2024
Chakkol, M.; Johnson, M.; Karatzas, A.; Papadopoulos, G.; Korfiatis, N.
Making supply chains great again: examining structural changes to US manufacturing supply chains Journal Article
In: International Journal of Operations and Production Management, vol. 44, no. 5, pp. 1083-1108, 2024, (cited By 0).
@article{Chakkol20241083,
title = {Making supply chains great again: examining structural changes to US manufacturing supply chains},
author = {M. Chakkol and M. Johnson and A. Karatzas and G. Papadopoulos and N. Korfiatis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168112831&doi=10.1108%2fIJOPM-12-2022-0783&partnerID=40&md5=01cacfff17d30ab3cd4a76567b6dce0b},
doi = {10.1108/IJOPM-12-2022-0783},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Operations and Production Management},
volume = {44},
number = {5},
pages = {1083-1108},
note = {cited By 0},
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Yang, N.; Dousios, D.; Korfiatis, N.; Chalvatzis, K.
Mapping the signaling environment between sustainability-focused entrepreneurship and investment inputs: A topic modeling approach Journal Article
In: Business Strategy and the Environment, 2024, (cited By 0).
@article{Yang2024,
title = {Mapping the signaling environment between sustainability-focused entrepreneurship and investment inputs: A topic modeling approach},
author = {N. Yang and D. Dousios and N. Korfiatis and K. Chalvatzis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190362254&doi=10.1002%2fbse.3748&partnerID=40&md5=08ee6c3347e3f905f5bd7c239110c0b8},
doi = {10.1002/bse.3748},
year = {2024},
date = {2024-01-01},
journal = {Business Strategy and the Environment},
note = {cited By 0},
keywords = {},
pubstate = {published},
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Stavropoulou, G.; Tsitseklis, K.; Mavraidi, L.; Chang, K. -I.; Zafeiropoulos, A.; Karyotis, V.; Papavassiliou, S.
Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes Journal Article
In: Sensors, vol. 24, no. 8, 2024, ISSN: 14248220.
@article{Stavropoulou2024,
title = {Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes},
author = {G. Stavropoulou and K. Tsitseklis and L. Mavraidi and K. -I. Chang and A. Zafeiropoulos and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191412428&doi=10.3390%2fs24082618&partnerID=40&md5=cf5b51c8b7dcd4b785279973b67a7847},
doi = {10.3390/s24082618},
issn = {14248220},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Sensors},
volume = {24},
number = {8},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased. © 2024 by the authors.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased. © 2024 by the authors.
Dimitriou, P.; Karyotis, V.
Empowering Random Walk Link Prediction Algorithms in Complex Networks by Adapted Structural Information Journal Article
In: IEEE Access, vol. 12, pp. 45044-45059, 2024, ISSN: 21693536, (cited By 0).
@article{Dimitriou202445044,
title = {Empowering Random Walk Link Prediction Algorithms in Complex Networks by Adapted Structural Information},
author = {P. Dimitriou and V. Karyotis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189142829&doi=10.1109%2fACCESS.2024.3381510&partnerID=40&md5=87890e22714752dd52338e7a791b1f94},
doi = {10.1109/ACCESS.2024.3381510},
issn = {21693536},
year = {2024},
date = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {45044-45059},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the link prediction problem a relevant algorithm running over a network attempts to determine whether a link between two nodes will exist in the future, given that it is not present at the moment. Most link prediction algorithms take into account the structure of the network on which they are applied and based on this, they attempt to predict the existence or not of future new edges in the network. However, many of them are quite standardized, applying the same concept and parametrization to all networks, thus not always achieving good results in every different network structure. Algorithms based on Graph Neural Networks (GNNs) are more adaptive to any network structure but they do not give appreciable results when the only information available is the network structure. In this paper, we propose a new approach to this problem that approximates the structure of a complex network by allowing adjusted weight to this network structure to create additional information, which we can embed into effective algorithms such as local and superposed random walk link prediction. To achieve this goal, we use well-known kernel functions such as Sigmoids, in which we fit their parameters appropriately by a genetic algorithm to achieve the best possible approximation. To demonstrate the effectiveness of our proposed method we have compared our prediction method results based on precision, AUC and AUPR on eleven selected networks of different structures and properties with seven well-known link prediction algorithms and one more utilizing GNNs. In every case, we have improved the results of random walk algorithms and in most cases we achieved better results from all employed benchmark algorithms. © 2013 IEEE.},
note = {cited By 0},
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In the link prediction problem a relevant algorithm running over a network attempts to determine whether a link between two nodes will exist in the future, given that it is not present at the moment. Most link prediction algorithms take into account the structure of the network on which they are applied and based on this, they attempt to predict the existence or not of future new edges in the network. However, many of them are quite standardized, applying the same concept and parametrization to all networks, thus not always achieving good results in every different network structure. Algorithms based on Graph Neural Networks (GNNs) are more adaptive to any network structure but they do not give appreciable results when the only information available is the network structure. In this paper, we propose a new approach to this problem that approximates the structure of a complex network by allowing adjusted weight to this network structure to create additional information, which we can embed into effective algorithms such as local and superposed random walk link prediction. To achieve this goal, we use well-known kernel functions such as Sigmoids, in which we fit their parameters appropriately by a genetic algorithm to achieve the best possible approximation. To demonstrate the effectiveness of our proposed method we have compared our prediction method results based on precision, AUC and AUPR on eleven selected networks of different structures and properties with seven well-known link prediction algorithms and one more utilizing GNNs. In every case, we have improved the results of random walk algorithms and in most cases we achieved better results from all employed benchmark algorithms. © 2013 IEEE.
Kakkavas, G.; Fryganiotis, N.; Karyotis, V.; Papavassiliou, S.
Generative Deep Learning Techniques for Traffic Matrix Estimation from Link Load Measurements Journal Article
In: IEEE Open Journal of the Communications Society, vol. 5, pp. 1029-1046, 2024, ISSN: 2644125X, (cited By 0).
@article{Kakkavas20241029,
title = {Generative Deep Learning Techniques for Traffic Matrix Estimation from Link Load Measurements},
author = {G. Kakkavas and N. Fryganiotis and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183941616&doi=10.1109%2fOJCOMS.2024.3358740&partnerID=40&md5=bbc1e767cb9b5d8bf30cadd9973dd81a},
doi = {10.1109/OJCOMS.2024.3358740},
issn = {2644125X},
year = {2024},
date = {2024-01-01},
journal = {IEEE Open Journal of the Communications Society},
volume = {5},
pages = {1029-1046},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Traffic matrices (TMs) contain crucial information for managing networks, optimizing traffic flow, and detecting anomalies. However, directly measuring traffic to construct a TM is resource-intensive and computationally expensive. A more practical approach involves estimating the TM from readily available link load measurements, which falls under the category of inferential network monitoring based on indirect measurements known as network tomography. This paper focuses on solving the problem of estimating the traffic matrix from link loads by utilizing deep generative models. The proposed models are trained using historical data - specifically, previously observed TMs - and are then leveraged to transform traffic matrix estimation (TME) into a simpler minimization problem in a lower-dimensional latent space. This transformed problem can be efficiently solved using a gradient-based optimizer. Our work aims to examine and test different model architectures and optimization approaches. The performance of the proposed methods is comparatively evaluated over a comprehensive set of suitable metrics on two publicly available datasets comprising actual traffic matrices obtained from real backbone networks. In addition, we compare our approach with a state-of-the-art method previously published in the literature. © 2020 IEEE.},
note = {cited By 0},
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pubstate = {published},
tppubtype = {article}
}
Traffic matrices (TMs) contain crucial information for managing networks, optimizing traffic flow, and detecting anomalies. However, directly measuring traffic to construct a TM is resource-intensive and computationally expensive. A more practical approach involves estimating the TM from readily available link load measurements, which falls under the category of inferential network monitoring based on indirect measurements known as network tomography. This paper focuses on solving the problem of estimating the traffic matrix from link loads by utilizing deep generative models. The proposed models are trained using historical data - specifically, previously observed TMs - and are then leveraged to transform traffic matrix estimation (TME) into a simpler minimization problem in a lower-dimensional latent space. This transformed problem can be efficiently solved using a gradient-based optimizer. Our work aims to examine and test different model architectures and optimization approaches. The performance of the proposed methods is comparatively evaluated over a comprehensive set of suitable metrics on two publicly available datasets comprising actual traffic matrices obtained from real backbone networks. In addition, we compare our approach with a state-of-the-art method previously published in the literature. © 2020 IEEE.
Nikopoulou, M.; Kourouthanassis, P.; Pateli, A.
In: Tourism, vol. 72, no. 1, pp. 40-55, 2024, (cited By 0).
@article{Nikopoulou202440,
title = {The Role of Organizational Innovativeness and Size on Information and Communication Technology (ICT) Adoption During COVID-19: Evidence From the Hospitality Industry},
author = {M. Nikopoulou and P. Kourouthanassis and A. Pateli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188427127&doi=10.37741%2ft.72.1.4&partnerID=40&md5=f35994a12637ddf2b03794d13727b3e1},
doi = {10.37741/t.72.1.4},
year = {2024},
date = {2024-01-01},
journal = {Tourism},
volume = {72},
number = {1},
pages = {40-55},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Nikopoulou, Maria; Kourouthanassis, Panos; Chasapi, Giota; Pateli, Adamantia; Mylonas, Naoum
Determinants of Digital Transformation in the Hospitality Industry: Technological, Organizational, and Environmental Drivers Journal Article
In: Sustainability, vol. 15, no. 3, pp. 2736, 2023.
@article{nikopoulou2023determinants,
title = {Determinants of Digital Transformation in the Hospitality Industry: Technological, Organizational, and Environmental Drivers},
author = {Maria Nikopoulou and Panos Kourouthanassis and Giota Chasapi and Adamantia Pateli and Naoum Mylonas},
year = {2023},
date = {2023-01-01},
journal = {Sustainability},
volume = {15},
number = {3},
pages = {2736},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Karatzas, A.; Papadopoulos, G.; Stamolampros, P.; Raja, J. Z.; Korfiatis, N.
Front- and back-end employee satisfaction during service transition Journal Article
In: International Journal of Operations and Production Management, vol. 43, no. 7, pp. 1121-1147, 2023, (cited By 1).
@article{Karatzas20231121,
title = {Front- and back-end employee satisfaction during service transition},
author = {A. Karatzas and G. Papadopoulos and P. Stamolampros and J. Z. Raja and N. Korfiatis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146287671&doi=10.1108%2fIJOPM-06-2022-0352&partnerID=40&md5=aaab6ee17bf8e9deba30438547bf14ff},
doi = {10.1108/IJOPM-06-2022-0352},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Operations and Production Management},
volume = {43},
number = {7},
pages = {1121-1147},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Argyropoulou, M.; Zissis, D.; Korfiatis, N.; Zampou, E.
Horizontal collaboration in the last mile distribution: gauging managerial response to disruption and abnormal demand Journal Article
In: Benchmarking, vol. 30, no. 2, pp. 460-474, 2023, (cited By 6).
@article{Argyropoulou2023460,
title = {Horizontal collaboration in the last mile distribution: gauging managerial response to disruption and abnormal demand},
author = {M. Argyropoulou and D. Zissis and N. Korfiatis and E. Zampou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126354347&doi=10.1108%2fBIJ-06-2021-0328&partnerID=40&md5=14ebd486a4a11a9355efa73cb560a93b},
doi = {10.1108/BIJ-06-2021-0328},
year = {2023},
date = {2023-01-01},
journal = {Benchmarking},
volume = {30},
number = {2},
pages = {460-474},
note = {cited By 6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Yang, N.; Korfiatis, N.; Zissis, D.; Spanaki, K.
Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach Journal Article
In: Annals of Operations Research, 2023, (cited By 0).
@article{Yang2023,
title = {Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach},
author = {N. Yang and N. Korfiatis and D. Zissis and K. Spanaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162233226&doi=10.1007%2fs10479-023-05336-z&partnerID=40&md5=9ff3d3a1fbdb5fc860830719ef0e6fd0},
doi = {10.1007/s10479-023-05336-z},
year = {2023},
date = {2023-01-01},
journal = {Annals of Operations Research},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dimitriou, P.; Karyotis, V.
On the computation of Delaunay triangulations via genetic algorithms Journal Article
In: Evolutionary Intelligence, vol. 13, no. 17, 2023, ISSN: 18645909, (cited By 0).
@article{Dimitriou2023,
title = {On the computation of Delaunay triangulations via genetic algorithms},
author = {P. Dimitriou and V. Karyotis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180719695&doi=10.1007%2fs12065-023-00893-5&partnerID=40&md5=ce28c67dcb7bd7bbfa3417161b1fa975},
doi = {10.1007/s12065-023-00893-5},
issn = {18645909},
year = {2023},
date = {2023-01-01},
journal = {Evolutionary Intelligence},
volume = {13},
number = {17},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In this work, we introduce a new approach for computing Delaunay triangulations. Delaunay triangulations have numerous applications in geolocation, communications and other ICT systems or practical applications. Having available various types of approaches for computing such structures is rather desired from an implementation and computational point of view. We adopt Genetic Algorithms for computing Delaunay triangulations and present the design and evaluation of our novel approach. We consider a set of points in the plane as vertices and connect them with edges, creating the point graph. We have developed in C++ an application framework based on genetic algorithms, called Delaunay_Genetic, which produces the Delaunay triangulation structure of a given set of points in the plane. Delaunay_Genetic considers a novel graph-based chromosome representation of desired solutions, creates an initial population of individuals (chromosomes), an initial generation, and produces from the original population (generation) new generations of individuals in each repetition of the genetic process of Reproduction. Each new generation emerges more robust than the previous one. Our evaluations have revealed that the Delaunay triangulation yielded by Delaunay_Genetic, achieves an accuracy of 98–100% of the optimal Delaunay triangulation, while maintaining good convergence speed. Despite its limitations in computational time and space, the proposed novel approach exhibits several complementary benefits to computational geometry based approaches, such as allowing the insertion of new points in the triangulation dynamically, leading to seamless adaptation to new conditions, parallelization of the computational process and tolerance to noise regarding the coordinates of the points. Therefore, this work provides a useful alternative approach for computing Delaunay triangulations. © 2023, The Author(s).},
note = {cited By 0},
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pubstate = {published},
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}
In this work, we introduce a new approach for computing Delaunay triangulations. Delaunay triangulations have numerous applications in geolocation, communications and other ICT systems or practical applications. Having available various types of approaches for computing such structures is rather desired from an implementation and computational point of view. We adopt Genetic Algorithms for computing Delaunay triangulations and present the design and evaluation of our novel approach. We consider a set of points in the plane as vertices and connect them with edges, creating the point graph. We have developed in C++ an application framework based on genetic algorithms, called Delaunay_Genetic, which produces the Delaunay triangulation structure of a given set of points in the plane. Delaunay_Genetic considers a novel graph-based chromosome representation of desired solutions, creates an initial population of individuals (chromosomes), an initial generation, and produces from the original population (generation) new generations of individuals in each repetition of the genetic process of Reproduction. Each new generation emerges more robust than the previous one. Our evaluations have revealed that the Delaunay triangulation yielded by Delaunay_Genetic, achieves an accuracy of 98–100% of the optimal Delaunay triangulation, while maintaining good convergence speed. Despite its limitations in computational time and space, the proposed novel approach exhibits several complementary benefits to computational geometry based approaches, such as allowing the insertion of new points in the triangulation dynamically, leading to seamless adaptation to new conditions, parallelization of the computational process and tolerance to noise regarding the coordinates of the points. Therefore, this work provides a useful alternative approach for computing Delaunay triangulations. © 2023, The Author(s).
Nikiforos, M. N.; Deliveri, K.; Kermanidis, K. L.; Pateli, A.
Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing † Journal Article
In: Computers, vol. 12, no. 6, 2023, (cited By 0).
@article{Nikiforos2023,
title = {Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing †},
author = {M. N. Nikiforos and K. Deliveri and K. L. Kermanidis and A. Pateli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163617082&doi=10.3390%2fcomputers12060111&partnerID=40&md5=946cf5523adfabb6fc705fe24e9fdba3},
doi = {10.3390/computers12060111},
year = {2023},
date = {2023-01-01},
journal = {Computers},
volume = {12},
number = {6},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2022
Leonidou, L. C.; Eteokleous, P. P.; Christofi, A. -M.; Korfiatis, N.
Drivers, outcomes, and moderators of consumer intention to buy organic goods: Meta-analysis, implications, and future agenda Journal Article
In: Journal of Business Research, vol. 151, pp. 339-354, 2022, (cited By 21).
@article{Leonidou2022339,
title = {Drivers, outcomes, and moderators of consumer intention to buy organic goods: Meta-analysis, implications, and future agenda},
author = {L. C. Leonidou and P. P. Eteokleous and A. -M. Christofi and N. Korfiatis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134350742&doi=10.1016%2fj.jbusres.2022.06.027&partnerID=40&md5=aa80a245377c085dadc9a1100b00449c},
doi = {10.1016/j.jbusres.2022.06.027},
year = {2022},
date = {2022-01-01},
journal = {Journal of Business Research},
volume = {151},
pages = {339-354},
note = {cited By 21},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Leonidou, L. C.; Aykol, B.; Samiee, S.; Korfiatis, N.
In: Management International Review, vol. 62, no. 5, pp. 741-784, 2022, (cited By 4).
@article{Leonidou2022741,
title = {A Meta-analysis of the Antecedents and Outcomes of Consumer Foreign Country Image Perceptions: The Moderating Role of Macro-level Country Differences},
author = {L. C. Leonidou and B. Aykol and S. Samiee and N. Korfiatis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137065635&doi=10.1007%2fs11575-022-00482-1&partnerID=40&md5=b339c56f003cf3e2b2d27f50862cd614},
doi = {10.1007/s11575-022-00482-1},
year = {2022},
date = {2022-01-01},
journal = {Management International Review},
volume = {62},
number = {5},
pages = {741-784},
note = {cited By 4},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chopdar, P. K.; Paul, J.; Korfiatis, N.; Lytras, M. D.
Examining the role of consumer impulsiveness in multiple app usage behavior among mobile shoppers Journal Article
In: Journal of Business Research, vol. 140, pp. 657-669, 2022, (cited By 54).
@article{Chopdar2022657,
title = {Examining the role of consumer impulsiveness in multiple app usage behavior among mobile shoppers},
author = {P. K. Chopdar and J. Paul and N. Korfiatis and M. D. Lytras},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119454161&doi=10.1016%2fj.jbusres.2021.11.031&partnerID=40&md5=9a5c4ebdf8f8f13159f8b24ecc66a3ab},
doi = {10.1016/j.jbusres.2021.11.031},
year = {2022},
date = {2022-01-01},
journal = {Journal of Business Research},
volume = {140},
pages = {657-669},
note = {cited By 54},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tsolakis, N.; Zissis, D.; Papaefthimiou, S.; Korfiatis, N.
Towards AI driven environmental sustainability: an application of automated logistics in container port terminals Journal Article
In: International Journal of Production Research, vol. 60, no. 14, pp. 4508-4528, 2022, (cited By 34).
@article{Tsolakis20224508,
title = {Towards AI driven environmental sustainability: an application of automated logistics in container port terminals},
author = {N. Tsolakis and D. Zissis and S. Papaefthimiou and N. Korfiatis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104889855&doi=10.1080%2f00207543.2021.1914355&partnerID=40&md5=52d969cfe0253171fb53ba51b1fddb2d},
doi = {10.1080/00207543.2021.1914355},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Production Research},
volume = {60},
number = {14},
pages = {4508-4528},
note = {cited By 34},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kakkavas, Grigorios; Diamanti, Maria; Stamou, Adamantia; Karyotis, Vasileios; Bouali, Faouzi; Pinola, Jarno; Apilo, Olli; Papavassiliou, Symeon; Moessner, Klaus
Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective Journal Article
In: Sensors, vol. 22, no. 2, pp. 426, 2022.
@article{kakkavas2022design,
title = {Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective},
author = {Grigorios Kakkavas and Maria Diamanti and Adamantia Stamou and Vasileios Karyotis and Faouzi Bouali and Jarno Pinola and Olli Apilo and Symeon Papavassiliou and Klaus Moessner},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
number = {2},
pages = {426},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Avgeris, Marios; Spatharakis, Dimitrios; Dechouniotis, Dimitrios; Leivadeas, Aris; Karyotis, Vasileios; Papavassiliou, Symeon
ENERDGE: Distributed energy-aware resource allocation at the edge Journal Article
In: Sensors, vol. 22, no. 2, pp. 660, 2022.
@article{avgeris2022enerdge,
title = {ENERDGE: Distributed energy-aware resource allocation at the edge},
author = {Marios Avgeris and Dimitrios Spatharakis and Dimitrios Dechouniotis and Aris Leivadeas and Vasileios Karyotis and Symeon Papavassiliou},
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Kakkavas, Grigorios; Karyotis, Vasileios; Papavassiliou, Symeon
Topology Inference and Link Parameter Estimation Based on End-to-End Measurements Journal Article
In: Future Internet, vol. 14, no. 2, pp. 45, 2022.
@article{kakkavas2022topology,
title = {Topology Inference and Link Parameter Estimation Based on End-to-End Measurements},
author = {Grigorios Kakkavas and Vasileios Karyotis and Symeon Papavassiliou},
year = {2022},
date = {2022-01-01},
journal = {Future Internet},
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Kallitsis, Georgios; Karyotis, Vasileios; Papavassiliou, Symeon
On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information Journal Article
In: Future Internet, vol. 14, no. 8, pp. 242, 2022.
@article{kallitsis2022potential,
title = {On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information},
author = {Georgios Kallitsis and Vasileios Karyotis and Symeon Papavassiliou},
year = {2022},
date = {2022-01-01},
journal = {Future Internet},
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Karageorgiou, Stavros; Karyotis, Vasileios
Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks Journal Article
In: Network, vol. 2, no. 3, pp. 456–478, 2022.
@article{karageorgiou2022markov,
title = {Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks},
author = {Stavros Karageorgiou and Vasileios Karyotis},
year = {2022},
date = {2022-01-01},
journal = {Network},
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Karageorgiou, S.; Karyotis, V.
Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks Journal Article
In: Network, vol. 2, no. 3, pp. 456-478, 2022, ISSN: 26738732, (cited By 3).
@article{Karageorgiou2022456,
title = {Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks},
author = {S. Karageorgiou and V. Karyotis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147550817&doi=10.3390%2fnetwork2030028&partnerID=40&md5=7ea772731efd89baef0e3a0dde1c8236},
doi = {10.3390/network2030028},
issn = {26738732},
year = {2022},
date = {2022-01-01},
journal = {Network},
volume = {2},
number = {3},
pages = {456-478},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
abstract = {In this paper, we focus on the dynamics of the spread of malicious software (malware) in multi-layer networks of various types, e.g., cyber-physical systems. Recurring malware has been one of the major challenges in modern networks, and significant research and development has been dedicated to mitigating it. The majority of relevant works has focused on networks characterized by “flat” topologies, namely topologies in which all nodes consist of a single layer, studying the dynamics of propagation of a specific threat or various types of malware over a homogeneous topology. As cyber-physical systems and multi-layer networks in general are gaining in popularity and penetration, more targeted studies are needed. In this work, we focus on the propagation dynamics of recurring malware, namely Susceptible–Infected–Susceptible (SIS type) in multi-layer topologies consisting of combinations of two different types of networks, e.g., a small-world overlaying a random geometric, or other such combinations. We utilize a stochastic modeling framework based on Markov Random Fields for analyzing the propagation dynamics of malware over such networks. Through analysis and simulation, we discover the most vulnerable and the most robust topology among the six considered combinations, as well as a result of rather practical use, namely that the denser the network, the more flexibility it provides for malware mitigation eventually. © 2022 by the authors.},
note = {cited By 3},
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In this paper, we focus on the dynamics of the spread of malicious software (malware) in multi-layer networks of various types, e.g., cyber-physical systems. Recurring malware has been one of the major challenges in modern networks, and significant research and development has been dedicated to mitigating it. The majority of relevant works has focused on networks characterized by “flat” topologies, namely topologies in which all nodes consist of a single layer, studying the dynamics of propagation of a specific threat or various types of malware over a homogeneous topology. As cyber-physical systems and multi-layer networks in general are gaining in popularity and penetration, more targeted studies are needed. In this work, we focus on the propagation dynamics of recurring malware, namely Susceptible–Infected–Susceptible (SIS type) in multi-layer topologies consisting of combinations of two different types of networks, e.g., a small-world overlaying a random geometric, or other such combinations. We utilize a stochastic modeling framework based on Markov Random Fields for analyzing the propagation dynamics of malware over such networks. Through analysis and simulation, we discover the most vulnerable and the most robust topology among the six considered combinations, as well as a result of rather practical use, namely that the denser the network, the more flexibility it provides for malware mitigation eventually. © 2022 by the authors.
Kallitsis, G.; Karyotis, V.; Papavassiliou, S.
On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information Journal Article
In: Future Internet, vol. 14, no. 8, 2022, ISSN: 19995903, (cited By 0).
@article{Kallitsis2022,
title = {On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information},
author = {G. Kallitsis and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136674256&doi=10.3390%2ffi14080242&partnerID=40&md5=88202815a5ca02b44073f8b85926fabd},
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issn = {19995903},
year = {2022},
date = {2022-01-01},
journal = {Future Internet},
volume = {14},
number = {8},
publisher = {MDPI},
abstract = {In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed applications deployed in a desert/sparsely populated area without infrastructure, a space network, etc. Delay-tolerant routing protocols can prevent excessive packet timer expiration, but do not provide any packet delivery time guarantee. Thus, they are unsuitable for time-sensitive applications that are more intensely desired nowadays in the next generation networking applications. By incorporating AoI into the operation of delay-tolerant routing protocols, we aim at devising routing protocols that can cope with both long propagation times and challenges related to time-sensitivity in packet delivery. More specifically, in this work, we modify the operation of a well-known delay-tolerant routing protocol, namely FRESH, to make AoI-based packet forwarding decisions, aiming at achieving specific delay guarantees regarding the end-to-end delivery time. We investigate the advantages and disadvantages of such an approach compared to the traditional FRESH protocol. This work serves as a cornerstone for successfully demonstrating the potential of exploiting AoI in delay-tolerant routing and its applications. © 2022 by the authors.},
note = {cited By 0},
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In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed applications deployed in a desert/sparsely populated area without infrastructure, a space network, etc. Delay-tolerant routing protocols can prevent excessive packet timer expiration, but do not provide any packet delivery time guarantee. Thus, they are unsuitable for time-sensitive applications that are more intensely desired nowadays in the next generation networking applications. By incorporating AoI into the operation of delay-tolerant routing protocols, we aim at devising routing protocols that can cope with both long propagation times and challenges related to time-sensitivity in packet delivery. More specifically, in this work, we modify the operation of a well-known delay-tolerant routing protocol, namely FRESH, to make AoI-based packet forwarding decisions, aiming at achieving specific delay guarantees regarding the end-to-end delivery time. We investigate the advantages and disadvantages of such an approach compared to the traditional FRESH protocol. This work serves as a cornerstone for successfully demonstrating the potential of exploiting AoI in delay-tolerant routing and its applications. © 2022 by the authors.
Kakkavas, G.; Diamanti, M.; Stamou, A.; Karyotis, V.; Bouali, F.; Pinola, J.; Apilo, O.; Papavassiliou, S.; Moessner, K.
Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective Journal Article
In: Sensors, vol. 22, no. 2, 2022, ISSN: 14248220, (cited By 16).
@article{Kakkavas2022,
title = {Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective},
author = {G. Kakkavas and M. Diamanti and A. Stamou and V. Karyotis and F. Bouali and J. Pinola and O. Apilo and S. Papavassiliou and K. Moessner},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122184044&doi=10.3390%2fs22020426&partnerID=40&md5=94c4a7adfcadc3d81cda5f51e00ce6d7},
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issn = {14248220},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
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publisher = {MDPI},
abstract = {The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 16},
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}
The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Avgeris, M.; Spatharakis, D.; Dechouniotis, D.; Leivadeas, A.; Karyotis, V.; Papavassiliou, S.
ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge Journal Article
In: Sensors, vol. 22, no. 2, 2022, ISSN: 14248220, (cited By 17).
@article{Avgeris2022,
title = {ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge},
author = {M. Avgeris and D. Spatharakis and D. Dechouniotis and A. Leivadeas and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123731074&doi=10.3390%2fs22020660&partnerID=40&md5=3f622c6a52a9e5935515a33023f22b4b},
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issn = {14248220},
year = {2022},
date = {2022-01-01},
journal = {Sensors},
volume = {22},
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publisher = {MDPI},
abstract = {Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 17},
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pubstate = {published},
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}
Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Kloutsiniotis, P. V.; Mihail, D. M.; Mylonas, N.; Pateli, A.
In: International Journal of Hospitality Management, vol. 102, 2022, (cited By 54).
@article{Kloutsiniotis2022,
title = {Transformational Leadership, HRM practices and burnout during the COVID-19 pandemic: The role of personal stress, anxiety, and workplace loneliness},
author = {P. V. Kloutsiniotis and D. M. Mihail and N. Mylonas and A. Pateli},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123318530&doi=10.1016%2fj.ijhm.2022.103177&partnerID=40&md5=b2bc134fe58bd75053ffdcb205bbeaa0},
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journal = {International Journal of Hospitality Management},
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Steininger, D. M.; Mikalef, P.; Pateli, A.; Ortiz-De-guinea, A.
Dynamic Capabilities in Information Systems Research: A Critical Review, Synthesis of Current Knowledge, and Recommendations for Future Research Journal Article
In: Journal of the Association for Information Systems, vol. 23, no. 2, pp. 447-490, 2022, (cited By 60).
@article{Steininger2022447,
title = {Dynamic Capabilities in Information Systems Research: A Critical Review, Synthesis of Current Knowledge, and Recommendations for Future Research},
author = {D. M. Steininger and P. Mikalef and A. Pateli and A. Ortiz-De-guinea},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126625462&doi=10.17705%2f1jais.00736&partnerID=40&md5=119bca6de7c73ada20df2c10c4e050ba},
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year = {2022},
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number = {2},
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2021
Kostagiolas, Petros; Milkas, Anastasios; Kourouthanassis, Panos; Dimitriadis, Kyriakos; Tsioufis, Konstantinos; Tousoulis, Dimitrios; Niakas, Dimitrios
The impact of health information needs' satisfaction of hypertensive patients on their clinical outcomes Journal Article
In: Aslib Journal of Information Management, vol. 73, no. 1, pp. 43–62, 2021.
@article{kostagiolas2021impact,
title = {The impact of health information needs' satisfaction of hypertensive patients on their clinical outcomes},
author = {Petros Kostagiolas and Anastasios Milkas and Panos Kourouthanassis and Kyriakos Dimitriadis and Konstantinos Tsioufis and Dimitrios Tousoulis and Dimitrios Niakas},
year = {2021},
date = {2021-01-01},
journal = {Aslib Journal of Information Management},
volume = {73},
number = {1},
pages = {43--62},
publisher = {Emerald Publishing Limited},
keywords = {},
pubstate = {published},
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Vlachakis, Dimitrios; Papakonstantinou, Eleni; Sagar, Ram; Bacopoulou, Flora; Exarchos, Themis; Kourouthanassis, Panos; Karyotis, Vasileios; Vlamos, Panayiotis; Lyketsos, Constantine; Avramopoulos, Dimitrios; others,
Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease Journal Article
In: Cells, vol. 10, no. 7, pp. 1627, 2021.
@article{vlachakis2021improvingb,
title = {Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease},
author = {Dimitrios Vlachakis and Eleni Papakonstantinou and Ram Sagar and Flora Bacopoulou and Themis Exarchos and Panos Kourouthanassis and Vasileios Karyotis and Panayiotis Vlamos and Constantine Lyketsos and Dimitrios Avramopoulos and others},
year = {2021},
date = {2021-01-01},
journal = {Cells},
volume = {10},
number = {7},
pages = {1627},
publisher = {MDPI},
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Symitsi, E.; Stamolampros, P.; Daskalakis, G.; Korfiatis, N.
The informational value of employee online reviews Journal Article
In: European Journal of Operational Research, vol. 288, no. 2, pp. 605-619, 2021, (cited By 29).
@article{Symitsi2021605,
title = {The informational value of employee online reviews},
author = {E. Symitsi and P. Stamolampros and G. Daskalakis and N. Korfiatis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087207797&doi=10.1016%2fj.ejor.2020.06.001&partnerID=40&md5=337fdaa0e01051c854c7ee8e852ad9a9},
doi = {10.1016/j.ejor.2020.06.001},
year = {2021},
date = {2021-01-01},
journal = {European Journal of Operational Research},
volume = {288},
number = {2},
pages = {605-619},
note = {cited By 29},
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Tsitseklis, Konstantinos; Vitoropoulou, Margarita; Karyotis, Vasileios; Papavassiliou, Symeon
Socio-Aware Recommendations Under Complex User Constraints Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 8, no. 2, pp. 377–387, 2021.
@article{tsitseklis2021socio,
title = {Socio-Aware Recommendations Under Complex User Constraints},
author = {Konstantinos Tsitseklis and Margarita Vitoropoulou and Vasileios Karyotis and Symeon Papavassiliou},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {8},
number = {2},
pages = {377--387},
publisher = {IEEE},
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Vitoropoulou, Margarita; Tsitseklis, Konstantinos; Karyotis, Vasileios; Papavassiliou, Symeon
CoveR: An Information Diffusion Aware Approach for Efficient Recommendations Under User Coverage Constraints Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 8, no. 4, pp. 894–905, 2021.
@article{vitoropoulou2021cover,
title = {CoveR: An Information Diffusion Aware Approach for Efficient Recommendations Under User Coverage Constraints},
author = {Margarita Vitoropoulou and Konstantinos Tsitseklis and Vasileios Karyotis and Symeon Papavassiliou},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {8},
number = {4},
pages = {894--905},
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Bouali, Faouzi; Pinola, Jarno; Karyotis, Vasileios; Wissingh, Bastiaan; Mitrou, Michalis; Krishnan, Prageeth; Moessner, Klaus
5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook Journal Article
In: IEEE Open Journal of Intelligent Transportation Systems, vol. 2, pp. 73–96, 2021.
@article{bouali20215g,
title = {5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook},
author = {Faouzi Bouali and Jarno Pinola and Vasileios Karyotis and Bastiaan Wissingh and Michalis Mitrou and Prageeth Krishnan and Klaus Moessner},
year = {2021},
date = {2021-01-01},
journal = {IEEE Open Journal of Intelligent Transportation Systems},
volume = {2},
pages = {73--96},
publisher = {IEEE},
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pubstate = {published},
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Kakkavas, Grigorios; Stamou, Adamantia; Karyotis, Vasileios; Papavassiliou, Symeon
Network tomography for efficient monitoring in SDN-enabled 5G networks and beyond: Challenges and opportunities Journal Article
In: IEEE Communications Magazine, vol. 59, no. 3, pp. 70–76, 2021.
@article{kakkavas2021network,
title = {Network tomography for efficient monitoring in SDN-enabled 5G networks and beyond: Challenges and opportunities},
author = {Grigorios Kakkavas and Adamantia Stamou and Vasileios Karyotis and Symeon Papavassiliou},
year = {2021},
date = {2021-01-01},
journal = {IEEE Communications Magazine},
volume = {59},
number = {3},
pages = {70--76},
publisher = {IEEE},
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Vlachakis, Dimitrios; Papakonstantinou, Eleni; Sagar, Ram; Bacopoulou, Flora; Exarchos, Themis; Kourouthanassis, Panos; Karyotis, Vasileios; Vlamos, Panayiotis; Lyketsos, Constantine; Avramopoulos, Dimitrios; others,
Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease Journal Article
In: Cells, vol. 10, no. 7, pp. 1627, 2021.
@article{vlachakis2021improving,
title = {Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease},
author = {Dimitrios Vlachakis and Eleni Papakonstantinou and Ram Sagar and Flora Bacopoulou and Themis Exarchos and Panos Kourouthanassis and Vasileios Karyotis and Panayiotis Vlamos and Constantine Lyketsos and Dimitrios Avramopoulos and others},
year = {2021},
date = {2021-01-01},
journal = {Cells},
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Tsitseklis, Konstantinos; Krommyda, Maria; Karyotis, Vasileios; Kantere, Verena; Papavassiliou, Symeon
Behavioral Information Diffusion for Opinion Maximization in Online Social Networks Journal Article
In: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, vol. 8, no. 2, pp. 1269–1282, 2021.
@article{tsitseklis2021behavioral,
title = {Behavioral Information Diffusion for Opinion Maximization in Online Social Networks},
author = {Konstantinos Tsitseklis and Maria Krommyda and Vasileios Karyotis and Verena Kantere and Symeon Papavassiliou},
year = {2021},
date = {2021-01-01},
journal = {IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING},
volume = {8},
number = {2},
pages = {1269--1282},
publisher = {IEEE COMPUTER SOC 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA~…},
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Mitrou, Michalis; Vlacheas, Panagiotis; Foteinos, Vassilis; Laskaridis, Vassilis; Koutsianopoulos, Konstantinos; Uitto, Mikko; Pinola, Jarno; Zhang, Haibin; Wissingh, Bastiaan; Morris, Donal; others,
Ioannis Tzanettis (Editor),“D6. 1 Preliminary Trials plan,” 5G-HEART Project, Deliverable D6. 1 Journal Article
In: 2021.
@article{mitrou2021ioannis,
title = {Ioannis Tzanettis (Editor),“D6. 1 Preliminary Trials plan,” 5G-HEART Project, Deliverable D6. 1},
author = {Michalis Mitrou and Panagiotis Vlacheas and Vassilis Foteinos and Vassilis Laskaridis and Konstantinos Koutsianopoulos and Mikko Uitto and Jarno Pinola and Haibin Zhang and Bastiaan Wissingh and Donal Morris and others},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vitoropoulou, M.; Tsitseklis, K.; Karyotis, V.; Papavassiliou, S.
CoveR: An Information Diffusion Aware Approach for Efficient Recommendations under User Coverage Constraints Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 8, no. 4, pp. 894-905, 2021, ISSN: 2329924X, (cited By 2).
@article{Vitoropoulou2021894,
title = {CoveR: An Information Diffusion Aware Approach for Efficient Recommendations under User Coverage Constraints},
author = {M. Vitoropoulou and K. Tsitseklis and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103753269&doi=10.1109%2fTCSS.2021.3067711&partnerID=40&md5=24ae5598764569c3f5f8bd69b762c4fb},
doi = {10.1109/TCSS.2021.3067711},
issn = {2329924X},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {8},
number = {4},
pages = {894-905},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this article, we consider the problem of recommendations to the users of an online social network (OSN), through an information diffusion aware recommender system (IDARS). We map the assignment of recommendations in influence networks to a problem of selecting an $ell $ -cover of the minimum total cost, which is defined to be a set of assignments such that each user in the OSN is recommended of at least $ell $ different items at the minimum defined cost. This corresponds to a special case of the minimum weighted partition set cover problem, which is a generalization of the minimum weighted set cover problem, both of which are proven to be NP-hard. We formulate a corresponding integer programming problem and we apply a linear programming (LP)-based branch and bound (BnB) methodology for its solution. We also propose a greedy algorithm, denoted as CoveR, which we show to be an $O((Delta /delta )cdot H(Delta))$ -approximation for the $ell $ -coverage problem, where $Delta $ and $delta $ are the maximum and minimum degree of an influence network, respectively, and $H(Delta)$ is the $Delta text th$ harmonic number. We investigate CoveR's performance through extensive simulations on both synthetic and real networks, which indicate that the quality of its solution is comparable to the one obtained by the BnB method, while at the same time outperforms other information diffusion-aware recommendation heuristics. © 2014 IEEE.},
note = {cited By 2},
keywords = {},
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}
In this article, we consider the problem of recommendations to the users of an online social network (OSN), through an information diffusion aware recommender system (IDARS). We map the assignment of recommendations in influence networks to a problem of selecting an $ell $ -cover of the minimum total cost, which is defined to be a set of assignments such that each user in the OSN is recommended of at least $ell $ different items at the minimum defined cost. This corresponds to a special case of the minimum weighted partition set cover problem, which is a generalization of the minimum weighted set cover problem, both of which are proven to be NP-hard. We formulate a corresponding integer programming problem and we apply a linear programming (LP)-based branch and bound (BnB) methodology for its solution. We also propose a greedy algorithm, denoted as CoveR, which we show to be an $O((Delta /delta )cdot H(Delta))$ -approximation for the $ell $ -coverage problem, where $Delta $ and $delta $ are the maximum and minimum degree of an influence network, respectively, and $H(Delta)$ is the $Delta text th$ harmonic number. We investigate CoveR's performance through extensive simulations on both synthetic and real networks, which indicate that the quality of its solution is comparable to the one obtained by the BnB method, while at the same time outperforms other information diffusion-aware recommendation heuristics. © 2014 IEEE.
Vlachakis, D.; Papakonstantinou, E.; Sagar, R.; Bacopoulou, F.; Exarchos, T.; Kourouthanassis, P.; Karyotis, V.; Vlamos, P.; Lyketsos, C.; Avramopoulos, D.; Mahairaki, V.
Improving the utility of polygenic risk scores as a biomarker for alzheimer’s disease Journal Article
In: Cells, vol. 10, no. 7, 2021, ISSN: 20734409, (cited By 8).
@article{Vlachakis2021,
title = {Improving the utility of polygenic risk scores as a biomarker for alzheimer’s disease},
author = {D. Vlachakis and E. Papakonstantinou and R. Sagar and F. Bacopoulou and T. Exarchos and P. Kourouthanassis and V. Karyotis and P. Vlamos and C. Lyketsos and D. Avramopoulos and V. Mahairaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110284259&doi=10.3390%2fcells10071627&partnerID=40&md5=c4fc2228d5300c29c3dad75f7d553f2d},
doi = {10.3390/cells10071627},
issn = {20734409},
year = {2021},
date = {2021-01-01},
journal = {Cells},
volume = {10},
number = {7},
publisher = {MDPI},
abstract = {The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
note = {cited By 8},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Tsitseklis, K.; Krommyda, M.; Karyotis, V.; Kantere, V.; Papavassiliou, S.
Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases Journal Article
In: IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1269-1282, 2021, ISSN: 23274697, (cited By 3).
@article{Tsitseklis20211269,
title = {Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases},
author = {K. Tsitseklis and M. Krommyda and V. Karyotis and V. Kantere and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112343198&doi=10.1109%2fTNSE.2020.3022248&partnerID=40&md5=29b1774f2e931454978b20d2dd8d9592},
doi = {10.1109/TNSE.2020.3022248},
issn = {23274697},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Network Science and Engineering},
volume = {8},
number = {2},
pages = {1269-1282},
publisher = {IEEE Computer Society},
abstract = {Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding. © 2013 IEEE.},
note = {cited By 3},
keywords = {},
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}
Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding. © 2013 IEEE.
Tsitseklis, K.; Vitoropoulou, M.; Karyotis, V.; Papavassiliou, S.
Socio-Aware Recommendations under Complex User Constraints Journal Article
In: IEEE Transactions on Computational Social Systems, vol. 8, no. 2, pp. 377-387, 2021, ISSN: 2329924X, (cited By 1).
@article{Tsitseklis2021377,
title = {Socio-Aware Recommendations under Complex User Constraints},
author = {K. Tsitseklis and M. Vitoropoulou and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099574885&doi=10.1109%2fTCSS.2020.3046686&partnerID=40&md5=dd9c5d680b886d8e4fcaf5abba0c7d4e},
doi = {10.1109/TCSS.2020.3046686},
issn = {2329924X},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Computational Social Systems},
volume = {8},
number = {2},
pages = {377-387},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints. © 2014 IEEE.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints. © 2014 IEEE.
Kakkavas, G.; Stamou, A.; Karyotis, V.; Papavassiliou, S.
Network Tomography for Efficient Monitoring in SDN-Enabled 5G Networks and Beyond: Challenges and Opportunities Journal Article
In: IEEE Communications Magazine, vol. 59, no. 3, pp. 70-76, 2021, ISSN: 01636804, (cited By 29).
@article{Kakkavas202170,
title = {Network Tomography for Efficient Monitoring in SDN-Enabled 5G Networks and Beyond: Challenges and Opportunities},
author = {G. Kakkavas and A. Stamou and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105611912&doi=10.1109%2fMCOM.001.2000458&partnerID=40&md5=16953d3d2cba346d5c5b36d400f023c4},
doi = {10.1109/MCOM.001.2000458},
issn = {01636804},
year = {2021},
date = {2021-01-01},
journal = {IEEE Communications Magazine},
volume = {59},
number = {3},
pages = {70-76},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Efficient monitoring plays a vital role in software-defined networking (SDN)-enabled 5G networks, involving the monitoring of performance metrics for both physical and virtual networks and considering the requirements of a variety of traffic types originating from different applications. However, conventional network monitoring tools based on traditional IP-based mechanisms may not be able to keep up with the dynamic nature of 5G networks. Network tomography (NT) is an emerging monitoring approach that estimates network performance based on measurements realized at a limited subset of network elements, presenting benefits over traditional monitoring techniques, but susceptible to identifiability issues. In this article, we demonstrate how NT can respond to current monitoring challenges, complementing and working together with SDN, yielding accurate estimations with low overhead, while exploiting SDN capabilities such as the centralized view of the entire network, direct flow-level measurements, and controllable routing. Furthermore, we present the spectrum of applications of NT-based solutions in 5G networks and beyond, ranging from virtual to vehicular networks. © 1979-2012 IEEE.},
note = {cited By 29},
keywords = {},
pubstate = {published},
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}
Efficient monitoring plays a vital role in software-defined networking (SDN)-enabled 5G networks, involving the monitoring of performance metrics for both physical and virtual networks and considering the requirements of a variety of traffic types originating from different applications. However, conventional network monitoring tools based on traditional IP-based mechanisms may not be able to keep up with the dynamic nature of 5G networks. Network tomography (NT) is an emerging monitoring approach that estimates network performance based on measurements realized at a limited subset of network elements, presenting benefits over traditional monitoring techniques, but susceptible to identifiability issues. In this article, we demonstrate how NT can respond to current monitoring challenges, complementing and working together with SDN, yielding accurate estimations with low overhead, while exploiting SDN capabilities such as the centralized view of the entire network, direct flow-level measurements, and controllable routing. Furthermore, we present the spectrum of applications of NT-based solutions in 5G networks and beyond, ranging from virtual to vehicular networks. © 1979-2012 IEEE.
Bouali, F.; Pinola, J.; Karyotis, V.; Wissingh, B.; Mitrou, M.; Krishnan, P.; Moessner, K.
5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook Journal Article
In: IEEE Open Journal of Intelligent Transportation Systems, vol. 2, pp. 73-96, 2021, ISSN: 26877813, (cited By 11).
@article{Bouali202173,
title = {5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook},
author = {F. Bouali and J. Pinola and V. Karyotis and B. Wissingh and M. Mitrou and P. Krishnan and K. Moessner},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115699611&doi=10.1109%2fOJITS.2021.3072220&partnerID=40&md5=064d6c7ee6054b96672f977e53cabcf6},
doi = {10.1109/OJITS.2021.3072220},
issn = {26877813},
year = {2021},
date = {2021-01-01},
journal = {IEEE Open Journal of Intelligent Transportation Systems},
volume = {2},
pages = {73-96},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed. © 2020 IEEE.},
note = {cited By 11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed. © 2020 IEEE.
Mikalef, P.; Pateli, A.; Wetering, R.
In: European Journal of Information Systems, vol. 30, no. 5, pp. 512-540, 2021, (cited By 86).
@article{Mikalef2021512,
title = {IT architecture flexibility and IT governance decentralisation as drivers of IT-enabled dynamic capabilities and competitive performance: The moderating effect of the external environment},
author = {P. Mikalef and A. Pateli and R. Wetering},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089981454&doi=10.1080%2f0960085X.2020.1808541&partnerID=40&md5=fba4a21706294ae2fb192b96b3a992e1},
doi = {10.1080/0960085X.2020.1808541},
year = {2021},
date = {2021-01-01},
journal = {European Journal of Information Systems},
volume = {30},
number = {5},
pages = {512-540},
note = {cited By 86},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2020
Poulos, M.; Korfiatis, N.; Papavlassopoulos, S.
Assessing stationarity in web analytics: A study of bounce rates Journal Article
In: Expert Systems, vol. 37, no. 3, 2020, (cited By 9).
@article{Poulos2020,
title = {Assessing stationarity in web analytics: A study of bounce rates},
author = {M. Poulos and N. Korfiatis and S. Papavlassopoulos},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077879659&doi=10.1111%2fexsy.12502&partnerID=40&md5=1db347b89928161a721aa98672748c71},
doi = {10.1111/exsy.12502},
year = {2020},
date = {2020-01-01},
journal = {Expert Systems},
volume = {37},
number = {3},
note = {cited By 9},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stamolampros, P.; Korfiatis, N.; Chalvatzis, K.; Buhalis, D.
Harnessing the “wisdom of employees” from online reviews Journal Article
In: Annals of Tourism Research, vol. 80, 2020, (cited By 23).
@article{Stamolampros2020,
title = {Harnessing the “wisdom of employees” from online reviews},
author = {P. Stamolampros and N. Korfiatis and K. Chalvatzis and D. Buhalis},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062302567&doi=10.1016%2fj.annals.2019.02.012&partnerID=40&md5=f56d9e882cf177ceb4d48876ccda6342},
doi = {10.1016/j.annals.2019.02.012},
year = {2020},
date = {2020-01-01},
journal = {Annals of Tourism Research},
volume = {80},
note = {cited By 23},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kakkavas, Grigorios; Gkatzioura, Despoina; Karyotis, Vasileios; Papavassiliou, Symeon
A review of advanced algebraic approaches enabling network tomography for future network infrastructures Journal Article
In: Future Internet, vol. 12, no. 2, pp. 20, 2020.
@article{kakkavas2020review,
title = {A review of advanced algebraic approaches enabling network tomography for future network infrastructures},
author = {Grigorios Kakkavas and Despoina Gkatzioura and Vasileios Karyotis and Symeon Papavassiliou},
year = {2020},
date = {2020-01-01},
journal = {Future Internet},
volume = {12},
number = {2},
pages = {20},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kakkavas, Grigorios; Tsitseklis, Konstantinos; Karyotis, Vasileios; Papavassiliou, Symeon
A software defined radio cross-layer resource allocation approach for cognitive radio networks: From theory to practice Journal Article
In: IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 2, pp. 740–755, 2020.
@article{kakkavas2020software,
title = {A software defined radio cross-layer resource allocation approach for cognitive radio networks: From theory to practice},
author = {Grigorios Kakkavas and Konstantinos Tsitseklis and Vasileios Karyotis and Symeon Papavassiliou},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Cognitive Communications and Networking},
volume = {6},
number = {2},
pages = {740--755},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tsitseklis, Konstantinos; Krommyda, Maria; Karyotis, Vasileios; Kantere, Verena; Papavassiliou, Symeon
Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases Journal Article
In: IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1269–1282, 2020.
@article{tsitseklis2020scalable,
title = {Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases},
author = {Konstantinos Tsitseklis and Maria Krommyda and Vasileios Karyotis and Verena Kantere and Symeon Papavassiliou},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Network Science and Engineering},
volume = {8},
number = {2},
pages = {1269--1282},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stai, Eleni; Karyotis, Vasileios
Optimal resource allocation in multihop wireless networks relying on energy harvesting Journal Article
In: IEEE Communications Letters, vol. 25, no. 1, pp. 224–228, 2020.
@article{stai2020optimal,
title = {Optimal resource allocation in multihop wireless networks relying on energy harvesting},
author = {Eleni Stai and Vasileios Karyotis},
year = {2020},
date = {2020-01-01},
journal = {IEEE Communications Letters},
volume = {25},
number = {1},
pages = {224--228},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}