The Evaluation of Content-Based Web Queries

We introduce the notions of syntactically and semantically structured data to refine the notion of semi-structured data. As we will see, most data found on the Web is syntactically structured. In order to evaluate content-based Web queries, semantically structured data is needed. The problem occurs to transform syntactically structured data into semantically structured data. Syntactically and semantically structured data can be represented by trees. Our main contribution is a powerful restructuring mechanism that allows to express the transformation of trees representing syntactically structured data to trees that represent semantically structured data. We embed our restructuring mechanism into RAW (Relational Algebra for the Web) and demonstrate its expressiveness by several example queries

PQL: A Declarative Query Language over Dynamic Biological Schemata

We introduce the PQL query language (PQL) used in the GeneSeek genetic data integration project. PQL incorporates many features of query languages for semi-structured data. To this we add the ability to express metadata constraints like intended semantics and database curation approach. These constraints guide the dynamic generation of potential query plans. This allows a single query to remain relevant even in the presence of source and mediated schemas that are continually evolving, as is often the case in data integration

Web Page Retrieval by Structure

Our research explores the possibility of categorizing webpages and webpage genre by structure or layout. Based on our results, we believe that webpage structure could play an important role, along with textual and visual keywords, in webpage categorization and searching

CXQuery: A novel XML query language

XML is becoming the data exchange standard on the Internet. Previously proposed XML query languages, such as XQuery, Quilt, YALT, Lorel, and XML-QL, lack schema definition of the query result; therefore, they are limited for defining views, integrating data, updating, and further querying, all of which are often needed in e-Business applications. We propose a novel XML query language called CXQuery, which defines the schema of the query results explicitly and can easily define views, and integrate, update, and query XML data. In addition, CXQuery can express spatial and spatio-temporal queries using a constraint-based querying approach

Rule-based information integration

In this report, we show the process of information integration. We specifically discuss the language used for integration. We show that integration consists of two phases, the schema mapping phase and the data integration phase. We formally define transformation rules, conversion, evolution and versioning. We further discuss the integration process from a data point of view

Comparative Analysis of Five XML Query Languages

XML is becoming the most relevant new standard for data representation and exchange on the WWW. Novel languages for extracting and restructuring the XML content have been proposed, some in the tradition of database query languages (i.e. SQL, OQL), others more closely inspired by XML. No standard for XML query language has yet been decided, but the discussion is ongoing within the World Wide Web Consortium and within many academic institutions and Internet-related major companies. We present a comparison of five, representative query languages for XML, highlighting their common features and differences.Comment: TeX v3.1415, 17 pages, 6 figures, to be published in ACM Sigmod Record, March 200

Bulkloading and Maintaining XML Documents

The popularity of XML as a exchange and storage format brings about massive amounts of documents to be stored, maintained and analyzed -- a challenge that traditionally has been tackled with Database Management Systems (DBMS). To open up the content of XML documents to analysis with declarative query languages, efficient bulk loading techniques are necessary. Database technology has traditionally been offering support for these tasks but yet falls short of providing efficient automation techniques for the challenges that large collections of XML data raise. As storage back-end, many applications rely on relational databases, which are designed towards large data volumes. This paper studies the bulk load and update algorithms for XML data stored in relational format and outlines opportunities and problems. We investigate both (1) bulk insertion and deletion as well as (2) updates in the form of edit scripts which heavily use pointer-chasing techniques which often are considered orthogonal to the algebraic operations relational databases are optimized for. To get the most out of relational database systems, we show that one should make careful use of edit scripts and replace them with bulk operations if more than a very small portion of the database is updated. We implemented our ideas on top of the Monet Database System and benchmarked their performance