A Framework for Semi-automated Web Service Composition in Semantic Web

Number of web services available on Internet and its usage are increasing very fast. In many cases, one service is not enough to complete the business requirement; composition of web services is carried out. Autonomous composition of web services to achieve new functionality is generating considerable attention in semantic web domain. Development time and effort for new applications can be reduced with service composition. Various approaches to carry out automated composition of web services are discussed in literature. Web service composition using ontologies is one of the effective approaches. In this paper we demonstrate how the ontology based composition can be made faster for each customer. We propose a framework to provide precomposed web services to fulfil user requirements. We detail how ontology merging can be used for composition which expedites the whole process. We discuss how framework provides customer specific ontology merging and repository. We also elaborate on how merging of ontologies is carried out.Comment: 6 pages, 9 figures; CUBE 2013 International Conferenc

Mobile Agent based Market Basket Analysis on Cloud

This paper describes the design and development of a location-based mobile shopping application for bakery product shops. Whole application is deployed on cloud. The three-tier architecture consists of, front-end, middle-ware and back-end. The front-end level is a location-based mobile shopping application for android mobile devices, for purchasing bakery products of nearby places. Front-end level also displays association among the purchased products. The middle-ware level provides a web service to generate JSON (JavaScript Object Notation) output from the relational database. It exchanges information and data between mobile application and servers in cloud. The back-end level provides the Apache Tomcat Web server and MySQL database. The application also uses the Google Cloud Messaging for generating and sending notification of orders to shopkeeper.Comment: 6 pages, 7 figure

Reverse Proxy Framework using Sanitization Technique for Intrusion Prevention in Database

With the increasing importance of the internet in our day to day life, data security in web application has become very crucial. Ever increasing on line and real time transaction services have led to manifold rise in the problems associated with the database security. Attacker uses illegal and unauthorized approaches to hijack the confidential information like username, password and other vital details. Hence the real time transaction requires security against web based attacks. SQL injection and cross site scripting attack are the most common application layer attack. The SQL injection attacker pass SQL statement through a web applications input fields, URL or hidden parameters and get access to the database or update it. The attacker take a benefit from user provided data in such a way that the users input is handled as a SQL code. Using this vulnerability an attacker can execute SQL commands directly on the database. SQL injection attacks are most serious threats which take users input and integrate it into SQL query. Reverse Proxy is a technique which is used to sanitize the users inputs that may transform into a database attack. In this technique a data redirector program redirects the users input to the proxy server before it is sent to the application server. At the proxy server, data cleaning algorithm is triggered using a sanitizing application. In this framework we include detection and sanitization of the tainted information being sent to the database and innovate a new prototype.Comment: 9 pages, 6 figures, 3 tables; CIIT 2013 International Conference, Mumba

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table