Projects

BaseX is an early adopter of the upcoming XQuery Full Text Recommendation. This paper presents some of the enhancements made to the XML database to fully support the language extensions. The system s data and index structures are described, and implementation details are given on the XQuery compiler, which supports sequential scanning, index-based, and hybrid processing of full-text queries. Experimental analysis and an insight into visual result presentation of query results conclude the presentation.

BaseX is an early adopter of the upcom- ing XQuery Full Text Recommendation. This paper presents some of the enhancements made to the XML database to fully support the language extensions. The system s data and index structures are described, and implementation details are given on the XQuery compiler, which sup- ports sequential scanning, index-based, and hybrid processing of full-text queries. Experimental analysis and an insight into visual result presen- tation of query results conclude the presentation.

Ever-growing data volumes demand for storage systems beyond current file systems abilities, particularly, a powerful querying capability. With the rise of XML, the database community has been challenged by semi-structured data processing, enhancing their field of activity. Since file systems are structured hierarchically they can be mapped to XML and as such stored in and queried by an XML-aware database. We provide an evaluation of a state-of-the-art XML-aware database implementing a file system.

XML documents are widely used as a generic container for textual contents. As they are increasingly growing in size, XML databases are emerging to efficiently store and query their contents. Besides, due to the hierarchic structure of XML documents, hierarchic visualizations are needed to facilitiate cognitive access to query results. BaseX is a simple database prototype, mapping XML documents to a table based tree encoding. An integrated treemap visualization and a query interface allow visual access to the documents and demonstrate the efficiency of the underlying data storage.

Two competing encoding concepts are known to scale well with growing amounts of XML data: XPath Accelerator encoding implemented by MonetDB for in-memory documents and X-Hive's Persistent DOM for on-disk storage. We identified two ways to improve XPath Accelerator and present prototypes for the respective techniques: BaseX boosts in-memory performance with optimized data and value index structures while Idefix introduces native block-oriented persistence with logarithmic update behavior for true scalability, overcoming main-memory constraints.
An easy-to-use Java-based benchmarking framework was developed and used to consistently compare these competing techniques and perform scalability measurements. The established XMark benchmark was applied to all four systems under test. Additional fulltext-sensitive queries against the well-known DBLP database complement the XMark results.
Not only did the latest version of X-Hive finally surprise with good scalability and performance numbers. Also, both BaseX and Idefix hold their promise to push XPath Accelerator to its limits: BaseX efficiently exploits available main memory to speedup XML queries while Idefix surpasses main-memory constraints and rivals the on-disk leadership of X-Hive. The competition between XPath Accelerator and Persistent DOM definitely is relaunched.