Thesis supervisor: András Hajdu
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: DE IK
Description of the research topic:
Syllabus
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
1. Kellerer, Hans, Pferschy, Ulrich, Pisinger, David: Knapsack Problems, Springer, 2004.
2. Christos H. Papadimitriou, Kenneth Steiglitz: Combinatorial Optimization: Algorithms and Complexity, Dover Books on Computer Science, 1998.
3. Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea: Big Data: Algorithms, Analytics, and Applications, Chapman and Hall/CRC, 2015.
4. Ali Emrouznejad: Big Data Optimization: Recent Developments and Challenges, Springer, 2016.