Search Space Reduction Approach for Self-adaptive Web Service Discovery in Dynamic Mobile Environment

摘要: The proliferation of functionally similar Mobile Web Service (MWS) result in huge search space, the discovery MWS on such large space increases response time and probability discovering irrelevant irrespective matchmaking algorithm. existing research mostly focused applying coarse-grained reduction that fails to deal with cold-start data sparsity challenges at expense computing resources. proposed is achieved by subsuming k-means modified negative selection algorithm (M-NSA) place service an appropriate category so matching only performed target category. experimental results show significant improvement terms accuracy categorization which can improve a dynamic mobile environment (DME).