Taotletava projekti eesmärk on arendada kompleksselt andmemahukate ning keeruliste probleemide lahendamiseks vajalikke algoritme, tarkvara, meetodeid ja hajusarvutuste tarkvaralist infrastruktuuri lähtudes rakendusvaldkondade praktilistest võtmeprobleemidest ja vajadustest. Projekti käigus soovime 1) arendada uusi analüüsimeetodeid andmekaevanduse, mustrite tuvastamise ning suurte lineaarsete võrrandisüsteemide lahendamiseks, 2) arendada korrektseid, töökindlaid ja turvalisi GRID arvutuste vahekihte mille kaudu punkti 1 meetodeid rakendada, 3) arendada lõppkasutajatele sobivaid kasutajaliideseid ja kasutajakoolitust ja 4) positiivse lõpptulemusena lahendada rakendusvaldkondade (sh. bio- ja meditsiiniinformaatika, arvutiturvalisuse ning info-otsingute) spetsiifilisi probleeme.
The goal of the research is to develop in an integrated manner novel methods and tools for solving large-scale and complex computational problems on distributed environments like GRID. We will develop methods for formal validation, data security and protection, middleware, as well as algorithms and methods for different applications that require large-scale data analysis. Overall, we will 1) develop data mining, pattern discovery, and machine learning algorithms and tools, 2) continue developing the DOUG solver for solving very large linear equations (Domain Decomposition on Unstructured Grids), 3) develop formal methods and practical approaches for ensuring the correctness, robustness, and data protection of GRID computations, 4) develop end-user interfaces and study user training aspects, and last but not least, 5) will apply the developed methods for solving various problems in several application areas, including bioinformatic analyses of gene regulatory networks and gene transcriptional control, computer systems logs analysis, and large database analysis.