A Framework for Mining and Querying Summarized XML Data through Tree-Based Association Rules

The massive amount of datasets expressed in different formats, such as relational, XML, and RDF, available in several real applications, may cause some difficulties to non-expert users trying to access these datasets without having sufficient knowledge on their content and structure. Moreover, the processes of query composition, especially in the absence of a schema, and interpretation of the obtained answers may be non-trivial. Data mining techniques, already widely applied to extract frequent correlations of values from both structured and semistructured datasets, provide several interesting solutions for knowledge elicitation. However, the mining process is often guided by the designer, who determines the portion of a dataset where useful patterns can be extracted based on his/her deep knowledge of the application scenario. In our opinion, a research challenge is to mine hidden information from huge datasets, and then use it order to gain useful knowledge. DOI: 10.4018/978-1-61350-356-0.ch012