XML data warehouses form an interesting basis for decision-support applications that exploit heterogeneous data from multiple sources. However, XML-native database systems currently suffer from limited performances in terms of manageable data volume and response time for complex analytical queries. Fragmenting and distributing XML data warehouses (e.g., on data grids) allow to address both these issues. In this paper, we work on XML warehouse fragmentation. In relational data warehouses, several studies recommend the use of derived horizontal fragmentation. Hence, we propose to adapt it to the XML context. We particularly focus on the initial horizontal fragmentation of dimensions' XML documents and exploit two alternative algorithms. We experimentally validate our proposal and compare these alternatives with respect to a unified XML warehouse model we advocate for.
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. Developing a data warehouse for XML documents involves two major processes: one of creating it, by processing XML raw documents into a specified data warehouse repository; and the other of querying it, by applying techniques to better answer usersâ€™ queries. This paper focuses on the first part; that is identifying a systematic approach for building a data warehouse of XML documents, specifically for transferring data from an underlying XML database into a defined XML data warehouse. The proposed methodology on building XML data warehouses covers processes including data cleaning and integration, summarization, intermediate XML documents, and updating linking existing documents and creating fact tables. In this paper, we also present a case study on how to put this methodology into practice. We utilise the XQuery technology in all of the above processes. A large amount of heterogeneous information is now available in enterprises. Some their data sources are repositories of XML data or they are viewed as XML data independently on their inner implementation. In this paper, we study the foundations of XML data warehouses. We adapt the traditional star schema with explicit dimension hierarchies for XML environment. We propose the notion of XML-referential integrity for handling XML-dimension hierarchies. For querying XML data warehouses, we introduce a semijoin operation based on approximate matching XML data and discuss its effective evaluation. XML is suitable for structuring complex data coming from different sources and supported by heterogeneous formats. It allows a flexible formalism capable to represent and store different types of data. Therefore, the importance of integrating XML documents in data warehouses is becoming increasingly high. In this paper, we propose an XML-based methodology, named X-Warehousing, which designs warehouses at a logical level, and populates them with XML documents at a physical level. Our approach is mainly oriented to users analysis objectives expressed according to an XML Schema and merged with XML data sources. The resulted XML Schema represents the logical model of a data warehouse. Whereas, XML documents validated against the analysis objectives populate the physical model of the data warehouse, called the XML cube. 