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Rathi R.,Vellore Institute of Technology | Visvanathan P.,Ganadipathy Tulsis Enggineering College | Kanchana R.,Adhiparasakthi Engineering College
International Journal of Applied Engineering Research | Year: 2013

Cloud computing is a practical approach to experience direct cost benefits and it has the potential to transform a data centre from a capital-intensive set up to a variable priced environment. Cloud providers trade their services on cloud resources for money. It include separate prices for infrastructure elements, i.e., disk space, CPU, I/O, bandwidth and applications deployed on the infrastructure where the service is prices as static, and provides the user for pay as per the usage. These pricing scheme does not maximize profit for cloud providers. The goal of cloud economy is to optimize user satisfaction and cloud profit. So, we propose a novel scheme that achieves optimal pricing for the services of a cloud cache which is offered by a cloud provider as an integral part of its platform-as-a-service. The goal of such a cloud cache is to provide efficient querying on the back-end data at a low cost and a dynamic pricing scheme for queries executed in the cloud cache. Data caching in a cloud is a key paradigm for improving the performance of web services in terms of both end-user latency and database load. © Research India Publications. Source

Rathi R.,Vellore Institute of Technology | Visvanathan P.,Ganadipathy Tulsis Enggineering College | Kanchana R.,Vellore Institute of Technology | Anitha A.,Vellore Institute of Technology
International Journal of Applied Engineering Research | Year: 2013

Nowadays, there is a huge amount of data being collected, especially at the age of internet. It is very hard to extract knowledge from this huge amount of data. These data are of no use unless we retrieve knowledge from it. As there is a huge raise in in the volume of data, it is an obvious challenge to reduce the data set and extract useful information from it. This paper mainly focuses on extracting knowledge in the form of rules using rough set by finding the reduced set of data. In rough set theory, the data is represented in the form information system. Discernability matrix is used to find the core and reduct of the attribute. After finding the core and reduct the strength of the attributes is found and the rules are generated. Also the proposed work is validated by taking real time example which extracts knowledge in the form of rules. © Research India Publications. Source

Lokesh J.,Ganadipathy Tulsis Enggineering College | Rahamathunnisa U.,Vellore Institute of Technology | Sudhakar K.,Ganadipathy Tulsis Enggineering College | Sharmeela C.,Anna University
International Journal of Engineering and Technology | Year: 2013

With enormous use of sensor network, the sensor applications was enfeeble by large data movements across the network owing to the intrinsic characteristics of resource-constrained sensors. This paper emphasizes on two issues such as accuracy and communication overhead, data aggregation is an effective way to reduce communication issues at lower level, to take important decisions and accuracy at final aggregation result. Next a model is predict from the extracted data using supervised learning at the upper level thus reduce the communication effort, the high dimensional reduction of the data mining task, then improving the accuracy of the sensing task. Source

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