Evaluation of data mining tools for telecommunication monitoring data using design of experiment

Samneet Singh; Yan Liu; Wayne Ding; Zheng Li

doi:10.1109/BigDataCongress.2016.43

Evaluation of data mining tools for telecommunication monitoring data using design of experiment

Samneet Singh, Yan Liu, Wayne Ding, Zheng Li

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

Telecommunication monitoring data requires the automation of data analysis workflows. A data mining tool provides data workflow management systems to process and perform analysis tasks. This paper presents an evaluation of two example data mining tools following the principles of design of experiment (DOE) to run forecasting and clustering workflows for telecom monitoring data. We conduct both quantitative and qualitative evaluation on datasets collected from a trial mobile network. The datasets consist of 1 month, six months, one year and two years of time frames that provide the average number of connected users per cell on base stations. The observations from this evaluation provide insights of each data mining tool in the context of data analysis workflows. This documented design of experiment will further facilitate replicating this evaluation study and evaluate other data mining tools.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	283-290
Number of pages	8
ISBN (Electronic)	9781509026227
DOIs	https://doi.org/10.1109/BigDataCongress.2016.43
Publication status	Published - 2016 Oct 5
Event	5th IEEE International Congress on Big Data, BigData Congress 2016 - San Francisco, United States Duration: 2016 Jun 27 → 2016 Jul 2

Conference

Conference	5th IEEE International Congress on Big Data, BigData Congress 2016
Country/Territory	United States
City	San Francisco
Period	2016/06/27 → 2016/07/02

Subject classification (UKÄ)

Other Computer and Information Science

Free keywords

Big data
Data mining workflow
Empirical evaluation
Telecom service

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10.1109/BigDataCongress.2016.43

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Singh, S., Liu, Y., Ding, W., & Li, Z. (2016). Evaluation of data mining tools for telecommunication monitoring data using design of experiment. In Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016 (pp. 283-290). Article 7584949 IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataCongress.2016.43

@inproceedings{986ad26be6334b998613075bbca2aa1a,

title = "Evaluation of data mining tools for telecommunication monitoring data using design of experiment",

abstract = "Telecommunication monitoring data requires the automation of data analysis workflows. A data mining tool provides data workflow management systems to process and perform analysis tasks. This paper presents an evaluation of two example data mining tools following the principles of design of experiment (DOE) to run forecasting and clustering workflows for telecom monitoring data. We conduct both quantitative and qualitative evaluation on datasets collected from a trial mobile network. The datasets consist of 1 month, six months, one year and two years of time frames that provide the average number of connected users per cell on base stations. The observations from this evaluation provide insights of each data mining tool in the context of data analysis workflows. This documented design of experiment will further facilitate replicating this evaluation study and evaluate other data mining tools.",

keywords = "Big data, Data mining workflow, Empirical evaluation, Telecom service",

author = "Samneet Singh and Yan Liu and Wayne Ding and Zheng Li",

year = "2016",

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doi = "10.1109/BigDataCongress.2016.43",

language = "English",

pages = "283--290",

booktitle = "Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016",

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

address = "United States",

note = "5th IEEE International Congress on Big Data, BigData Congress 2016 ; Conference date: 27-06-2016 Through 02-07-2016",

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Singh, S, Liu, Y, Ding, W & Li, Z 2016, Evaluation of data mining tools for telecommunication monitoring data using design of experiment. in Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016., 7584949, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 283-290, 5th IEEE International Congress on Big Data, BigData Congress 2016, San Francisco, United States, 2016/06/27. https://doi.org/10.1109/BigDataCongress.2016.43

Evaluation of data mining tools for telecommunication monitoring data using design of experiment. / Singh, Samneet; Liu, Yan; Ding, Wayne et al.
Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016. IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. p. 283-290 7584949.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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AB - Telecommunication monitoring data requires the automation of data analysis workflows. A data mining tool provides data workflow management systems to process and perform analysis tasks. This paper presents an evaluation of two example data mining tools following the principles of design of experiment (DOE) to run forecasting and clustering workflows for telecom monitoring data. We conduct both quantitative and qualitative evaluation on datasets collected from a trial mobile network. The datasets consist of 1 month, six months, one year and two years of time frames that provide the average number of connected users per cell on base stations. The observations from this evaluation provide insights of each data mining tool in the context of data analysis workflows. This documented design of experiment will further facilitate replicating this evaluation study and evaluate other data mining tools.

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Evaluation of data mining tools for telecommunication monitoring data using design of experiment

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