(2016). A hyperbolic space analytics framework for big network data and their applications. IEEE Network, 30(1), 11–17. https://doi.org/10.1109/MNET.2016.7389825
Περίληψη
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.
- DOI
- 10.1109/MNET.2016.7389825
- Τύπος
- Άρθρο σε Περιοδικό
- Έτος
- 2016
Σύνδεσμοι
BibTeX
@article{stai2016hyperbolic,
title = {A hyperbolic space analytics framework for big network data and their applications},
author = {Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},
url = {https://doi.org/10.1109/MNET.2016.7389825},
doi = {10.1109/MNET.2016.7389825},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {IEEE Network},
volume = {30},
number = {1},
pages = {11--17},
publisher = {IEEE},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}