Applied Algorithms for Large-Scale Problems
Project data
Department in charge
Business intelligence, e-science and Web mining are rapidly growing sources of extreme large scale problems. We aim to design efficient methods for these problems based on deep theoretical foundations and relying on emerging means of distributed or many-core architectures.
We expect new basic research results in the following areas:
- Web data and social network analysis;
- Content based image retrieval;
- Efficiency of distributed data warehouses for log processing and entity resolution;
- Handling mass data of transport and mobility.
We pass beyond existing technologies in designing new frameworks, combining distributed and multicore technologies, and seeking new, breakthrough applications in an interdisciplinary research of interrelated fields in Database Technologies, Theory of Algorithms, Search and Information Retrieval as well as Data Mining and Machine Learning. Our research is a mixture of theoretical bounds for approximation error and running time, models and heuristics for solving practical problems, and measurements on real life data.
