Abstract
In this paper, we propose an algorithm called HW-Transform for transforming hidden web data to XML format using machine learning by extending stalker to handle hyperlinked hidden web pages. One of the key features of our approach is that we identify and transform key attributes of query results into XML attributes. These key attributes facilitate applications such as change detection and data integration. by efficiently identifying related or identical results. Based on the proposed algorithm, we have implemented a prototype system called hw-stalker using Java. Our experiments demonstrate that HW-Transform shows acceptable performance for transforming query results to XML.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kovalev, V., Bhowmick, S.S., Madria, S. (2004). HW-STALKER: A Machine Learning-Based Approach to Transform Hidden Web Data to XML. In: Galindo, F., Takizawa, M., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2004. Lecture Notes in Computer Science, vol 3180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30075-5_90
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DOI: https://doi.org/10.1007/978-3-540-30075-5_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22936-0
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