Many applications log a large amount of events continuously. Extracting interesting knowledge from logged events is an emerging active research area in data mining. In this context, we propose an approach for mining frequent events and association rules from logged events in XML format. This approach is composed of two-main phases: I) constructing a novel tree structure called Frequency XML-based Tree (FXT), which contains the frequency of events to be mined; II) querying the constructed FXT using XQuery to discover frequent itemsets and association rules. The FXT is constructed with a single-pass over logged data. We implement the proposed algorithm and study various performance issues. The performance study shows that the algorithm is efficient, for both constructing the FXT and discovering association rules.
There are two main types of approaches for XML data mining in the literature. The first type of approaches applies relational data mining tools on XML data by mapping XML documents to relational data model and storing them in a relational database @cite_10 . The second type of approaches applies data mining techniques directly onto native-XML data @cite_4 @cite_0 @cite_9 . We are interested with the second type of approaches, specifically mining frequent itemsets and association rules from XML data. Wan and Dobbie provide XQuery implementation of the well-known Apriori algorithm @cite_8 , to extract association rules from XML documents without any pre-processing or post-processing @cite_0 . Their algorithm is adapted to simple and well-defined XML format. This algorithm is extended with pre-processing step in order to mine more complex and irregular XML documents @cite_9 . Authors actually transform complex documents into a format that can be mined by Wan algorithm using XSLT. propose XMINE @cite_4 , a tool to extract XML association rules from XML documents. The XMINE operator is based on XPath and XQuery to express complex mining tasks on the content and the structure of XML data.
@cite_4 @cite_8 @cite_9 @cite_0 @cite_10
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The recent success of XML as a standard to represent semi-structured data, and the increasing amount of available XML data pose new challenges to the data mining community. In this paper we present the XMINE operator, a tool developed to extract XML association rules for XML documents. The operator, based on XPath and inspired by the syntax of XQuery, allows us to express complex mining tasks, compactly and intuitively. XMINE can be used to specify indifferently (and simultaneously) mining tasks both on the content and on the structure of the data, since the distinction in XML is slight. In recent years XML has became very popular for representing semistructured data and a standard for data exchange over the web. Mining XML data from the web is becoming increasingly important. Several encouraging attempts at developing methods for mining XML data have been proposed. However, efficiency and simplicity are still a barrier for further development. Normally, pre-processing or post-processing are required for mining XML data, such as transforming the data from XML format to relational format. In this paper, we show that extracting association rules from XML documents without any pre-processing or post-processing using XQuery is possible and analyze the XQuery implementation of the well-known Apriori algorithm. In addition, we suggest features that need to be added into XQuery in order to make the implementation of the Apriori algorithm more efficient. Currently, XML has been penetrating all areas of Internet for exchanging data. This fast growing usage of XML makes great amount data sources of XML data available and raises the need for languages, methods and tools to extract knowledge through the collections of XML documents. To date, the famous A priori algorithm to mine any XML document for association rules without any pre-processing or post-processing has been implemented using only the XQuery language. But the algorithm only can mine the set of items that can be written a path expression for. However, the structure of the XML data can be more complex and irregular than that. Consequently, it is difficult to identify the mining context. In this paper, we introduced XSL and XSLT which are also proposed by W3C to do some preprocessing of the input XML documents, transform the complex and irregular XML document into simple and regular XML document which can meet the needs of our mining algorithm. Our preprocessing makes the algorithm much more adaptable and universal. Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-dimensionally in the same way as relational data. In this paper, we propose a new framework for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact data as well as dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multidimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure data. They specify text mining operators for aggregating text constituting the measure data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-dimensionally analyzing the U.S. patents.