Thesis supervisor: Zoltán Gál
Location of studies (in Hungarian): University of Debrecen Faculty of Informatics Abbreviation of location of studies: DE IK
Description of the research topic:
Syllabus
CEP (Complex Event Processing), ESP (Event Stream Processing) and SED (Surprise Event Detection) methods become hot topic of the research institutes and service developer companies in the last years. This phenomenon is obvious because the real-time control of different ICT (Information and Communication Technology) systems is a critical task. The optimal amount of measured data coming from different sensors should be determined in function of the target of the proposed problem. This aspect means not only the reduction of energy consumption by the communication subsystem of the sensor network, but filtering and augmenting of the redundant and missing data, respectively. The IFP (Information Flow Processing) is a natural attendant task of the IoT (Internet of Things). The EDA (Event-Driven Architecture) is a SOA (Service Oriented Architecture) where the event firing can trigger one or more services. From this approach the CEP can be considered a service receiving and detecting low level events and based on these generates high level events. CEP considers rules executing aggregation, filtering, and detection of low level events to create high level events. The research subject is oriented to intelligent and time critical decision process based on ICT and IoT events and application of the results in practice.
Bibliography
[1] David Luckham: The Power of Events. Addison Wesley, 2002.
[2] Hugh Taylor, Angela Yochem, Les Phillips, Frank Martinez: Event-Driven Architecture. Addison-Wesley 2009
[3] Sharma Chakravarthy, Qingchun Jiang: Stream Data Processing: A Quality of Service Perspective: Modeling, Scheduling, Load Shedding, and Complex Event Processing (Advances in Database Systems), Springer, 2009
[4] Real Time Intelligence & Complex Event Processing: http://www.complexevents.com/
[5] Jan F. Akyildiz, Mehmet Can Vuran, Wireless Sensor networks, Wiley, 2010.
[6] Ding, Zhengming, Zhao, Handong, Fu, Yun: Learning Representation for Multi-View Data Analysis, Springer International Publishing, 2019.
Deadline for application: 2022-11-15
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).
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