Authorship Analysis and Cross-Language Grammar Features

Capturing the essence of the writing style of authors is an important research area in natural language processing. It allows to identify and attribute the author of a previously unseen document, perform so-called style change detection (find the positions at which the author changes within a document), detect plagiarism intrinsically, develop new technology for writing support, or perform forensic analyses.


The cooperation between the Genetic Epidemiology, Medical University Innsbruck (Univ.-Prof Florian Kronenberg) and the Research Group Databases and Information Systems  (DBIS, Univ.-Prof Günther Specht) already started in 2007. The overall goal of the cooperation is to find new ways of storing and processing big data derived from Next Generation Sequencing (NGS) using RDBMS as well as NoSql Systems.


With Building Information Modeling (BIM), all data that accrues in the course of the life cycle of a construction project will be collected and provided to all involved agents, from the idea for the project to the planning, the construction, the operation, and the utilization of the resources after the demolition. For this purpose, we work intensely on a standardization and internationalization of the properties in use, with their dimensions, units, responsibilities and dependencies on the project phase. As part of two cooperation projects with Tyrolean companies ...

Music Information Retrieval

Music is ubiquitous in today's world-almost everyone enjoys listening to music. With the rise of streaming platforms, the amount of music available has substantially increased. While users may seemingly benefit from this plethora of available music, at the same time, it has increasingly made it harder for users to explore new music and find songs they like.

Recommender Systems and User Modeling

Recommender systems are ubiquitous in the digital world and largely determine the options that humans get to choose from on web platforms, from online shopping to music streaming. Recommender systems are mostly built upon statistics of past collective user behavior to mimic human preference and decision-making, assuming that users like what similar users liked in the past (so-called collaborative filtering).