Thesis supervisor: András Hajdu
Location of studies (in Hungarian): Debreceni Egyetem Informatikai Kar Abbreviation of location of studies: DE IK
Description of the research topic:
To process large datasets, traditional algorithmic approaches are hardly applicable -- additional constraints are needed to be introduced. Classic approaches cannot be scaled to extreme data volumes (the main problem is the time complexity) and do not fit the current platforms realizing
distributed processing and data storage models. The aim of the research is the development of such optimization methods that can adopt the classic algorithmic approaches in processing problems including Big Data. Moreover, they can exploit the services provided by the platform realizing distributed processing and corresponding hardware acceleration.
Bibliography:
Kellerer, Hans, Pferschy, Ulrich, Pisinger, David: Knapsack Problems, Springer, 2004.
Christos H. Papadimitriou, Kenneth Steiglitz: Combinatorial Optimization: Algorithms and Complexity, Dover Books on Computer Science, 1998.
Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea: Big Data: Algorithms, Analytics, and Applications, Chapman and Hall/CRC, 2015.
Recommended language skills (in Hungarian): angol Number of students who can be accepted: 1
Deadline for application: 2019-01-15
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
Login
