Deep Web new data discovery strategy based on the graph model of data attribute value lists
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2016-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016049/ |
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| Summary: | 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. |
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| ISSN: | 1000-436X |