New data intensive algorithms and structures for GPU processors
The goal of the project is to design new or adapt existing parallel structures and algorithms for multi-core general purpose graphic processing units. Among results we expect significant acceleration of existing solutions and enlargement of processed volumes of data. Scientific hypothesis proposes that this goal may be achieved with popular GPU devices and personal computers.
The dissertation also discusses an exemplary application of time series databases: the analysis of zebra mussel (Dreissena polymorpha) behaviour based on observations of the change of the gap between the valves, collected as a time series. We propose a new al- gorithm based on wavelets and kernel methods that detects relevant events in the collected data. This algorithm allows us to extract elementary behaviour events from the observa- tions. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions. Since zebra mussels are well-known bioindicators this is an important step towards the creation of an advanced environmental biomonitoring system.