Data structures are methods for organizing data so they can be accessed efficiently. Good data structures are important for applications in database systems, for instance, where data storage is central, but also as subelements of algorithms for computational problems of almost all kinds. Often, there is a direct connection between the efficiency of the data structure and the efficiency of the algorithm, and to find and/or develop the right data structure is often a key element in the development of a new and better algorithm for a given problem. Thus, data structures is one of the classic research areas of Computer Science.
IMADA's research in data structures evolves around fundamental, generally-applicable data structures such as search trees, priority queues, and graph representations, as well as specialized data structures aimed at
applications in bioinformatics, database systems, geometric algorithms, and string algorithms, and also data structures with specialized features, for instance for utilizing repetitions or other systematic operation patterns on the structure.
Our most pronounced focus point is data structures for massive data. Here, it is primarily harddisk access rather than CPU requirements that dominates execution time, and significant improvements in runtime performance are achievable by developing data structures optimizing this access.
The group has a large and active cooperation with many national and international partners, e.g., University of Aarhus (Denmark), Technical University of Denmark, IT University (Denmark), as well as Carleton University (Canada), Dalhousie University (Canada), University of Waterloo (Cananda), Stony Brook (USA), NYU-Poly (USA), Goethe University Frankfurt (Germany), Technical University Munich (Germany).
Kim Skak Larsen