International Journal Information and Communication Technology Research
Volume:2 Issue: 3, Summer 2009

Web services as the most important event in distributed computing, have achieved great popularity among software developers today. A critical step in the process of developing service-oriented applications is web service discovery, i.e., the identification of existing relevant web services that can potentially be used in the context of a new web application.In this paper, we have proposed a novel method based on data mining techniques to assist and improve the web service discovery process as well as the development of service-oriented applications. Our assistant discovery approach is based on automatic finding of semantic similarity between web services through the application of clustering methods. We have introduced a new fuzzy semantic clustering algorithm which assists web service consumers in discovering a group of similar web services through an individual query. This objective is attained by way of a search space reduction mechanism which adds to the efficiency of the approach. Our proposed approach provides dynamic and flexible clusters which can be changed at discovery process.We have conducted an experimental study on a data set of tagged web services with ontology. The ontology supports the semantic analysis. Preliminary results from clustering indicate the possibility of retrieving web services at the discovery process with reasonable precision by applying the proposed similarity model. From these promising results, we conclude that web service discovery process could be performing in a reasonable time because of reduced search space.

Linked Data as an important and novel subject has attracted great attention in the realm of the Semantic Web. Many works deal with publishing existing datasets as Linked Data. This paper discusses the challenges of publishing Persian linked data, and their potential solutions, based on the experiences and lessons learned from a project focused on publishing some academic data of the Ferdowsi University of Mashhad as Linked Data.

As e-commerce sales continue to grow, the associated online fraud remains an attractive source of revenue for fraudsters. These fraudulent activities impose a considerable financial loss to merchants, making online fraud detection a necessity. The problem of fraud detection is concerned with not only capturing the fraudulent activities, but also capturing them as quickly as possible. This timeliness is crucial to decrease financial losses. In this research, a profiling method has been proposed for credit card fraud detection. The focus is on fraud cases which cannot be detected at the transaction level. Based on the fact that there are strong periodic patterns in cardholder's behavior, the time series of aggregated daily amounts spent on an individual credit card has been considered in the proposed method. In this method, the inherent periodic and seasonal patterns are extracted from the time series to construct a cardholder's profile. These patterns have been used to shorten the time between when a fraud occurs and when it is finally detected. Simulation results indicate that the new approach has resulted in a timelier fraud detection, improved detection rate and consequently less financial loss in the cases where a cardholder follows a regular or semi regular periodic behavior. The proposed method is equally applicable to other e-payment methods with minor application-specific modifications.

In this paper, an architecture is proposed for the new emerging concept of knowledge networks. This architecture is based on a multi-aspect view to the problem of starting such a network in an organization. By integrating infrastructure, knowledge and business layers mounted on two aspects of human-based and organizational supportive conditions in a layered architecture, we proposed a novel architecture that contains intra-layer built-in assessment mechanisms as well. These assessment mechanisms ensure the gradual refinement of the network as the structure of each layer becomes fixed. In this paper, it has been discussed that how a multi-layer architecture with minimum amount of dependency among layers (that has the ability of interaction in business and knowledge layers) mayfacilitate knowledge management processes. The architecture can also give some guidelines on how to start such networks in inter-organizational level.One of the main applications of this architecture is to propose appropriate guidelines for the development of required sub-systems of intra-organizational knowledge networks. Therefore, at the end of this paper, the method of extracting these guidelines onthe establishment of one of thekey sub-systems for managing explicit organizational knowledge (e.g. Document Management system) is explained in details. These recommendations are based on a well-known standard of this domain. As the proposed extraction method is originatedfrom the layers independency and self-assessment mechanism between layers, it can purposefully bere-used for collaboration/experience/process management systems as well.

Considering convergence of vast and different technologies in new generation networks, the required quality of service is the key success factor. The site implementation according to special benchmarking (TPC-W), to achieve the acceptable service performance may be offered in a cost effective manner, is very complicated. In this paper a mathematical method will be introduced to help providers to make informed decisions with respect of right level of resources (number and server capacity, number and power of CPUs, and amount of permanent and temporary memory). The numerical results show that the new formulation confirms the results e-commerce site emulation in an acceptable level.

Interaction of humans and computer agents should be harmonized by adapting the automation level of IT systems, to maintain a high performance for the system, in the changing environmental conditions. This research presents an expert system for realization of adaptive autonomy (AA), using Petri nets, referred to as AAPNES. The design is based on the practical list of environmental conditions and superior expert's judgments. As revealed by the results, the presented AAPNES can effectively determine the proper level of automation for the changing performance shaping factors of human-automation interaction systems in the smart grid.

The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area. In the context of spam filtering systems, addressing the evolving nature of spams, which leads to obsolete the related models, has been always a challenge. In this paper an adaptive spam filtering system based on language model is proposed which can detect concept drift based on computing the deviation in email contents distribution. The proposed method can be used along with any existing classifier; particularly in this paper we use Naïve Bayes method as classifier. The proposed method has been evaluated with Enron data set. The results indicate the efficiency of the method in detecting concept drift and its superiority over Naïve Bayes classifier in terms of accuracy.