With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority.
XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this chapter, we present two such techniques. First, we propose a join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. Second, we present a strategy for selecting XML materialized views by cluster…
XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity and complexity of multidimensional data in a way traditional relational data warehouse approaches fail to achieve. However, XML-native database systems currently suffer from limited performance, both i…
Data warehousing and OLAP applications must nowadays handle complex data that are not only numerical or symbolic. The XML language is well-suited to logically and physically represent complex data. However, its usage induces new theoretical and practical challenges at the modeling, storage and analysis levels, and a new trend toward XML warehousing has been emerging for a couple of years. Unfortunately, no standard XML data warehouse architecture emerges. In this paper, we propose a unified XML warehouse reference model that synthesizes and enhances related work, and fits into a global XML war…
XML is gradually employed as a standard of data exchange in web environment since its inception in the 90s until present. It serves as a data exchange between systems and other applications. Meanwhile the data volume has grown substantially in the web and thus effective methods of storing and retrieving these data is essential. One recommended way is physically or virtually fragments the large chunk of data and distributes the fragments into different nodes. Fragmentation design of XML document contains of two parts: fragmentation operation and fragmentation method. The three fragmentation ope…
The data warehousing and OLAP technologies are now moving onto handling complex data that mostly originate from the Web. However, intagrating such data into a decision-support process requires their representation under a form processable by OLAP and/or data mining techniques. We present in this paper a complex data warehousing methodology that exploits XML as a pivot language. Our approach includes the integration of complex data in an ODS, under the form of XML documents; their dimensional modeling and storage in an XML data warehouse; and their analysis with combined OLAP and data mining te…
The aim of this article is to present an overview of the major XML warehousing approaches from the literature, as well as the existing approaches for performing OLAP analyses over XML data (which is termed XML-OLAP or XOLAP; Wang et al., 2005). We also discuss the issues and future trends in this area and illustrate this topic by presenting the design of a unified, XML data warehouse architecture and a set of XOLAP operators expressed in an XML algebra.
With XML becoming a standard for business information representation and exchange, stor-ing, indexing, and querying XML documents have rapidly become major issues in database research. In this context, query processing and optimization are primordial, native-XML data-bases not being mature yet. Data structures such as indices, which help enhance performances substantially, are extensively researched, especially since XML data bear numerous specifici-ties with respect to relational data. In this paper, we survey state-of-the-art XML indices and discuss the main issues, tradeoffs and future tren…
Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introd…
