XML Warehousing and OLAP

XML Warehousing and OLAP

Hadj Mahboubi (University of Lyon (ERIC Lyon 2), France)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-010-3.ch323
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Abstract

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.
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Background

XML warehousing research may be subdivided into three families. The first family focuses on Web data integration for decision-support purposes. However, actual XML warehouse models are not very elaborate. The second family of approaches is explicitly based on classical warehouse logical models (star-like schemas). The third family we identify relates to document warehousing. In addition, recent efforts aim at performing OLAP analyses over XML data.

XML Web Warehouses

The objective of these approaches is to gather XML Web sources and integrate them into a data warehouse. For instance, Xyleme (2001) is a dynamic warehouse for XML data from the Web that supports query evaluation, change control and data integration. No particular warehouse model is proposed, though.

Golfarelli et al. (2001) propose a semi-automatic approach for building a data mart’s conceptual schema from XML sources. The authors show how multidimensional design may be carried out starting directly from XML sources and propose an algorithm for correctly inferring the information needed for data warehousing.

Finally, Vrdoljak et al. (2003) introduce the design of a Web warehouse that originates from XML Schemas describing operational sources. This method consists in preprocessing XML Schemas, in creating and transforming the schema graph, in selecting facts and in creating a logical schema that validates a data warehouse.

XML Data Warehouses

In his XML-star schema, Pokorný (2002) models a star schema in XML by defining dimension hierarchies as sets of logically connected collections of XML data, and facts as XML data elements.

Hümmer et al. (2003) propose a family of templates enabling the description of a multidimensional structure for integrating several data warehouses into a virtual or federated warehouse. These templates, collectively named XCube, consist of three kinds of XML documents with respect to specific schemas: XCubeSchema stores metadata; XCubeDimension describes dimensions and their hierarchy levels; and XCubeFact stores facts, i.e., measures and the corresponding dimensions.

Rusu et al. (2005) propose a methodology, based on the XQuery technology, for building XML data warehouses, which covers processes such as data cleaning, summarization, intermediating XML documents, updating/linking existing documents and creating fact tables. Facts and dimensions are represented by XML documents built with XQueries.

Park et al. (2005) introduce an XML warehousing framework where every fact and dimension is stored as an XML document. The proposed model features a single repository of XML documents for facts and multiple repositories for dimensions (one per dimension).

Eventually, Boussaïd et al. (2006) propose an XML-based methodology, X-Warehousing, for warehousing complex data (Darmont et al., 2005). They use XML Schema as a modeling language to represent users’ analysis needs, which are compared to complex data stored in heterogeneous XML sources. Information needed for building an XML cube is then extracted from these sources.

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