Simple API for XML

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.

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.

Abstract: With the advent of XML as a standard for data representation and exchange on the Internet, storing and querying XML data becomes more and more important. Several XML query languages have been proposed, and the common feature of the languages is the use of regular path expressions to query XML data. This poses a new challenge concerning indexing and searching XML data, because conventional approaches based on tree traversals may not meet the processing requirements under heavy access requests. In this paper, we propose a new system for indexing and storing XML data based on a numbering scheme for elements. This numbering scheme quickly determines the ancestor-descendant relationship between elements in the hierarchy of XML data. We also propose several algorithms for processing regular path expressions, namely, (1) -Join for searching paths from an element to another, (2) -Join for scanning sorted elements and attributes to find element-attribute pairs, and (3) -Join for finding Kleene-Closure on repeated paths or elements. The -Join algorithm is highly effective particularly for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation show that the proposed algorithms can process XML queries with regular path expressions by up to an or

Abstract: XML documents on the web are often found without DTDs, particularly when these documents have been created from legacy HTML. Yet having knowledge of the DTD can be valuable in querying and manipulating such documents. Recent work (cf. [10]) has given us a means to (re-)construct a DTD to describe the structure common to a given set of document instances. However, given a collection of documents with unknown DTDs, it may not be appropriate to construct a single DTD to describe every document in the collection. Instead, we would wish to partition the collection into smaller sets of “similar” documents, and then induce a separate DTD for each such set. It is this partitioning problem that we address in this paper. Given two XML documents, how can one measure structural (DTD) similarity between the two? We define a tree edit distance based measure suited to this task, taking into account XML issues such as optional and repeated sub-elements. We develop a dynamic programming algorithm to find this distance for any pair of documents. We validate our proposed distance measure experimentally. Given a collection of documents derived from multiple DTDs, we can compute pair-wise distances between documents in the collection, and then use these distances to cluster the documents. We find that the resulting clusters match the original DTDs almost perfectly, and demonstrate performance superior to alternatives based on previous proposals for measuring similarity of trees. The overall algorithm runs in time that is quadratic in document collection size, and quadratic in the combined size of the two documents involved in a given pair-wise distance calculation.