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Feature interactions detection in intelligent networks

Title:

Feature interactions detection in intelligent networks

Sefidcon, Azimeh (1999) Feature interactions detection in intelligent networks. Masters thesis, Concordia University.

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Abstract

Intelligent Networks (IN) have been introduced for rapid development and deployment of new services. However, these new services may interact with old ones in a negative and unexpected manner. This is known as feature interaction (FI) problem. In this thesis, a new pragmatic approach for feature interaction detection is proposed. A pragmatic method for FI detection is based on the gained experience from the study of the known FIs. In this approach, the causes of FIs given in Bellcore and European benchmarks serve as the starting point. The sufficient information for feature description is derived from these causes. This information is modelled using an object-oriented template. Features are described in terms of necessary resources and actions. Feature participants call model, their triggering constraints and operations describe the behaviour of the feature. The detection method takes these models as input and checks for interactions between features. The method consists of three parts: filtering algorithm, feature instantiation and detection algorithm. Using the filtering algorithm, all the possible interaction-prone scenarios are produced and the actual features are instantiated using the list of topologically different call combinations. The detection algorithm is run on these actual participants of the features in order to detect potential interactions. The approach has been implemented as a tool and applied successfully to the existing feature interaction benchmarks.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Sefidcon, Azimeh
Pagination:xv, 149 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.A.Sc.)
Program:Electrical and Computer Engineering
Date:1999
Thesis Supervisor(s):Khendek, Ferhat
ID Code:987
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:15
Last Modified:08 Dec 2010 10:17
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