Abstract Recently the reliability and dependability of the e-Government web pages is increasing as they not only provide news but also offer online services. is responsible to provide news, information and services to citizens, merchants and tourists in a reliable way. earlier we proposed a Web Monitoring System (WMS) that monitors the performance of a web portal in realtime [1] and... Recently the reliability and dependability of the e-Government web pages is increasing as they not only provide news but also offer online services. is responsible to provide news, information and services to citizens, merchants and tourists in a reliable way. earlier we proposed a Web Monitoring System (WMS) that monitors the performance of a web portal in realtime [1] and later extended to under visaulization of e Government services in [2]. Studies confirm that the web pages are complex and strtucture is difficult to understand and consequently the discovering of useful patterns and evaluting the web pages performance becomes difficult. We advocated that the potential lies in Visualization for evaluation. In the current extended study, we proposed a tool comprised of visualization and fuzzy association rule mining, and also did an extension to the process for monitoring and analyzing performances of e-Government webpages. Read More Read Less

Abstract The concept is viewed as an automated application of algorithms to discover hidden patterns and to extract knowledge from data. For querying huge datasets in interactive way, the Online Analytical Processing (OLAP) systems, are used. These OLAP systems are the predominant front-end tools used in data warehousing environments and the OLAP system’s market has developed rapidly during the last few... The concept is viewed as an automated application of algorithms to discover hidden patterns and to extract knowledge from data. For querying huge datasets in interactive way, the Online Analytical Processing (OLAP) systems, are used. These OLAP systems are the predominant front-end tools used in data warehousing environments and the OLAP system’s market has developed rapidly during the last few years. The integration of OLAP and data mining offer promising results. Currently many studies on data mining techniques along with OLAP have been applied in decision support applications to analyze large data sets in an efficient manner. However, in order to integrate data mining results with OLAP the data has to be modeled in a particular type of OLAP schema. An OLAP schema is a collection of database objects, including tables, views, indexes and synonyms. Schema generation process was considered a manual task but in the recent years research communities reported their work in automatic schema generation. Scanning of earlier stuides shown that in the past no integration of automatic schema generation with data mining was carried out. In this work, we propose a model for data mining and automatic schema generation of three types namely star, snowflake, and galaxy and also extend the work further by validation with the help of experiments. We found that the model we have generated is more important as it supports both integration and automation process. Read More Read Less

Abstract Audio watermarking is gaiing momentum in the recent years to embed copyright information in audio signals as a proof of their ownership. The significance of audio watermarking has enabled to protect the illegal manipulation of genuine audio sources. An efficient and scalable audio watermarking algorithm is designed and proposed in this study. The discrete wavelets transform (DWT) and the singular... Audio watermarking is gaiing momentum in the recent years to embed copyright information in audio signals as a proof of their ownership. The significance of audio watermarking has enabled to protect the illegal manipulation of genuine audio sources. An efficient and scalable audio watermarking algorithm is designed and proposed in this study. The discrete wavelets transform (DWT) and the singular value decomposition (SVD) are applied Suggestive evidences are generated through the experimental results. Read More Read Less

Abstract In this paper, we present a formal contextual security model for pervasive computing applications. This model is developed by addressing the required featurs such as - support of authorization and obligation policies, monitoring and dynamic revocation of access rights, support of personalized security rule contexts, and support of collaborative applications. Besides we design the model in a way to be... In this paper, we present a formal contextual security model for pervasive computing applications. This model is developed by addressing the required featurs such as - support of authorization and obligation policies, monitoring and dynamic revocation of access rights, support of personalized security rule contexts, and support of collaborative applications. Besides we design the model in a way to be logic-based. The mdoel is thus elegant as it enables the use of formal policy conflict and dynamic system analysis techniques. Read More Read Less

Abstract Conventional positive association rules are frequently occuring patterns. The patterns represent what decisions are routinely made based on a set of facts. Irregular association rules are the patterns that represent what decisions are rarely made based on the same set of facts. The application domains by nature require the irregular association rules and hence we developed a level wise search... Conventional positive association rules are frequently occuring patterns. The patterns represent what decisions are routinely made based on a set of facts. Irregular association rules are the patterns that represent what decisions are rarely made based on the same set of facts. The application domains by nature require the irregular association rules and hence we developed a level wise search algorithm that works based on action and non-action type data to find irregular association rules. Action type and non-action type will enable to discover irregular association rules. The algorithms that can tap the irregular associations have potential and our algorithm is found to be more effective in a real world datasets drawn in the health care field. Read More Read Less