Catálogo de publicaciones - libros

Efficient evaluation of queries on XML data is a major research issue. Structural join based techniques are well known for XPath evaluation. For the long path expressions, join techniques are not efficient as they increase the number of joins and disk I/O cost. Path based techniques try to reduce the number of joins. In this paper, we propose a metadata guided query evaluation technique which uses path based storage. We use interval encoding for the nodes. In addition, we use Strong DataGuide to assign integer path labels to distinct root-to-node label paths in the data tree. An is maintained for each distinct path consisting of nodes that can be reached by that path. The gives the one-to-many mapping between element names (or tag names) to element lists with nodes having that tag-name. The gives the root-to-leaf path for a given path label. Using these structures, we can combine top-down path matching and bottom-up path selections to efficiently evaluate linear path expressions. For twig queries, we perform structural joins at branch points. Through experimental evaluation on standard datasets, we show that our approach outperforms the existing path-index based approaches which in turn outperform structural join methods.

We present a new approach for accelerating the execution of XPath expressions using parameterized materialized XPath views (PXV). While the approach is generic we show how it can be utilized in an XML extension for relational database systems. Furthermore we discuss an algorithm for automatically determining the best PXV candidates to materialize based on a given workload. We evaluate our approach and show the superiority of our cost based algorithm for determining PXV candidates over frequent pattern based algorithms.

Many of today’s middleware solutions employ XML to resolve heterogeneities and to create an interoperable layer between sources and systems. However, XML performs poorly when queried in its native format, and local and global views are not supported by current XML products. In this paper, we support the concept of data everywhere by providing a view mechanism for XML together with a highly-optimised query processing strategy.

Order is essential for XML query processing. Efficient XML processing with order consideration over relational storage is non-trivial, especially for complex nested XQuery expressions. The order semantics may impede efficient query rewriting for nested query blocks. We propose a general order-sensitive XQuery processing approach involving three steps. First an algorithm is proposed for inferencing about and then isolating the order semantics in XQuery expressions specified over virtual XML views. This turns an XQuery plan into an one decorated with minimized . Then without loss of semantics, logical optimization via XQuery rewriting can be easily applied to this transformed query plan. As last step, the translation of the optimized logical plan into SQL now correctly incorporates the order context annotations to assure the original order semantics. Our experiments illustrate the performance gains achievable by our order handling strategy.

At the heart of any geographic information system (GIS) is a database system. Data representing geographic entities and spatial features are stored in these GIS and manipulated and visualised according to the user’s input. The rapid emergence of GIS has demanded the evolution of database systems to support these spatial data, and to provide powerful analysis operations and functions to assist in decision support, projections, predictions, and simulations in a wide variety of problem domains. The research reported on in this paper investigates a specific area of interest in geospatial database systems, that of the management and representation of evolving features. Features in a GIS group together entities or areas that are of particular interest from a specific viewpoint, such as counties, population, or in this case, roads.

With the expansion of the Internet, the amount of information available is continuously reaching higher growth rates. This fact leads to the necessity of developing new advanced tools to collect and filter information that meets users’ preferences. This paper presents an agent that uses automatic indexing, concept space generation, and a Hopfield artificial neural network to filter web pages according to users’ interests. The experiments that were conducted to evaluate the agent show that it has very satisfactory precision and coverage rates.

A novel approach to managing the information needed by healthcare practitioners working collaboratively to care for a patient is described. Traditionally, healthcare information systems have been disease focused, containing patient data related only to a specific function or concern, such as laboratory results or a particular disease. As healthcare is moving toward a collaborative, patient-centric approach which involves care teams comprising a range of health professionals with different needs, skills and working practices, they require up to date, reliable access to more comprehensive patient data; Data which is currently spread through databases at several treatment centres including hospitals, GP’s surgeries and palliative care centres. Additionally, this information must be accessible without disrupting the current systems and services provided by each institution from these systems. A new approach to data sharing based on Virtual Organisations (VO) with a Service Oriented Architecture (SOA) which will allow patient-relevant data to be accessed from the diverse sources available is presented. Preliminary requirements and challenges that such an approach will need to meet are presented, and the approach is compared with existing approaches to illustrate its applicability to this domain. Finally, results and future work are covered.

Whenever mobile ad-hoc networks are used as a large data storage, a huge number of queries requesting the same information again and again can slow down the network and drain battery power. In this paper, we introduce an example application, the query classes that are required to be supported, and show up two possible caching methods. The two caching methods are based on the concept of query shipping and data shipping, respectively. Since our caching strategies can be used simultaneously, the decision for doing data shipping depends among other aspects on the overhead of transferred data. We explain why an XML Query Result Size Estimator can assist the application in the question of which mechanism should be used for a certain query, and point to other related estimation techniques.

Spreadsheets such as Microsoft Excel are OLAP(On-Line Analytical Processing) [2] applications to easily analyze complex multidimensional data. In general, spreadsheets provide grid-like graphical interfaces together with various chart tools [4,5]. However, previous work on OLAP spreadsheets adopts a naive approach that directly retrieves, transmits, and presents all the resulting data at once. Thus, it is difficult to use the previous work for very large relational tables with millions of rows or columns due to the communication and space overhead.

One problem exists in current substructure mining algorithms is that when the sizes of molecular structure databases increase, the costs in terms of both time and space increase to a level that normal PCs are not powerful enough to perform substructure data mining tasks. After examining a number of well known molecular structure databases, we found that there exist a large number of common loop substructures within molecular structure databases, and repeatedly mining these same substructures costs the system resources significantly. In this paper, we introduce a new method: (1) to treat these common loop substructures as some kinds of “atom” structures; (2) to maintain the links of the new “atom” structures with the rest of the molecular structures, and to reorganize the original molecular structures. Therefore we avoid repeat many same operations during mining process and produce less redundant results. We tested the method using four real molecular structure databases: AID2DA’99/CA, AID2DA’99/CM, AID2DA’99 and NCI’99. The results indicated that (1) the speed of substructure mining has been improved due to the reorganization; (2) the number of patterns obtained by mining has been reduced with less redundant information.