With the rise of XML as a standard for representing business data, XML data warehouses appear as suitable solutions for Web-based decision-support applications. In this context, it is necessary to allow OLAP analyses over XML data cubes (XOLAP). Thus, XQuery extensions are needed. To help define a formal framework and allow much-needed performance optimizations on analytical queries expressed in XQuery, having an algebra at one's disposal is desirable. However, XOLAP approaches and algebras from the literature still largely rely on the relational model and/or only feature a small number of OLAP operators. In opposition, we propose in this paper to express a broad set of OLAP operators with the TAX XML algebra.

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 sources are gaining popularity in the context of Business Intelligence and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing complex and heterogeneous data. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore, recent research efforts are focusing on horizontal fragmentation techniques, which are able to overcome the above limitations. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses. In this paper, we propose the use of the K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses. We complement our analytical contribution with a comprehensive experimental assessment where we compare the efficiency of our proposal against existing fragmentation algorithms.

Abstract. With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML data. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeB's usage is illustrated by experiments on several XML database management systems.

With the eXtensible Markup Language (XML) becoming a standard for representing business data (Beyer et al., 2005), a new trend toward XML data warehousing has been emerging for a couple of years, as well as efforts for extending the XQuery language with near On-Line Analytical Processing (OLAP) capabilities (grouping, aggregation, etc.). Though this is not an easy task, these new approaches, techniques and architectures aim at taking specificities of XML into account (e.g., heterogeneous number and order of dimensions or complex measures in facts, ragged dimension hierarchies…) that would be intricate to handle in a relational environment. 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.