Abstract : Big-data computing is possibly the biggest innovation in computing in the ultimate decade. We have only begun to see its workable to collect, organize, and process statistics for profitable business ventures. Data mining is a most important component of the huge records technological know-how and the entire essence is to analytically explore information in search of regular patterns and to in addition validate the findings by applying the detected patterns to new statistics sets. Big Data issue large-volume, complex, growing records sets with multiple, independent sources. This paper explores the predominant challenges of large data mining some of which are as a result of the intrinsically distributed and complicated surroundings and some due to the large, unstructured and dynamic datasets handy for mining. We find out that the gradual shift in the direction of disbursed complex problem fixing environments is now prompting a range of new statistics mining research and development problems. In this paper also, we have proffered solution to the new challenges of large data mining by way of a proposition of the HACE theory to utterly harness the potential benefits of the large statistics revolution and to set off a innovative breakthrough in commerce and industry. The research also proposed three-tier facts mining structure for massive information that provides accurate and applicable social sensing remarks for a better appreciation of our society in real-time. Based on our observations, we recommend a re-visitation of most of the records mining strategies in use nowadays and a deployment of disbursed versions of the a number records mining models on hand in order to meet the new challenges of large data. Developers ought to take benefit of available massive data technologies with affordable, open source, and easy-to-deploy platforms. Keywords : Big data, Data mining, Autonomous sources, Distributed database, HACE theory.
Abstract : Ad hoc networks are one of the most exciting applications in the areas of automobile sector wireless communication. Ad hoc network technology will be used in the car's onboard communication unit to collect real-time data on traffic and road conditions from a variety of onboard sensors. Application of Ad-hoc networks include services like traffic control, real-time traffic re-routing and safety warning by traffic management intelligent systems. In this work, to improve road safety a hybrid VANET has been developed. In this paper deals with the challenges and special features and that distinguish these systems from other types of ad hoc sensor networks. Keywords : VANET, H-VANET, Sensor, Road Safety.
Abstract : Supporting enduring progression requires businesses to manage customer loyalty and commitment very prudently. Pragmatic studies have been conducted in many countries to study the relation between e-loyalty and commitment to its antecedents in the online retail business. However experimental research on these lines is practically missing in India. This article describes a theoretical model for investigating the influence of the antecedents: identification, enthusiasm, attention, absorption and interaction on fulfilment on e-loyalty and advocacy in the online retail context in India. The theoretical model is used as a basis to formulate hypotheses. The hypotheses are tested with data collected from a survey of online customers. The output from these tests show that the customer feel strong and energetic during the visit of the e-tailer website. Trust factor shows that the customer believes the e-tailer has the best interest in their minds and as an identification the customer feels it as an insult when someone citizens the e-tailer. Enthusiasm and attention show the discretionary time thing about the e-tailer and the time spent on the e-tailers website due to absorption. A positive drive of loyalty and customer commitment in online retailing is noteworthy and promising for the continued enhancement of e-retailing in the ensuing years. Keywords : Loyalty, Commitment, Online Retailing.