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Research Article Open Access
Utilizing the reasoning processing technology, a new cellular movie loading structure, known as AMESCloud, which has two main parts: AMoV (adaptive cellular movie streaming) and ESoV (efficient public movie sharing). AMoV and ESoV build a personal broker to offer movie loading services efficiently for each cellular customer. For a given customer, AMoV lets her undercover broker adaptively adjust her stream flow with a scalable movie programming technique in accordance with the reviews of link quality. Likewise, ESoV watches the online community communications among cellular users, and their personal providers try to prefetch movie content in advance. We apply a model of the AMES-Cloud structure to show its performance. It is shown that the personal providers in the atmosphere can effectively offer the flexible loading, and perform movie discussing (i.e., prefetching) in accordance with the online community analysis.. We first recommend an precise Comparison-based Profile Related method (eCPM) which runs between two parties, an initiator and a -responder. The eCPM enable the initiator to acquire the comparison-based matching outcome about a specified feature in their profiles, while stop their feature values from exposure. We then recommend an implied Comparison-based Profile Related method (iCPM) which allows the initiator to straight acquire some information instead of the evaluation outcome from the -responder. The information unique to customer profile can be separated into multiple groups by the -responder. The initiator unquestioningly selects the involved category which is unknown to the -responder.