Methodology for System Adaptation based on Characteristic Patterns

This paper describes the methodology for system description and application so that the system can be managed using real time system adaptation. Here, we propose approaches relying on machine learning methods, which would enable to characterize key patterns and detect them in real time and in their altered form as well. Then, based on the pattern recognized, it is possible to apply a suitable intervention to system inputs so that the system responds in the desired way. Our aim is to develop and apply a hybrid approach based on machine learning methods, particularly based on soft-computing methods to identify patterns successfully and for the subsequent adaptation of the system. The main goal of the paper is to recognize important pattern and adapt the system?s behaviour based on the pattern desired way.P(ED2.1.00/03.0089), P(GA102/09/1680), S, Z(MSM7088352101

Use Case Maps for Engineering Real Time and Distributed Computer . . .

Two major problems in the engineering of software -intensive, real time and distributed computer systems are, without becoming lost in code details, I) understanding how an implemented system works as a whole, and II) specifying, before implementation starts, how the required behaviour of the whole system is to be achieved. These lead to other problems, such as, long iteration cycles during forward engineering while various code changes are tried in attempts to fix erroneous system behaviour, and inadvertently introducing code changes during maintenance or reengineering that will damage correct system behaviour because there is a lack of backwards traceability to it from the code. This paper illustrates the application of a new technique called use case maps to solving these problems, using as an example a system constructed from a publicdomain, object-oriented, software framework called ACE. 1. Introduction Problems I and II identified in the abstract are common to all kinds of soft..