Catálogo de publicaciones - libros

It is a difficult task to transform an XQuery posed gainst an XML view into an SQL appropriate for the original relational schema to get data. The main reason lies in the difference of the schema modeling power and the query syntax. In this paper, we propose to represent an XQuery as a set of for trees and return trees, or called the query forests as a whole, based on the functionality of the path expressions specified in each clause. These trees show the structural constraint of the input query and serve as the vehicle to combine the mapping SQL fragments into a complete SQL statement. A prototype has been implemented to show the effectiveness of the transformation process.

In this paper, the focus is on accelerating XPath location steps for evaluating regular path expression with predicate parameter in particular since it is a core component of many XML processing standards such as XSLT or XQuery. We present a new indexing structure, namely Xp-tree, which is used to speed-up the evaluation of XPath. Based on accelerating a node using planar combined with the numbering scheme, we devise efficiently derivative algorithms. Our experimental results demonstrate that the proposed method outperforms previous approaches using R-tree indexing mechanism in processing XML queries.

Semi-structured data has become more and more attention-getting with the emergence of XML, and it has aroused much enthusiasm for integrating XML and SQL in database community. Due to the complexity of XQuery, graphical XML query languages have been developed to help users query XML data. In this paper, we propose a new XML-to-SQL solution on the base of ORA-SS, a rich semantic model for semi-structured data. We model the data by ORA-SS schema and store them in an ORDB. Based on ORA-SS, we developed a graphical XML query language GLASS that not only expresses the query constraints and reconstruction structure in XML view but also the relational semantic in the XML view. This paper focuses on the translation algorithm from GLASS to SQLX, an XML extension on traditional SQL.

In mobile environments, tracking the changing positions of moving objects efficiently could substantially improve the quality of the Location Based Services. There arises the high demand for the indexes to support frequent updates. In this paper, we propose a novel grid-based index for moving objects, namely the GTree. Based on the recursive partition of the space and lazy maintenance, the GTree could maximize the stability of the index structure while minimizing the dynamic adjustment, and therefore significantly reduce the update overhead. Different from traditional top-down or bottom-up updates, we present a median-down approach, which could effectively reduce the number of disk access. As an alternative, a bulk-loading technique is introduced. The experiments show that the GTree has good update performance as well as query performance.

Although several sophisticated index structures for moving objects have been proposed, the hashing method based on a simple grid has been widely employed due to its simplicity. Since the performance of the hashing is largely affected by the size of a grid cell, it should be carefully decided with regard to the workload. In many real applications, however, the workload varies dynamically as time, for example the traffic in the commuting time vs. that in the night. The basic hashing cannot handle this dynamic workload because the cell size cannot be changed during the execution. In this paper, we propose the adaptive multi-level hashing to support the dynamic workload efficiently. The proposed technique maintains two levels of the hashes, one for fast moving objects and the other one for quasi-static objects. A moving object changes its level adaptively according to the degree of its movement.