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knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword
addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA
, using pay-as-you-go billing strategy, a Service Level Agreement (SLA)- based dynamic model is designed, where customer satisfaction serves as the primary factor of the model. Finally, some simulations are conducted to show the effectiveness of the proposed model. Keywords-Massive data, cloud computing, service level
(IR) and Keywords Matching (KM) techniques. The aim is to report Skills Rec gains over conventional IR techniques. Based on Skills Rec results, this paper also provides top N=8 user-user recommendations most likely to be similar for a given active learner as a testing data.
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