TL;DR: This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system, and proposes three order encoding methods that can be used to represent XML order in the relational data model, and also proposes algorithms for translating ordered XPath expressions into SQL using these encoding methods.
Abstract: XML is quickly becoming the de facto standard for data exchange over the Internet. This is creating a new set of data management requirements involving XML, such as the need to store and query XML documents. Researchers have proposed using relational database systems to satisfy these requirements by devising ways to "shred" XML documents into relations, and translate XML queries into SQL queries over these relations. However, a key issue with such an approach, which has largely been ignored in the research literature, is how (and whether) the ordered XML data model can be efficiently supported by the unordered relational data model. This paper shows that XML's ordered data model can indeed be efficiently supported by a relational database system. This is accomplished by encoding order as a data value. We propose three order encoding methods that can be used to represent XML order in the relational data model, and also propose algorithms for translating ordered XPath expressions into SQL using these encoding methods. Finally, we report the results of an experimental study that investigates the performance of the proposed order encoding methods on a workload of ordered XML queries and updates.
TL;DR: A Syntax for Data: Typing semistructured data and the Lore system and database products supporting XML are explained.
Abstract: 1 Introduction 2 A Syntax for Data 3 XML 4 Query Languages 5 Query Languages for XML 6 Interpretation and advanced features 7 Typing semistructured data 8 Query Processing 9 The Lore system 10 Strudel 11 Database products supporting XML
TL;DR: In this paper, a method to convert data between a relational format and an XML document, by creating a set of XML Mapping Definition from metadata, selecting relational data from a relational application database, and converting the relational data to the XML document using the set of mapping definitions, is presented.
Abstract: A method to convert data between a relational format and an XML document, by creating a set of XML Mapping Definition from metadata; selecting relational data from a relational application database, and converting the relational data to the XML document using the set of XML Mapping Definition.
TL;DR: It turns out that the relational approach can handle most (but not all) of the semantics of semi-structured queries over XML data, but is likely to be effective only in some cases.
Abstract: XML is fast emerging as the dominant standard for representing data in the World Wide Web. Sophisticated query engines that allow users to effectively tap the data stored in XML documents will be crucial to exploiting the full power of XML. While there has been a great deal of activity recently proposing new semistructured data models and query languages for this purpose, this paper explores the more conservative approach of using traditional relational database engines for processing XML documents conforming to Document Type Descriptors (DTDs). To this end, we have developed algorithms and implemented a prototype system that converts XML documents to relational tuples, translates semi-structured queries over XML documents to SQL queries over tables, and converts the results to XML. We have qualitatively evaluated this approach using several real DTDs drawn from diverse domains. It turns out that the relational approach can handle most (but not all) of the semantics of semi-structured queries over XML data, but is likely to be effective only in some cases. We identify the causes for these limitations and propose certain extensions to the relational model that would make it more appropriate for processing queries over XML documents.
TL;DR: The XRANK system is presented, designed to handle the novel features of XML keyword search, which naturally generalizes a hyperlink based HTML search engine such as Google and can be used to query a mix of HTML and XML documents.
Abstract: We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents.