2017
Karyotis, V.; Stai, E.
Hyperbolic big data analytics for dynamic network management and optimization Book
CRC Press, 2017, ISBN: 9781498784870; 9781498784863, (cited By 1).
Abstract | Links | BibTeX | Tags: Big data; Environmental management; Machinery; Network layers; Network management, Dynamic network management; Efficient managements; Embedding of graphs; Multi-layer network; Network structures; Networking environment; Operational environments; Structural feature, Information management
@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},
keywords = {Big data; Environmental management; Machinery; Network layers; Network management, Dynamic network management; Efficient managements; Embedding of graphs; Multi-layer network; Network structures; Networking environment; Operational environments; Structural feature, Information management},
pubstate = {published},
tppubtype = {book}
}
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.
2016
Stai, E.; Karyotis, V.; Papavassiliou, S.
A hyperbolic space analytics framework for big network data and their applications Journal Article
In: IEEE Network, vol. 30, no. 1, pp. 11-17, 2016, ISSN: 08908044, (cited By 26).
Abstract | Links | BibTeX | Tags: Big data; Data handling; Data reduction; Decision making; Digital storage; Economics; Information analysis; Measurements; Optimization; Problem solving; Resource allocation; Topology, Current practices; Diverse applications; Effective solution; Network economics; Network resource allocations; Network topology; Resource management; Routing, Information management
@article{Stai201611,
title = {A hyperbolic space analytics framework for big network data and their applications},
author = {E. Stai and V. Karyotis and S. Papavassiliou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962018671&doi=10.1109%2fMNET.2016.7389825&partnerID=40&md5=452d87373227f1a5a381b819787d5630},
doi = {10.1109/MNET.2016.7389825},
issn = {08908044},
year = {2016},
date = {2016-01-01},
journal = {IEEE Network},
volume = {30},
number = {1},
pages = {11-17},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Big data analytics have generated a paradigm shift in modern data analysis and decision making in almost every aspect of human society. Nowadays, massive amounts of generated network and correlated (networked) data pose critical computational and storage challenges, requiring the development of radical techniques to manage, process, and analyze them more efficiently. We propose embedding such data and their correlations in hyperbolic metric spaces as one approach aspiring to radically change current practices. In this article, we explore the potential that such data embedding and the corresponding hyperbolic space based data analytics can offer to networks, their applications, and their services. We demonstrate how this approach may lead to more efficient and scalable problem solving within diverse application domains, such as network design/analysis, network resource allocation optimization, and network economics/marketing, paving the way for more diverse and effective solutions in the future. © 2016 IEEE.},
note = {cited By 26},
keywords = {Big data; Data handling; Data reduction; Decision making; Digital storage; Economics; Information analysis; Measurements; Optimization; Problem solving; Resource allocation; Topology, Current practices; Diverse applications; Effective solution; Network economics; Network resource allocations; Network topology; Resource management; Routing, Information management},
pubstate = {published},
tppubtype = {article}
}
Big data analytics have generated a paradigm shift in modern data analysis and decision making in almost every aspect of human society. Nowadays, massive amounts of generated network and correlated (networked) data pose critical computational and storage challenges, requiring the development of radical techniques to manage, process, and analyze them more efficiently. We propose embedding such data and their correlations in hyperbolic metric spaces as one approach aspiring to radically change current practices. In this article, we explore the potential that such data embedding and the corresponding hyperbolic space based data analytics can offer to networks, their applications, and their services. We demonstrate how this approach may lead to more efficient and scalable problem solving within diverse application domains, such as network design/analysis, network resource allocation optimization, and network economics/marketing, paving the way for more diverse and effective solutions in the future. © 2016 IEEE.