A data fusion framework for large-scale measurement platforms

Prapa Rattadilok; John McCall; Trevor Burbridge; Andrea Soppera; Philip Eardley

doi:10.1109/BigData.2015.7364000

A data fusion framework for large-scale measurement platforms

Prapa Rattadilok, John McCall, Trevor Burbridge, Andrea Soppera, Philip Eardley

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The need to assess internet performance from the user's perspective grows, as does the interest in deployment of Large-Scale Measurement Platforms (LMAPs). The potential of these platforms as a real-time network diagnostic tool is limited by the volume, velocity and variety of the data they generated. Fusing this data from multiple sources and generating a single piece of coherent information about the state of the network would increase the efficiency of network monitoring. The current practice of visually analysing LMAPs' data stream would certainly benefit from having automatically generated notifications in a timely manner alerting human controllers to the network's conditions of interest. This paper proposed a data fusion framework for LMAPs that makes use of mathematical distribution based sensors to generate probabilistic sensor outputs which are fused using a Dempster-Shafer Theory.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
Editors	Feng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2150-2158
Number of pages	9
ISBN (Electronic)	9781479999255
DOIs	https://doi.org/10.1109/BigData.2015.7364000
Publication status	Published - 22 Dec 2015
Externally published	Yes
Event	3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States Duration: 29 Oct 2015 → 1 Nov 2015

Publication series

Name	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Conference

Conference	3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/Territory	United States
City	Santa Clara
Period	29/10/15 → 1/11/15

Keywords

active measurements
data fusion
large-scale measurement platform
sensors

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Software

Access to Document

10.1109/BigData.2015.7364000

Cite this

Rattadilok, P., McCall, J., Burbridge, T., Soppera, A., & Eardley, P. (2015). A data fusion framework for large-scale measurement platforms. In F. Luo, K. Ogan, M. J. Zaki, L. Haas, B. C. Ooi, V. Kumar, S. Rachuri, S. Pyne, H. Ho, X. Hu, S. Yu, M. H.-I. Hsiao, & J. Li (Eds.), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2150-2158). Article 7364000 (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7364000

Rattadilok, Prapa ; McCall, John ; Burbridge, Trevor et al. / A data fusion framework for large-scale measurement platforms. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. editor / Feng Luo ; Kemafor Ogan ; Mohammed J. Zaki ; Laura Haas ; Beng Chin Ooi ; Vipin Kumar ; Sudarsan Rachuri ; Saumyadipta Pyne ; Howard Ho ; Xiaohua Hu ; Shipeng Yu ; Morris Hui-I Hsiao ; Jian Li. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2150-2158 (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015).

@inproceedings{3592e0f1259c4b93854d4ef27077899d,

title = "A data fusion framework for large-scale measurement platforms",

abstract = "The need to assess internet performance from the user's perspective grows, as does the interest in deployment of Large-Scale Measurement Platforms (LMAPs). The potential of these platforms as a real-time network diagnostic tool is limited by the volume, velocity and variety of the data they generated. Fusing this data from multiple sources and generating a single piece of coherent information about the state of the network would increase the efficiency of network monitoring. The current practice of visually analysing LMAPs' data stream would certainly benefit from having automatically generated notifications in a timely manner alerting human controllers to the network's conditions of interest. This paper proposed a data fusion framework for LMAPs that makes use of mathematical distribution based sensors to generate probabilistic sensor outputs which are fused using a Dempster-Shafer Theory.",

keywords = "active measurements, data fusion, large-scale measurement platform, sensors",

author = "Prapa Rattadilok and John McCall and Trevor Burbridge and Andrea Soppera and Philip Eardley",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 ; Conference date: 29-10-2015 Through 01-11-2015",

year = "2015",

month = dec,

day = "22",

doi = "10.1109/BigData.2015.7364000",

language = "English",

series = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2150--2158",

editor = "Feng Luo and Kemafor Ogan and Zaki, {Mohammed J.} and Laura Haas and Ooi, {Beng Chin} and Vipin Kumar and Sudarsan Rachuri and Saumyadipta Pyne and Howard Ho and Xiaohua Hu and Shipeng Yu and Hsiao, {Morris Hui-I} and Jian Li",

booktitle = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",

address = "United States",

}

Rattadilok, P, McCall, J, Burbridge, T, Soppera, A & Eardley, P 2015, A data fusion framework for large-scale measurement platforms. in F Luo, K Ogan, MJ Zaki, L Haas, BC Ooi, V Kumar, S Rachuri, S Pyne, H Ho, X Hu, S Yu, MH-I Hsiao & J Li (eds), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015., 7364000, Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, Institute of Electrical and Electronics Engineers Inc., pp. 2150-2158, 3rd IEEE International Conference on Big Data, IEEE Big Data 2015, Santa Clara, United States, 29/10/15. https://doi.org/10.1109/BigData.2015.7364000

A data fusion framework for large-scale measurement platforms. / Rattadilok, Prapa; McCall, John; Burbridge, Trevor et al.
Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. ed. / Feng Luo; Kemafor Ogan; Mohammed J. Zaki; Laura Haas; Beng Chin Ooi; Vipin Kumar; Sudarsan Rachuri; Saumyadipta Pyne; Howard Ho; Xiaohua Hu; Shipeng Yu; Morris Hui-I Hsiao; Jian Li. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2150-2158 7364000 (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A data fusion framework for large-scale measurement platforms

AU - Rattadilok, Prapa

AU - McCall, John

AU - Burbridge, Trevor

AU - Soppera, Andrea

AU - Eardley, Philip

PY - 2015/12/22

Y1 - 2015/12/22

N2 - The need to assess internet performance from the user's perspective grows, as does the interest in deployment of Large-Scale Measurement Platforms (LMAPs). The potential of these platforms as a real-time network diagnostic tool is limited by the volume, velocity and variety of the data they generated. Fusing this data from multiple sources and generating a single piece of coherent information about the state of the network would increase the efficiency of network monitoring. The current practice of visually analysing LMAPs' data stream would certainly benefit from having automatically generated notifications in a timely manner alerting human controllers to the network's conditions of interest. This paper proposed a data fusion framework for LMAPs that makes use of mathematical distribution based sensors to generate probabilistic sensor outputs which are fused using a Dempster-Shafer Theory.

AB - The need to assess internet performance from the user's perspective grows, as does the interest in deployment of Large-Scale Measurement Platforms (LMAPs). The potential of these platforms as a real-time network diagnostic tool is limited by the volume, velocity and variety of the data they generated. Fusing this data from multiple sources and generating a single piece of coherent information about the state of the network would increase the efficiency of network monitoring. The current practice of visually analysing LMAPs' data stream would certainly benefit from having automatically generated notifications in a timely manner alerting human controllers to the network's conditions of interest. This paper proposed a data fusion framework for LMAPs that makes use of mathematical distribution based sensors to generate probabilistic sensor outputs which are fused using a Dempster-Shafer Theory.

KW - active measurements

KW - data fusion

KW - large-scale measurement platform

KW - sensors

UR - http://www.scopus.com/inward/record.url?scp=84963772470&partnerID=8YFLogxK

U2 - 10.1109/BigData.2015.7364000

DO - 10.1109/BigData.2015.7364000

M3 - Conference contribution

AN - SCOPUS:84963772470

T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

SP - 2150

EP - 2158

BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

A2 - Luo, Feng

A2 - Ogan, Kemafor

A2 - Zaki, Mohammed J.

A2 - Haas, Laura

A2 - Ooi, Beng Chin

A2 - Kumar, Vipin

A2 - Rachuri, Sudarsan

A2 - Pyne, Saumyadipta

A2 - Ho, Howard

A2 - Hu, Xiaohua

A2 - Yu, Shipeng

A2 - Hsiao, Morris Hui-I

A2 - Li, Jian

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015

Y2 - 29 October 2015 through 1 November 2015

ER -

Rattadilok P, McCall J, Burbridge T, Soppera A, Eardley P. A data fusion framework for large-scale measurement platforms. In Luo F, Ogan K, Zaki MJ, Haas L, Ooi BC, Kumar V, Rachuri S, Pyne S, Ho H, Hu X, Yu S, Hsiao MHI, Li J, editors, Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2150-2158. 7364000. (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015). doi: 10.1109/BigData.2015.7364000

A data fusion framework for large-scale measurement platforms

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this