Paper Info
Title
Predicting Students' Behavioral Patterns in University Networks for Efficient Bandwidth Allocation: A Hybrid Data Mining Method (Application Paper).
Abstract
The effective bandwidth management in multi-service computer networks such as university networks has become a challenge in recent years. The growth of internet traffic and limitation of bandwidth resources persuade the information technology (IT) managers to focus on effective bandwidth allocation policies. One of the important issues discussed in this domain is how to assign the bandwidth fairly based on the priority levels. In this paper, focusing on the priority-based bandwidth allocation, a hybrid data mining method is developed to manage the limited bandwidth in a university network more effectively. This method is composed of two main steps and uses the clustering and classification techniques. The main purpose is to detect, analyze and predict studentsu0027 behavioral patterns in a university network and identify the main factors that affect their tendency in using internet. The proposed method is applied on a real data of a network university. The results indicate that degree level and age are the most important factors that influence studentsu0027 tendency to use internet. The results would be also useful for prediction purposes. It helps the IT managers to predict a new studentu0027s tendency to use internet given his/her characteristics. By analyzing the results, the IT managers can make better decisions to optimize the allocation of bandwidth resources.
Year
Venue
Field
2016
IRI
Data mining,Bandwidth allocation,Computer science,Bandwidth (signal processing),Artificial intelligence,Dynamic bandwidth allocation,Network traffic control,Channel allocation schemes,Machine learning,Internet traffic,Bandwidth management,The Internet
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Elham Akhond Zadeh Noughabi100.68
Behrouz H. Far220740.15
Bijan Raahemi315522.29