Evaluation of data mining tools for telecommunication monitoring data using design of experiment
(2016) 5th IEEE International Congress on Big Data, BigData Congress 2016 p.283-290- 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... (More)
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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- author
- Singh, Samneet ; Liu, Yan LU ; Ding, Wayne and Li, Zheng LU
- organization
- publishing date
- 2016-10-05
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Big data, Data mining workflow, Empirical evaluation, Telecom service
- host publication
- Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016
- article number
- 7584949
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 5th IEEE International Congress on Big Data, BigData Congress 2016
- conference location
- San Francisco, United States
- conference dates
- 2016-06-27 - 2016-07-02
- external identifiers
-
- scopus:84994613807
- wos:000390212200036
- ISBN
- 9781509026227
- DOI
- 10.1109/BigDataCongress.2016.43
- language
- English
- LU publication?
- yes
- id
- 986ad26b-e633-4b99-8613-075bbca2aa1a
- date added to LUP
- 2016-12-07 12:13:32
- date last changed
- 2025-01-12 17:00:39
@inproceedings{986ad26b-e633-4b99-8613-075bbca2aa1a, abstract = {{<p>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.</p>}}, author = {{Singh, Samneet and Liu, Yan and Ding, Wayne and Li, Zheng}}, booktitle = {{Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016}}, isbn = {{9781509026227}}, keywords = {{Big data; Data mining workflow; Empirical evaluation; Telecom service}}, language = {{eng}}, month = {{10}}, pages = {{283--290}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Evaluation of data mining tools for telecommunication monitoring data using design of experiment}}, url = {{http://dx.doi.org/10.1109/BigDataCongress.2016.43}}, doi = {{10.1109/BigDataCongress.2016.43}}, year = {{2016}}, }