Thesis supervisor: László Toka
Location of studies (in Hungarian): Department of Telecommunications and Media Informatics Abbreviation of location of studies: TMIT
Description of the research topic:
The evolution of the digital world, the ubiquitous connectivity and compute, and the spreading of Internet of Things technologies have led to an extremely steep increase in the amount of data collected from information sources of all kinds, e.g., environmental sensors, user and device logs from IT systems, financial transactions. The gathered information cannot be processed with traditional database solutions within an acceptable timeframe for several data analytics use cases, but novel systems consisting of edge computing and big data technologies are capable of handling huge amount of complex and rapidly changing data. Today both cloud solutions and artifical intelligence are the fast developing technological areas: new solutions and tools are emerging every day.
Components of data processing systems, including big data applications, are usually deployed to distributed systems built on server clusters. With the advent of virtualization, services and applications can be installed in virtual machines, containers or serverless functions and can be run on virtualized infrastructure, instead of on bare metal. Besides of applying virtualization in centralized resources, such as data centers, virtualized computing platform technologies also allow for the exploitation of the benefits of easily running data analytics applications on geographically scattered infrastructure for processing the data close to where it was generated.
The virtualized technologies running on distributed systems for data analytics applications pose numerous questions and research problems. In terms of networking, the research plan involves performance, reliability and economic aspects. In terms of AI solutions, the research plan includes the possibility of using deep learning techniques in a distributed setting. The overall goal is to model, analyze and design high performing big data frameworks that are distributed (in geographical sense) and reliable for applications that will determine the everyday life of society in the near future. The applicant must have a good understanding of data analytics use cases, data mining techniques, big data applications, compute and network virtualization technologies and distributed systems.
Required language skills: English Number of students who can be accepted: 1
Deadline for application: 2021-06-14
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).
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