Chang, Understanding web query interfaces : Best-effort parsing with hidden syntax, Proceedings of the ACM SIGMOD international Conferenceon Management of data (Page: 107 Year of of Publication: 2004 ISBN:1-58113-859-8) Zhang, Toward large scale integration: Building a MetaQuerier over databases on the web, Proceedings of the 2nd Conference on Innovative Data Systems Research CIDR (Page: 44 Year of Publication: 2005) Raghavan, H.Garcia-Molina, Crawling the hidden web, Proceedings of the 27th International conference on VLDB (Page : 129 Year of Publication: 2001 ISBN: 1-55860-804-4)ĭocument Object Model Level 1 specification. Ives, Principles of Data Integration, (Elsevier, 2012). Halevy, Google’s Deep Web Crawl, Proceedings of the VLDB Endowment (Page: 1241 Year of Publication: 2008 ISSN: 2150-8097).Ī. Yu (Ed.), Lecture Notes in Computer Science, Advanced Conceptual Modeling Techniques chapter, Vol. Yau, Extracting Data Behind Web Forms, In A. V.Keulen, Deep Web Entity Monitoring, Proceedings of the 22nd International World Wide Web, (Page : 377 Year of Publication : 2013 ISBN:978-1-4503-2038-2). Leong (Ed.), Lecture Notes in Computer Science, Advances in Web-age information Management Chapter, Volume 4016 (Berlin:Springer-Verlag, 2006, 252-262). Hidalgo, Crawling web pages with support for client-side dynamism, In J. Norsk informatikkonferanse ISSN : 1892-0713, 2013. Fagernes, Crawling JavaScript websites using WebKit-with application to analysis to hate speech in online discussion. Management Information Systems, University of Arizona, USA, 1985. Choobineh, Form Driven Conceptual Data Modelling, Ph.D. Rahmouni, Extraction Of Object-Oriented Schemas from existing relational databases: a Form-driven Approach, INFORMATICA, Vol. Proceedings of the 5th ACM/IEEE-CS joint conference in Digital Libraries (Page: 100 Year of Publication: 2005 ISBN: 1-58113-876-8). Cho, Downloading Textual Hidden Web Content through Keyword Queries. Computer Engineering, University Mohammed V, EMI, Rabat, Morocco, 2008.Ī. Zellou, Contribution to the LAV rewriting in the context of WASSIT, toward a resources integration, Ph.D. Now, Predicts 2013: Application Integration, Gartner Report, 2012.Ī. L.Bing, Web Data Mining (Springer, 2007).ī. Zhang, Structured Databases on the Web: Observations and Implications, ACM SIGMOD Record, Vol. Chang, Accessing the Deep Web: A survey, Communications of the ACM, Vol. Bergman, The Deep Web: Surfacing Hidden Value, Journal of Electronic Publishing, Vol. Lyman, H.R, Varian, How Much Information 2003?, University of California, 2003. All the information extracted by our approach from and through the associated Html forms are used subsequently to build our final relational schema describing the associated deep web source.Ĭopyright © 2015 Praise Worthy Prize - All rights reserved. Our approach process uses two external knowledge databases: The first one is our proprietary knowledge database about the deep web domains called the Identification Tables and the second one is an external ontology. Our approach is based on a static and dynamic analysis of the Html forms giving access to the selected deep web source. This relational schema can be used by a virtual integration system to access the associated deep web source. Our aim in this paper is to present our automatic approach to extract a relational schema describing a selected deep web source. The deep web is much bigger and richer in information than the surface web, and its web sources are only accessible through the associated Html forms. The web is divided in two parts, a part that search engines can access and which is called the surface web, and an inaccessible part called the deep web.
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