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Integrating information from multiple data sources is becoming increasingly important for enterprises that partner with other companies for e‐commerce. However, companies have their internal business applications deployed on diverse platforms and no standard solution for integrating information from these sources exists. To support business intelligence query activities, it is useful to build a data warehouse on top of middleware that aggregates the data obtained from various heterogeneous database systems. Online analytical processing (OLAP) can then be used to provide fast access to materialized views from the data warehouse. Since extensible markup language (XML) documents are a common data representation standard on the Internet and relational tables are commonly used for production data, OLAP must handle both relational and XML data. SQL and XQuery can be used to process the materialized relational and XML data cubes created from the aggregated data. This paper shows how to handle the two kinds of data cubes from a relational–XML data warehouse using extract, transformation and loading. Copyright © 2008 John Wiley & Sons, Ltd.

Almost all enterprises use relational databases to handle real time business operations and most need to generate various XML documents for data exchanges internally among various departments and externally with business partners. Exporting data in a relational database to an XML document can be considered a data conversion process. Based on the four approaches for data conversion: Customized program, Interpretive transformer, Translator generator, and Logical level translation, this paper proposes a new interpretive approach using Structured Export Markup Language (SEML) interpreter for converting relational data into XML documents. The frameworks and languages proposed by other researchers are neither generic nor able to generate arbitrary XML documents. Therefore, SEML interpreter is a simple, user friendly, and complete solution with a new mark-up language ? SEML ? for data conversion. The solution can be used as a generic tool for extracting, transforming, and loading (ETL) purposes. In other words, the SEML interpreter is a solution for relational databases similar to what X-Query is for XML databases.

The extensible markup language (XML) has become a standard for persistent storage and data interchange via the Internet due to its openness, self-descriptiveness, and flexibility. This article proposes a systematic approach to reverse engineer arbitrary XML documents to their conceptual schema, extended DTD graphs, which are DTD graphs with data semantics. The proposed approach not only determines the structure of the XML document, but also derives candidate data semantics from the XML element instances by treating each XML element instance as a record in a table of a relational database. One application of the determined data semantics is to verify the linkages among elements. Implicit and explicit referential linkages are among XML elements modeled by the parent-children structure and ID/IDREF(S), respectively. As a result, an arbitrary XML document can be reverse engineered into its conceptual schema in an extended DTD graph format.