Abstract Crawling all deep web data is difficult for third party applications due to dynamicity, autonomy and quantity of deep web data sources. To tackle the deep web crawling problem under the query type restriction(only top-k queries are allowed) and limited query resources, an approach for incremental web crawling with top-k query constraint is proposed. Historical data and domain knowledge are combined to maximize total repository data quality. Firstly, valid queries are generated using a query tree, and changes and corresponding cost of the query are estimated by historical data and domain knowledge. Next, grounded on the query cost and data quality of the estimation, the optimal subset is selected approximately to globally maximize total data quality under limited query resources. The experimental results on real datasets show the proposed approach improves the efficiency of crawling dynamic web database.
About author:: JIANG Junyan, born in 1987, Ph.D. candidate. His research interests include Web data management.PENG Zhiyong, born in 1963, Ph.D., professor. His research interests include complex data management, trusted data management and Web data management.WU Xiaoying(Corresponding author), born in 1973, Ph. D., associate professor. Her research interests include data management, query processing and optimization, keyword query, pattern mining, semantic web, and data integration.
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2017, Vol. 30 
