Christos Bellas
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Christos Bellas has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Anastasios Gounaris
Aristotle University of Thessaloniki, Thessaloniki, Greece
4 shared publications - Georgia Kougka
Centre for Research and Technology Hellas (CERTH)
2 shared publications Anastasios Gounaris
1 shared publication- Anna-Valentini Michailidou
Aristotle University of Thessaloniki, Thessaloniki, Greece
1 shared publication - Apostolos N. Papadopoulos
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
1 shared publication - Athanasios Naskos
Aristotle University of Thessaloniki
1 shared publication
Latest Publications
- 10 April 2024
- Published by Springer Nature in Cluster Computing
- Vol. 27 (6) , 8289-8306
Optimizing task allocation in multi-query edge analytics
- 10 April 2024
- Published by Springer Nature in Cluster Computing
- Vol. 27 (6) , 8289-8306
Optimizing task allocation in multi-query edge analytics
- Open Access
- 13 February 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 12, 25397-25410
Optimizing the Execution of Product Data Models
The Product Data Model (PDM) is an example of a declarative data-centric approach to modelling information-intensive business processes, which offers flexibility and facilitates process optimization. Declarative approaches are the de facto choice in all modern data-oriented workflows, but they require an optimizer to choose among multiple, alternative execution plans that can produce the desired end product. In PDM business processes, current optimization heuristics suffer from severe limitations regarding both their efficiency and applicability to realistic scenarios, stemming from a lack of consideration for the resource perspective of the processes being modelled and the advances in modern data flow optimizers. This work tackles both of these limitations with the proposal of rank-based operation ordering optimizations tailored to the specificities of PDM, which are also combined with the consideration of the resources available to execute the process operations and parallelism options. Through an extensive evaluation of the proposed solutions, it is showcased that there are significant performance gains from the advanced rank-based operation ordering techniques with the added support of parallel execution. The speedups observed were up to 5.5X compared to the state-of-the-art optimization heuristics.
- 13 February 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 12, 25397-25410
Open AccessOptimizing the Execution of Product Data Models
The Product Data Model (PDM) is an example of a declarative data-centric approach to modelling information-intensive business processes, which offers flexibility and facilitates process optimization. Declarative approaches are the de facto choice in all modern data-oriented workflows, but they require an optimizer to choose among multiple, alternative execution plans that can produce the desired end product. In PDM business processes, current optimization heuristics suffer from severe limitations regarding both their efficiency and applicability to realistic scenarios, stemming from a lack of consideration for the resource perspective of the processes being modelled and the advances in modern data flow optimizers. This work tackles both of these limitations with the proposal of rank-based operation ordering optimizations tailored to the specificities of PDM, which are also combined with the consideration of the resources available to execute the process operations and parallelism options. Through an extensive evaluation of the proposed solutions, it is showcased that there are significant performance gains from the advanced rank-based operation ordering techniques with the added support of parallel execution. The speedups observed were up to 5.5X compared to the state-of-the-art optimization heuristics.
- 1 July 2021
- Published by Elsevier in Parallel Computing
- Vol. 104-105
HySet: A hybrid framework for exact set similarity join using a GPU
- 1 July 2021
- Published by Elsevier in Parallel Computing
- Vol. 104-105
HySet: A hybrid framework for exact set similarity join using a GPU
- Open Access
- 21 July 2020
- Published by Springer Nature in SN Computer Science
- Vol. 1 (5) , 1-13
A Methodology for Runtime Detection and Extraction of Threat Patterns
As the confidentiality and integrity of modern health infrastructures is threatened by intrusions and real-time attacks related to privacy and cyber-security, there is a need for proposing novel methodologies to predict future incidents and identify new threat patterns. The main scope of this article is to propose an advanced extension to current Intrusion Detection System (IDS) solutions, which (i) harvests the knowledge out of health data sources or network monitoring to construct models for new threat patterns and (ii) encompasses methods for detecting threat patterns utilizing also advanced unsupervised machine learning data analytic methodologies. Although the work is motivated by the health sector, it is developed in a manner that is directly applicable to other domains.
- 21 July 2020
- Published by Springer Nature in SN Computer Science
- Vol. 1 (5) , 1-13
Open AccessA Methodology for Runtime Detection and Extraction of Threat Patterns
As the confidentiality and integrity of modern health infrastructures is threatened by intrusions and real-time attacks related to privacy and cyber-security, there is a need for proposing novel methodologies to predict future incidents and identify new threat patterns. The main scope of this article is to propose an advanced extension to current Intrusion Detection System (IDS) solutions, which (i) harvests the knowledge out of health data sources or network monitoring to construct models for new threat patterns and (ii) encompasses methods for detecting threat patterns utilizing also advanced unsupervised machine learning data analytic methodologies. Although the work is motivated by the health sector, it is developed in a manner that is directly applicable to other domains.
- Open Access
- 1 July 2019
- Published by Association for Computing Machinery (ACM)
- Vol. 8, 1-10
Exact Set Similarity Joins for Large Datasets in the GPGPU paradigm
- 1 July 2019
- Published by Association for Computing Machinery (ACM)
- Vol. 8, 1-10
Open AccessExact Set Similarity Joins for Large Datasets in the GPGPU paradigm