Deep Web new data discovery strategy based on the graph model of data attribute value lists - Zhi-ming CUI; Peng-peng ZHAO; Xue-feng XIAN; Li-gang FANG; Yuan-feng YANG; Cai-dong GU (Tongxin xuebao)
A novel deep Web data discovery strategy was proposed for new generated data record in resources.In the ap-proach,a new graph model of deep Web data attribute value lists was used to indicate the deep Web data source,an new data crawling task was transformed into a graph traversal process.This model was only related to the data,compared with the ex-isting query-related graph model had better adaptability and certainty,applicable to contain only a simple query interface of deep Web data sources.Based on this model,which could discovery incremental nodes and predict new data mutual infor-mation was used to compute the dependencies between nodes.When the query selects,as much as possible to reduce the negative impact brought by the query-dependent.This strategy improves the data crawling efficiency.Experimental results show that this strategy could maximize the synchronization between local and remote data under the same restriction.