

Survey: Autonomous Construction of Web Services
Abstract
The complexity of computing systems keeps growing even beyond human capabilities to handle the management tasks for achieving the best benefits from such systems. Autonomic computing was introduced with the promise of self-management where computing systems would be able to manage their behaviors, moreover the concept of web service emerged for the intension of enabling heterogeneous computing systems. Web Services are modular, self- describing, self-contained and loosely coupled applications that can be published, located, and invoked across the web. With the increasing number of web services available on the web, the need for web services composition is becoming more and more important. Web service composition is a technology that has received considerable attention in the last number of years. Web service composition is the process of linking single Web services together in order to accomplish more complex tasks. One area of Web service composition that has not received as much attention is the area of dynamic error handling and re- planning, enabling autonomic composition. Given a repository of service descriptions and a task to complete, it is possible for Artificial Intelligence planning techniques to be used to automatically create a plan that will achieve this goal. If however a service in the plan is unavailable or erroneous the plan will fail. Motivated by this problem, this research suggests autonomous re-planning as a means to overcome dynamic problems.
This research surveys various automations and techniques for web service composition and compares 18 methods, detailing each method’s advantages, disadvantages and challenges. This research offers the opportunity to study this field and its problems, provide more diverse data and examine the problems of traffic networks.
References
S.Ghosh, D.L.Reilly. Credit card fraud detection with a neural– network, in: Proceedings of the Twenty-seventh Hawaii International Conference on system Sciences, 1994, Chapter 1: Introduction 17621-630p.
E. Aleskerov, B. Freisleben, B.Rao. CARDWATCH: a neural network based database mining system for credit card fraud detection in: Proceedings of the Computational Intelligence for Financial Enginnering, 1997:220-226p.
J. R.Dorronsoro, F. Ginel, C.Sanchez and C.S. Cruz. Neural fraud detection in credit card operations. IEEE Transactions on Neural Network
,1997.8(4):827-834p
M. Syeda, Y.Q.Zhang, Y. Pan, Parallel granular neural networks for fast credit card fraud detection, in: Proceedings of the IEEE International Conference on Fuzzy Systems, 2002, 572-577p.
P.K. Chan, W. Fan, A.L. Prodromidis,
S.J. Stolfo. Distributed data mining in credit card fraud detection. in:
Proceedings of the IEEE Intelligent Systems, 1999, 67-74p
Refbacks
- There are currently no refbacks.