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. Parallelization strategies like MapReduce, usability of developed systems and reproducability of results are two major aspects within the cooperation.

At the moment we are facing a fundamental change in the way people consume music: More and more people switch from private, mostly limited music collections to public music streaming collections containing several millions of tracks generating tons of data. The usability of such streaming services heavily relies on good recommender systems assisting users in discovering music they like. This makes the field of music recommendation and music information retrieval in a highly interesting topic for academia as well as industry.

With the exponential growth of data available in the digital universe, having fast and space efficient access to data is increasingly becoming more and more important. At the same time, the amount of main memory have reached capacities that even large enterprise datasets can entirely be kept in RAM. To create state of the art data management systems, it is crucial to optimize those systems for modern hardware, in particular for concurrency, cache and space efficiency.
DBIS therefore conducts research in the field of main memory index structures and graph databases.

Today more and more text documents are made publicly available through large text collections or literary databases. As recent events show, the detection of plagiarism in such systems becomes considerably more important. To counter this problem, we propose the Plag-Inn algorithm in different variants, which attempt to expose plagiarism in text documents by analyzing the grammar of authors and finding significant stylistic differences within a single document.

Isochrones in unimodal networks have been used in city planning for more than forty years. The information technology of the 21st century has enabled the calculation of them in multimodal networks. This means that various travel types, like using a bus, driving a car and going by feet, are observed. This takes the computation of isochrones to a much more complex level. We develop and improve algorithms that can be used to calculate large isochrones in multimodal networks.