"INNOVATE delivers a model for the description of: (i) queries; (ii) QCs – entities responsible for the allocation of queries; (iii) QPs – entities placed in nodes (e.g. edge nodes) where the data are collected. We propose the use of specific characteristics for modelling queries and QPs. We adopt characteristics that can be matched each other: The complexity class of a query with the load of QPs; The deadline for execution with the speed of processing; The constraints of a query with the data present in a node. Based on the matching process, we try to reveal the most efficient allocations. We propose models for the definition of the load of each QP, the matching between the constraints and the data present in nodes and the definition of the complexity class of a query.
An additional goal is to create a pool of learners that will become the basis for the definition of an ensemble learning scheme. The adopted learners are: (i) C4.5 decision tree; (ii) Random tree; (iii) Naive Bayes model; (iv) Bayesian Network; (v) Multinomial Naive Bayes model; (vi) Random Forest; (vii) Logistic Model Tree; (viii) REPTree model; (ix) JRip algorithm; (x) Multilayer Perceptron. We build and provide an ensemble learning scheme based on the pool of the adopted learners. Afterwards, we propose a meta-ensemble scheme defined by multiple ensemble learning models, i.e. The AdaBoost model; The Stacking model; The Bagging model. These ensemble schemes are ‘combined’ to define our meta-ensemble learning scheme based on the One-Over-All (OVA) methodology.
We also adopt Fuzzy Logic and propose the use of a Type-2 FL System having as inputs the aforementioned characteristic of queries and nodes/QPs and resulting the so called Efficiency of Allocation (EoA). EoA depicts the certainty (or uncertainty) that an allocation is optimal or not. We enhance the proposed system with an additional input that represents the opinion of an ‘expert’ about the specific allocation, i.e. a Support Vector Machine (SVM) model.
INNOVATE results were depicted by the following publications:
1. Y. Kathidjiotis, K. Kolomvatsos, C. Anagnostopoulos, ‘Predictive intelligence of reliable analytics in distributed computing environments’, Springer Applied Intelligence, 10.1007/s10489-020-01712-5 2020
2. K. Kolomvatsos, C. Anagnostopoulos, ‘A probabilistic Model for Assigning Queries at the Edge’, Springer Computing, 102, 865–892, 2020
3. K. Kolomvatsos, C. Anagnostopoulos, ‘Multi-criteria Optimal Task Allocation at the Edge’, Elsevier Future Generation Computer Systems, 2019
4. K. Kolomvatsos, ‘A Distributed, Proactive Intelligent Scheme for Securing Quality in Large Scale Data Processing’, Springer Computing, 2019.
5. K. Kolomvatsos, ‘An Efficient Scheme for Applying Updates in Pervasive Computing Applications’, Journal of Parallel and Distributed Computing, Elsevier, 128, 2019, pp. 1-14.
6. A. Karanika. P. Oikonomou, K. Kolomvatsos, C. Anagnostopoulos, ‘An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge’, in International IFIP CD-MAKE 2020.
7. Karanika, A., Oikonomou, P., Kolomvatsos, K., Loukopoulos, T., ‘A Demand-driven, Proactive Tasks Management Model at the Edge’, IEEE FUZZ-IEEE, 2020.
8. Karanika, A., Soula, M., Anagnostopoulos, C., Kolomvatsos, K., Stamoulis, G., ‘Optimized Analytics Query Allocation at the Edge of the Network’, 12th ICIDCS, Naples, Italy, 2019.
9. E. Aleksandrova, C. Anagnostopoulos, K. Kolomvatsos, ‘Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach’, 18th IEEE International Symposium on Parallel and Distributed Computing, 2019
11. K. Kolomvatsos, C. Anagnostopoulos, ‘In-Network Edge Intelligence for Optimal Task Allocation’, 30th ICTAI, Volos, Greece, 2018
12. K. Kolomvatsos, C. Anagnostopoulos, ‘An Edge-Centric Ensemble Scheme for Queries Assignment’, 8th International Workshop on Combinations of Intelligent Methods and Applications, Volos, Greece, 2018
13. Ivanov, H., Anagnostopoulos, C., Kolomvatsos, K., ‘In-Network Machine Learning Predictive Analytics: A Swarm Intelligence Approach’, in Convergence of Artificial Intelligence and the Internet of Things, Springer, 2020.
14. K. Kolomvatsos, C. Anagnostopoulos, ‘Edge-Centric Queries Stream Management based on an Ensemble Model’, Springer ""Smart Innovation, Systems and Technologies"" series volume, 2020."
