ACM Transactions on Internet Technology (TOIT)
Mining the Web's Link Structure
Computer
Using Reinforcement Learning to Spider the Web Efficiently
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Proceedings of the 27th International Conference on Very Large Data Bases
Structured databases on the web: observations and implications
ACM SIGMOD Record
Downloading textual hidden web content through keyword queries
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Query Selection Techniques for Efficient Crawling of Structured Web Sources
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
An adaptive crawler for locating hidden-Web entry points
Proceedings of the 16th international conference on World Wide Web
Organizing Structured Deep Web by Clustering Query Interfaces Link Graph
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Proceedings of the VLDB Endowment
Measurement and classification of humans and bots in internet chat
SS'08 Proceedings of the 17th conference on Security symposium
FiVaTech: Page-Level Web Data Extraction from Template Pages
IEEE Transactions on Knowledge and Data Engineering
ViDE: A Vision-Based Approach for Deep Web Data Extraction
IEEE Transactions on Knowledge and Data Engineering
Crawling the content hidden behind web forms
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
Deep Web adaptive crawling based on minimum executable pattern
Journal of Intelligent Information Systems
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Suitable architecture specification of a deep web crawler with surface web crawler as well as indexer is proposed for fetching large number of documents from deep web using rules. The functional dependency of core and allied fields in the FORM are identified for generating rules using SVM classifier and classifies them as most preferable, least preferable and mutually exclusive. The FORMs are filled with values from most preferable class for fetching large number of documents. The extracted document is indexed for information retrieval applications. The architecture is extended to distributed crawler using web services. The proposed crawler fetches large number of documents while using the values in most preferable class. This architecture has higher coverage rate and reduces fetching time. The retrieval performance is encouraging and achieves similar precision of retrieval as Google search engine system.