Catálogo de publicaciones - libros

(SI) refers to new information such as annotations and summaries overlaid on fragments of existing (BI) such as web pages and PDF documents. Each BI fragment is referenced using an encapsulated address called a . Based on the widespread applicability of SI and wide range of superimposed applications (SAs) that can be built, we consider here how to represent marks explicitly in a conceptual model for an SA. The goal of this work is to facilitate the development of SAs by making it easy to model SI (including the marks) and to exploit the middleware and query capability that we have developed for managing marks and interacting with the base applications. The contribution of this paper is a general-purpose framework to make marks explicit in a conceptual (ER) model. We present conventions to associate marks with entities, attributes, and relationships; and to represent that an attribute’s value is the same as the excerpt obtained from a mark. We also provide procedures to automatically convert ER schemas expressed using our conventions to relational schemas, and show how a resulting relational schema supports SQL queries over the combination of SI, the associated marks and the excerpts associated with the marks.

The notion of user preference in database modeling has recently received much attention in advanced applications, such as personalization of e-services, since it captures the human wishes on querying and managing data. The paradigm of preference-driven choices in the real world requires new semantic constraints in modelling. In this paper, we assume preference constraints can be defined over data domains and thus the assumption gives rise to preference relations as a special case of ordered relations over schemas consisting of the preference, preferencedependent and preference-independent attributes. We demonstrate that Lexicographically Ordered Functional Dependencies (LOFDs) can be employed to maintain the consistency of preference semantics embedded in preference database, since prioritized multiple preferences can be represented. We thus define a useful semantic constraint in terms of a set of LOFDs, called Preference Functional Dependencies (PFDs), in order to capture the semantics of the preference ranked data. We exhibit a sound and complete axiom system for PFDs, whose implication problem is shown to be decidable in polynomial-time. We also confirm the existence of Armstrong preference relations for PFDs, a fundamental result related to the practical use of PFDs in database design.

We consider hierarchical systems where nodes represent entities and edges represent binary relationships among them. An example is a hierarchical composition of Web services where the nodes denote services and edges represent the parent-child relationship of a service invoking another service. A fundamental issue to address in such systems is, for two nodes X and Y in the hierarchy whether X can see Y, that is, whether X has visibility over Y. In a general setting, X seeing Y may depend on (i) X wishing to see Y, (ii) Y wishing to be seen by X, and (iii) other nodes not objecting to X seeing Y. The visibility could be with respect to certain attributes like operational details, execution logs, security related issues, etc. In this paper, we develop a generic conceptual model to express visibility. We study two complementary notions: of a node X that includes all the nodes in the hierarchy that X sees; and of X that includes all the nodes that see X. We also identify the dual properties, coherence and correlation, that relate the visibility and noticeability notions. We propose elegant methods of constructing the spheres with these properties.

Event detection is a very important area of research that discovers new events reported in a stream of text documents. Previous research in event detection has largely focused on finding the first story and tracking the events of a specific topic. A topic is simply a set of related events defined by user supplied keywords with no associated semantics and little domain knowledge. We therefore introduce the Anticipatory Event Detection (AED) problem: given some user preferred event transition in a topic, detect the occurence of the transition for the stream of news covering the topic. We confine the events to come from the same application domain, in particular, mergers and acquisitions. Our experiments showed that classical cosine similarity method fails for the AED task, whereas our conceptual model-based approach, through the use of domain knowledge and named entity type assignments, seems promising. We show experimentally that an AED voting classifier operating on a vector representation with name entities replaced by types performed AED successfully.

XML is the de facto standard for representing and exchanging data on the World Wide Web and XSLT is a primary language for XML transformation. Integration of XML data is an increasingly important problem and many methods have been developed. In this paper, we study the related and more difficult problem of how to integrate XSLT programs. Program integration can be particularly important for server-side XSLT applications, where it is necessary to generate a global XSLT program, that is a combination of some initial XSLT programs and which is required to operate over a newly integrated XML database. This global program should inherit as much functionality from the initial XSLT programs as possible, since designing a brand new global XSLT program from scratch could be expensive, slow and error prone, especially when the initial XSLT programs are large or/and complicated. However, it is a challenging task to develop methods to support XSLT integration. Difficulties such as template identification, unmapped template processing and template equivalence all need to be resolved. In this paper, we propose a framework for semi-automatic integration of XSLT programs. Our method makes use of static analysis techniques for XSLT and consists of four key steps: i) Pattern Specialization, ii) Template Translation, iii) Lost Template Processing and iv) Program Integration. We are not aware of any previous work that deals with integrating XML transformations.

Recently, a number of relational-based approaches for detecting the changes to XML data have been proposed to address the scalability problem of main memory-based approaches (e.g., X-Diff, XyDiff). These approaches store the XML documents in the relational database and issue SQL queries (whenever appropriate) to detect the changes. In this paper, we propose a relational-based XML change detection technique (called ) that uses a approach as the underlying storage strategy for XML data. Previous efforts have focused on detecting changes to ordered XML in an storage environment. Although the schema-oblivious approach produces better compared to XyDiff (a main memory-based ordered XML change detection approach), its performance degrade with increase in data size and is slower than XyDiff for smaller data set. We propose a technique to overcome these limitations. Our experimental results show that is up to 22 times faster and more scalable than the relational-based schema-oblivious approach. The performances of and XyDiff (C version) are comparable. However, more importantly, our approach is more scalable compared to XyDiff for larger datasets and has much superior the result quality of deltas than XyDiff.

When web servers publish data formatted in XML, only the current state of the data is (generally) published. But data evolves over time as it is updated. Capturing that evolution is vital to recovering past versions, tracking changes, and evaluating temporal queries. This paper presents a system to build a data collection, which records the history of each published datum rather than just its current state. The key to exchanging temporal data is providing a to mediate the interaction between the publisher and the reader. The schema describes how to construct a temporal data collection by “gluing” individual states into an integrated history.

Statistical summaries in relational databases mainly focus on the distribution of data values and have been found useful for various applications, such as query evaluation and data storage. As xml has been widely used, e.g. for online data exchange, the need for (corresponding) statistical summaries in xml has been evident. While relational techniques may be applicable to the data values in xml documents, novel techniques are requried for summarizing the structures of xml documents. In this paper, we propose metrics for major structural properties, in particular, nestings of entities and one-to-many relationships, of XML documents. Our technique is different from the existing ones in that we generate a quantitative summary of an xml structure. By using our approach, we illustrate that some popular real-world and synthetic xml benchmark datasets are indeed highly skewed and hardly hierarchical and contain few recursions. We wish this preliminary finding shreds insight on improving the design of xml benchmarking and experimentations.

One of the main drawbacks of the Semantic Web is the lack of semantically rich data, since most of the information is still stored in relational databases. In this paper, we present an approach to map legacy data stored in relational databases into the Semantic Web using virtually any modern RDF query language, as long as it is closed within RDF. Consequently, a Semantic Web developer does not need to learn and adopt a new mapping language, but he may perform the mapping task using his preferred RDF query language.

Transitive propagation along properties can be modelled in various ways in the OWL description logic. Doing so allows existing description logic reasoners based on the tableaux algorithm to make inferences based on such transitive constructs. This is espectially useful for medical knowledge bases, where such constructs are common.

This paper compares, contrasts and evaluates a variety of different methods for simulating transitive propagation: property subsumption, classic SEP triples and adapted SEP triples. These modelling techniques remove the need to extending the OWL language with additional operators in order to express the transitive propagation. Other approaches require an extended tableaux reasoner or first-order logic prover, as well as a modification of the OWL standard.

The adapted SEP triples methodology is ultimately recommended as the most reliable modelling technique.