Catálogo de publicaciones - libros

Data instances integration, specially on the web, involves analyzing and matching data from two or more sources, including XML sources. XML sources, in particular, introduce new challenges to the integration process, given their dynamic and irregular structure. In this context, one of the hardest steps is to find out which XML instances are similar. This paper presents a group of algorithms to prepare XML instances for comparison. We analyse the benefit of these algorithms over existing XML comparison approaches.

It is important to process the updates when nodes are inserted into or deleted from the XML tree. However, all the existing labeling schemes have high update cost. In this paper, we innovatively introduce a concept of Forbidden Code Segment (), and then propose a novel and efficient encoding approach, called Extended Lexicographical Order encoding based on Forbidden Code Segment ( Encoding), whose codes are more compact than CDBS and QED codes. The most important characteristic is that our labeling scheme can gracefully handle arbitrary update patterns and avoid re-labeling in XML updates, which is not at the sacrifice of query performance. We deliver the detailed theoretic analyses and experiments to show that, the proposed labeling scheme is superior to all the existing dynamic labeling schemes to process updates in terms of the incremental label size and the time for updating.

Reliable measure of similarity between symbolic sequences is an important problem in the fields of database and data mining. A lot of distance functions have been developed for symbolic sequence data in the past years. However, most of them are focused on the distance between complete symbolic sequences while the distance measurement for incomplete symbolic sequences remains unexplored. In this paper, we propose a method to process similarity search over incomplete symbolic sequences. Without any knowledge about the positions and values of the missing elements, it is impossible to get the exact distance between a query sequence and an incomplete sequence. Instead of calculating this exact distance, we map a pair of symbolic sequences to a real-valued interval, i.e, we propose a lower bound and an upper bound of the underlying exact distance between a query sequence and an incomplete sequence. In this case, similarity search can be conducted with guaranteed performance in terms of either recall or precision. The proposed method is also extended to handle with real-valued sequence data. The experimental results on both synthetic and real-world data show that our method is both efficient and effective.

Nowadays, RFID applications have attracted a great deal of interest due to their increasing adoptions in supply chain management, logistics and security. They have posed many new challenges to existing underlying database technologies, such as the requirements of supporting big volume data, preserving data transition path and handling new types of queries. In this paper, we propose an efficient method to manage RFID data. We explore and take advantage of the containment relationships in the relational tables in order to support special queries in the RFID applications. The experimental evaluation conducted on an existing RDBMS demonstrates the efficiency of our method.

Developing an easy-to-use, visual query tool for non-expert users to perform their own ad-hoc queries from relational databases is an active research area. The challenge lies in designing a visual query application that is both versatile and user-friendly; often expressive power comes at the expense of non-intuitive user interfaces. This work is based on a novel two-phased approach, so that users first specify the subset of data that is of interest (using a so-called principal concept), whereby the query generates an intermediary dataset. In the second phase, users query this dataset to further eliminate unwanted rows through Boolean constraints and to perform any additional operations, i.e. grouping or columns renaming. The user interface has been kept simple and the presented ideas have been successfully implemented in a prototype known as OVI-2, for use with the university student database at Helsinki University of Technology.

With the growing popularity of distance courses the demand for good didactic materials also increases. However, preparing such material is a complicated task, often done in an inproper way, by simply transforming traditional teaching/learning material into a digital form. In such case, one of the most important aspects of learning – the presence of a teacher, is not considered. The following paper presents a method that allows creating didactic materials for distance learning that could compensate for the lack of direct contact with a teacher in the learning process.

Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust mapping method which creates a mediated schema tree from a large set of input XML schema trees and defines mappings from the contributing schema to the mediated schema. The result is an almost automatic technique giving good performance with approximate semantic match quality. Our method uses node ranks calculated by pre-order traversal. It combines tree mining with semantic label clustering which minimizes the target search space and improves performance, thus making the algorithm suitable for large scale data sharing. We report on experiments with up to 80 schemas containing 83,770 nodes, with our prototype implementation taking 587 seconds to match and merge them to create a mediated schema and to return mappings from input schemas to the mediated schema.

In the last decades, the Internet and the World Wide Web have evolved into a new infrastructure for science, business, and the public. Driven by the need to better cope with recent trends and developments caused by globalization, complexity, and the grand challenges, we are refining and enhancing this infrastructure with powerful new tools for communication, collaboration, computation and the huge amounts of resulting data and knowledge. Researchers and business people alike are more and more able to easily access the tools, the data and the IT resources they need to solve their applications and to increase knowledge, via Grid and Service Oriented Architectures, and the technologies and tools to build them. This presentation will highlight Grids, SOA, and Web 2.0 and how they relate and complement, and the benefits they bring to scientists, businesses, and our whole society, supported by a number of use cases from research, industry, and public community.