2014 LargeScalePredictiveModelingfor

• (Radosavljevik & van der Putten, 2014) ⇒ Dejan Radosavljevik, and Peter van der Putten. (2014). “Large Scale Predictive Modeling for Micro-simulation of 3G Air Interface Load.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623339

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Large+Scale+Predictive+Modeling+for+Micro-simulation+of+3G+Air+Interface+Load
• http://dl.acm.org/citation.cfm?id=2623330.2623339&preflayout=flat#citedby

Quotes

Author Keywords

• Air-interface load; data mining; linear regression; mobile network; simulation

Abstract

This paper outlines the approach developed together with the Radio Network Strategy & Design Department of a large European telecom operator in order to forecast the Air-Interface load in their 3G network, which is used for planning network upgrades and budgeting purposes. It is based on large scale intelligent data analysis and modeling at the level of thousands of individual radio cells resulting in 30,000 models per day. It has been embedded into a scenario simulation framework that is used by end users not experienced in data mining for studying and simulating the behavior of this complex networked system, as an example of a systematic approach to the deployment step in the KDD process. This system is already in use for two years in the country where it was developed and it is a part of a standard business process. In the last six months this national operator became a competence center for predictive modeling for micro-simulation of 3G air interface load for four other operators of the same parent company.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 LargeScalePredictiveModelingfor	Dejan Radosavljevik Peter van der Putten			Large Scale Predictive Modeling for Micro-simulation of 3G Air Interface Load				10.1145/2623330.2623339		2014