Abstract
There are several reasons that justify the study of a powerful, expressive and efficient XML-based framework for intelligent data analysis. First of all, the proliferation of XML sources offer good opportunities to mine new data. Second, native XML databases appear to be a natural alternative to relational databases when the purpose is querying both data and the extracted models in an uniform manner. This work offers a new query language for XML Data Mining. In presenting the language, we show its versatility, expressiveness and efficiency by proposing a concise, yet comprehensive set of queries which cover the major aspects of the data mining. Queries are designed over the well-known xmark XML database, that is a scalable benchmark dataset modeling an Internet auction site.
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Romei, A., Turini, F. (2011). Language Support to XML Data Mining: A Case Study. In: Filipe, J., Fred, A., Sharp, B. (eds) Agents and Artificial Intelligence. ICAART 2010. Communications in Computer and Information Science, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19890-8_2
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DOI: https://doi.org/10.1007/978-3-642-19890-8_2
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