information Visualization. Visual Analytics extends
InfoVis by integrating automated analysis methods (e.g., statistics, data mining,
machine learning) and focusing on analytical reasoning supported by interactive visual
interfaces.
Learning Outcomes: The goal of this course is to gain in-depth knowledge about methods,
concepts, and techniques of Information Retrieval, Information Visualization (InfoVis)
and Visual Analytics. The students should be able to assess the quality of retrieval
system performances, visual representations and gain skills for problem analysis, the
design and implementation, as well as the evaluation of visualization systems.
Knowledge:
• Text processing and indexing (n-grams, Latent Semantic Indexing)
• Retrieval models (probabilistic retrieval models (e.g. BM25,language models))
• Evaluation of retrieval systems (measures, collections, system vs. user-based evaluation)
• Information Extraction and Natural Language Processing
• Interactive Information Retrieval and Information Retrieval Interfaces
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