TY - GEN
T1 - Semantics management for big networks
AU - Mokhtar, Bassem
AU - Eltoweissy, Mohamed
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/2/27
Y1 - 2014/2/27
N2 - We define 'Big Networks' as those that generate big data and can benefit from big data management in their operations. Examples of big networks include the emerging Internet of things and social networks. A major challenge in big networks is storing, processing and accessing massive multidimensional data to extract useful information for more efficient and smarter networking operations. Dimension reduction, learning patterns and extracting semantics from big data would help in mitigating such challenge. We have proposes a network 'memory' system, termed NetMem, with storage and recollection mechanisms to access and manage data semantics in the Internet. NetMem is inspired by functionalities of human memory for learning patterns from huge amounts of data. In this paper we refine NetMem design and explore hidden Markov models, latent dirichlet allocation, and simple statistical analysis-based techniques for semantic reasoning in NetMem. In addition, we utilize locality sensitive hashing for reducing dimensionality. Our simulation study demonstrates the benefits of NetMem and highlights the advantages and limitations of the aforementioned techniques both with and without dimensionality reduction.
AB - We define 'Big Networks' as those that generate big data and can benefit from big data management in their operations. Examples of big networks include the emerging Internet of things and social networks. A major challenge in big networks is storing, processing and accessing massive multidimensional data to extract useful information for more efficient and smarter networking operations. Dimension reduction, learning patterns and extracting semantics from big data would help in mitigating such challenge. We have proposes a network 'memory' system, termed NetMem, with storage and recollection mechanisms to access and manage data semantics in the Internet. NetMem is inspired by functionalities of human memory for learning patterns from huge amounts of data. In this paper we refine NetMem design and explore hidden Markov models, latent dirichlet allocation, and simple statistical analysis-based techniques for semantic reasoning in NetMem. In addition, we utilize locality sensitive hashing for reducing dimensionality. Our simulation study demonstrates the benefits of NetMem and highlights the advantages and limitations of the aforementioned techniques both with and without dimensionality reduction.
KW - Big data
KW - Bio-inspired design
KW - Network management
KW - Network semantics
KW - Semantic reasoning
UR - http://www.scopus.com/inward/record.url?scp=84946693059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946693059&partnerID=8YFLogxK
U2 - 10.1109/IRI.2014.7051885
DO - 10.1109/IRI.2014.7051885
M3 - Conference contribution
AN - SCOPUS:84946693059
T3 - Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014
SP - 155
EP - 162
BT - Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014
A2 - Bertino, Elisa
A2 - Thuraisingham, Bhavani
A2 - Liu, Ling
A2 - Joshi, James
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Information Reuse and Integration, IEEE IRI 2014
Y2 - 13 August 2014 through 15 August 2014
ER -