Βιβλία και Συλλογικοί Τόμοι

Karyotis, V., Vitoropoulou, M., Kalatzis, N., Roussaki, I., & Papavassiliou, S. (2019). Efficient and socio-aware recommendation approaches for bigdata networked systems. Institution of Engineering and Technology. https://doi.org/10.1049/PBPC035F_ch4

@book{Karyotis201941,

title = {Efficient and socio-aware recommendation approaches for bigdata networked systems},

author = {V. Karyotis and M. Vitoropoulou and N. Kalatzis and I. Roussaki and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099450309&doi=10.1049%2fPBPC035F_ch4&partnerID=40&md5=ae7b353bd55c8bdfc06d9fd8ea9177c1},

doi = {10.1049/PBPC035F_ch4},

isbn = {9781785619755},

year  = {2019},

date = {2019-01-01},

journal = {Big Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust},

pages = {41-70},

publisher = {Institution of Engineering and Technology},

abstract = {In this chapter, we present several approaches designed for providing efficient recommendations in large web systems characterized by bigdata scales. The key feature of the considered approaches is that they all rely on different elements and properties of social/complex network analysis for addressing various deficiencies of legacy and current recommendation systems when very large operational scales emerge. The main challenges of recommendations addressed by the presented approaches are the diversity (novelty) of recommendations, the cold-start problem, scalability and noise filtering issues, as well as the efficiency of developing these approaches and integrating them in operational systems. This chapter aspires to provide an educated overview, leading to a solid fundamental background on how social/complex network analysis can be exploited for more effective recommendations in stringent environments characterized by large scales ofusers, items and associated data, cumulatively referred to as big network data. Furthermore, our work aims at highlighting the design principles that are more interesting for enabling the extension of the presented approaches and their combination with other current state-of-the-art techniques, thus leading to more socioaware and efficient recommendation approaches in the near and longer term future. © The Institution of Engineering and Technology 2020.},

note = {cited By 5},

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}

Karyotis, V., & Stai, E. (2017). Hyperbolic big data analytics for dynamic network management and optimization. CRC Press. https://doi.org/10.1201/b21278

@book{Karyotis2017177,

title = {Hyperbolic big data analytics for dynamic network management and optimization},

author = {V. Karyotis and E. Stai},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052714130&doi=10.1201%2fb21278&partnerID=40&md5=538cccccfa15d20aa1230ee5bb1891df},

doi = {10.1201/b21278},

isbn = {9781498784870; 9781498784863},

year  = {2017},

date = {2017-01-01},

journal = {Big Data and Computational Intelligence in Networking},

pages = {177-207},

publisher = {CRC Press},

abstract = {178Massive numbers of devices, growing user populations and voluminous amounts of produced/exchanged information are expected in the complex cyber-physical networks of the future. The new scales of operation give rise to a big network data era, where multi-layer networks form and users become content producers/consumers (prosumers). The challenges associated with the forthcoming networking environments require radical rethinking of current network analysis, management and operation practices. This chapter will focus on this effort, and more specifically on reinventing the machinery for computing key network metrics that allow improving network management/operation, while also developing more efficient overlay applications within demanding operational environments. Special attention is given to the impact of network evolution on the computation of such metrics, an aspect relatively neglected until recently. In order to efficiently compute key metrics associated with social or structural features of the network and track them when the infrastructure evolves, a big data analytics methodology, denoted as Hyperbolic Data Analytics (HDA), is applied. HDA is based on the embedding of graphs in the hyperbolic space, leading to more efficient management/operation, typically by exploiting hidden network structure. HDA is a characteristic example of developing computational intelligence over big network data and exploiting them for improving/optimizing both infrastructures and applications/services. HDA will provide the means for computing efficiently network analysis metrics, the evolution of which indicates the evolution of the corresponding network’s structure. © 2018 by Taylor and Francis Group, LLC.},

note = {cited By 1},

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Ivanov, T., Izberovic, S., & Korfiatis, N. (2016). The heterogeneity paradigm in big data architectures. https://doi.org/10.4018/978-1-5225-1759-7.ch021

Stai, E., Karyotis, V., Katsinis, G., Tsiropoulou, E. E., & Papavassiliou, S. (2016). A hyperbolic big data analytics framework within complex and social networks. CRC Press. https://doi.org/10.1201/9781315396705

Stai, E., Karyotis, V., & Papavassiliou, S. (2016). Modeling and control of user interest dictated information dissemination in communications and social networks. Nova Science Publishers, Inc..

@book{Stai201645,

title = {Modeling and control of user interest dictated information dissemination in communications and social networks},

author = {E. Stai and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156108074&partnerID=40&md5=283dfb537c58ae4ad64224391ac08e60},

isbn = {9781634852432; 9781634852258},

year  = {2016},

date = {2016-01-01},

journal = {Advances in Social Networking Research},

pages = {45-88},

publisher = {Nova Science Publishers, Inc.},

abstract = {Information dissemination is one of the most critical processes in modern societies and one of the most important reasons for the proliferation of communications networks, especially of online social networks that have enabled global information exchange at rapid scales. This chapter will focus on information dissemination models that compared to the current literature, they specifically take into account aspects related to user interest and its temporal variability in the dynamics of information spreading. We focus on a dissemination framework regarding solely useful information, where users of communications and online social networks are willing to accept information and potentially further propagate it to their uninformed peers, depending on the interest each user has on specific topics. This is a novel perspective to consider information dissemination under inhomogeneous mixing, since the time-varying user interests affect significantly the outcome and the dynamics of information propagation and have an impact on its control process. Such models can be employed for further improving information dissemination analysis and control methodologies, and they can be exploited in various application domains, e.g., from infrastructure design and management, to marketing and advertising improvement. This chapter addresses both the modeling of information dissemination, especially via epidemics and evolutionary game theoretic techniques, as well as their control via optimal control frameworks and dual optimization techniques. © 2016 Nova Science Publishers, Inc. All rights reserved.},

note = {cited By 0},

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pubstate = {published},

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}

Karyotis, V., & Khouzani, M. H. R. (2016). Malware Diffusion Models for Modern Complex Networks: Theory and Applications. Elsevier Inc.. https://doi.org/10.1016/C2014-0-02168-5

@book{Karyotis20161,

title = {Malware Diffusion Models for Modern Complex Networks: Theory and Applications},

author = {V. Karyotis and M. H. R. Khouzani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966747178&doi=10.1016%2fC2014-0-02168-5&partnerID=40&md5=06baba5b752e60adbf37584ad2514605},

doi = {10.1016/C2014-0-02168-5},

isbn = {9780128027141},

year  = {2016},

date = {2016-01-01},

journal = {Malware Diffusion Models for Modern Complex Networks: Theory and Applications},

pages = {1-296},

publisher = {Elsevier Inc.},

abstract = {Malware Diffusion Models for Wireless Complex Networks: Theory and Applications provides a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. As the proliferation of portable devices, namely smartphones and tablets, and their increased capabilities, has propelled the intensity of malware spreading and increased its consequences in social life and the global economy, this book provides the theoretical aspect of malware dissemination, also presenting modeling approaches that describe the behavior and dynamics of malware diffusion in various types of wireless complex networks. Sections include a systematic introduction to malware diffusion processes in computer and communications networks, an analysis of the latest state-of-the-art malware diffusion modeling frameworks, such as queuing-based techniques, calculus of variations based techniques, and game theory based techniques, also demonstrating how the methodologies can be used for modeling in more general applications and practical scenarios. Presents a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. Systematically introduces malware diffusion processes, providing the relevant mathematical background. Discusses malware modeling frameworks and how to apply them to complex wireless networks. Provides guidelines and directions for extending the corresponding theories in other application domains, demonstrating such possibility by using application models in information dissemination scenarios. © 2016 Elsevier Inc. All rights reserved.},

note = {cited By 34},

keywords = {},

pubstate = {published},

tppubtype = {book}

}

Karyotis, V., Stai, E., & Papavassiliou, S. (2013). Evolutionary dynamics of complex communications networks. CRC Press.

@book{Karyotis20131,

title = {Evolutionary dynamics of complex communications networks},

author = {V. Karyotis and E. Stai and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904483613&partnerID=40&md5=62e7c97f5554f50887c7864862c82d57},

isbn = {9781466518414; 9781466518407},

year  = {2013},

date = {2013-01-01},

journal = {Evolutionary Dynamics of Complex Communications Networks},

pages = {1-277},

publisher = {CRC Press},

abstract = {Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to that observed in natural processes. This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student. The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists. © 2014 by Taylor & Francis Group, LLC.},

note = {cited By 33},

keywords = {},

pubstate = {published},

tppubtype = {book}

}

Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to that observed in natural processes. This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student. The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists. © 2014 by Taylor & Francis Group, LLC.

Karyotis, V., & Papavassiliou, S. (2010). Mobility-induced capacity-delay tradeoffs in wireless multihop networks. Nova Science Publishers, Inc..

@book{Karyotis2010137,

title = {Mobility-induced capacity-delay tradeoffs in wireless multihop networks},

author = {V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896200241&partnerID=40&md5=1bccb2aa11aaa0c9d16fc2153b0d90e1},

isbn = {9781608761869},

year  = {2010},

date = {2010-01-01},

journal = {Cluster Computing and Multi-Hop Network Research},

pages = {137-162},

publisher = {Nova Science Publishers, Inc.},

abstract = {Wireless mobile multihop networks, such as ad hoc and sensor networks, have become rather popular in realizing seamless distributed computation and accommodating autonomic behavior. In these networks, the lack of central infrastructure, the spatio-temporal dynamic nature of wireless communications along with their mobilityinduced dynamic topology, pose additional challenges on the traffic-carrying capacity of the system and raise concerns on the delay performance of packet delivery mechanisms. Despite such impediments of the wireless multihop environment, it has been recently shown that mobility can be exploited by various relay techniques for increasing the capacity of such networks. However, the throughput-effectiveness of the mobilityexploiting relay methods takes place at the cost of a greater number of intermediate hops traversed by packets traveling in the network in order to reach their final destination and additional buffering required at the intermediate nodes, revealing an inherent tradeoff between capacity and delay in mobile multihop networks. In this chapter, we first briefly review various mobility models, and then focus on the inherent mobilityinduced tradeoff between capacity and delay in multihop networks. Mobility is treated as both a constructive (increasing capacity), as well as a destructive (increasing delay) factor in wireless infrastructureless networks. Formal problem settings are presented, followed by applications and protocols that could be used in actual networks for increasing performance and realizing viable networking services. We also provide a qualitative and quantitative comparison among the various protocols, from which directions for future research studies are indicated. © 2010 by Nova Science Publishers, Inc. All rights reserved.},

note = {cited By 0},

keywords = {},

pubstate = {published},

tppubtype = {book}

}

Karyotis, V., & Papavassiliou, S. (2009). Topology Control in Cooperative Wireless Ad Hoc Networks. CRC Press. https://doi.org/10.1201/9781420064704-13

Pateli, A., & Giaglis, G. (2007). An interdisciplinary research framework to investigate electronic business models. https://doi.org/10.1142/9789812707628_0007

Korfiatis, N., Sicilia, M. A., Hess, C., Stein, K., & Schlieder, C. (2007). Social network models for enhancing reference-based search engine rankings. https://doi.org/10.4018/978-1-59904-543-6.ch006

Book Sections

Karyotis, V., & Stai, E. (2017). Hyperbolic Big Data Analytics for Dynamic Network Management and Optimization. In Big Data and Computational Intelligence in Networking (pp. 177–208). CRC Press.

Kourouthanassis, P. E., & Giaglis, G. M. (2016). The design challenge of pervasive information systems. In Pervasive information systems (pp. 29–85). Routledge.

Kourouthanassis, P. E., & Giaglis, G. M. (2016). Toward pervasiveness: Four eras of information systems development. In Pervasive Information Systems (pp. 19–42). Routledge.

Kourouthanassis, P. E., & Giaglis, G. M. (2016). Pervasive Electronic Services in Health Care Ilias Maglogiannis and Stathes Hadjiefthymiades. In Pervasive Information Systems (pp. 196–210). Routledge.

Stai, E., Karyotis, V., Katsinis, G., Tsiropoulou, E. E., & Papavassiliou, S. (2016). Hyperbolic Big Data Analytics within Complex and Social Networks. In Big Data in Complex and Social Networks (pp. 13–46). Chapman and Hall/CRC.

Kourouthanassis, P. E., Mikalef, P., Ioannidou, M., & Pateli, A. (2014). Exploring the online satisfaction gap of medical doctors: an expectation-confirmation investigation of information needs. In Genedis 2014: Computational Biology and Bioinformatics (pp. 217–228). Springer International Publishing Cham.

Karyotis, V., Anifantis, E., & Papavassiliou, S. (2014). Cross-layer based resource management frameworks for mobile cognitive radio networks. In Resource management in mobile computing environments (pp. 285–322). Springer, Cham.

Karaiskos, D. C., & Kourouthanassis, P. E. (2008). Determinants of User Acceptance for RFID Ticketing Systems. In Advances in Ubiquitous Computing: Future Paradigms and Directions (pp. 150–170). IGI Global.

Giaglis, G. M., Kourouthanassis, P., & Tsamakos, A. (2003). Towards a classification framework for mobile location based services. In Mobile commerce: technology, theory and applications (pp. 67–85). IGI Global.