Multi-Party Coordination in the Context of MOWS

Separation of concerns has been presented as a promising tool to tackle the design of complex systems in which cross-cutting properties that do not fit into the scope of a class must be satisfied. In this paper, we show that interactions amongst a number of objects can also be described separately from functionality, which enhances reusability of functional code and interaction patterns. We present our proposal in the context of Multi-Qrganisational Web-Based Systems (MOWS) and also present a framework that provides the infrastructure needed to implement multiparty coordination as an independent aspect

Aspect-oriented interaction in multi-organisational web-based systems

Separation of concerns has been presented as a promising tool to tackle the design of complex systems in which cross-cutting properties that do not fit into the scope of a class must be satisfied. Unfortunately, current proposals assume that objects interact by means of object-oriented method calls, which implies that they embed interactions with others into their functional code. This makes them dependent on this interaction model, and makes it difficult to reuse them in a context in which another interaction model is more suited, e.g., tuple spaces, multiparty meetings, ports, and so forth. In this paper, we show that functionality can be described separately from the interaction model used, which helps enhance reusability of functional code and coordination patterns. Our proposal is innovative in that it is the first that achieves a clear separation between functionality and interaction in an aspect-oriented manner. In order to show that it is feasible, we adapted the multiparty interaction model to the context of multiorganisational web-based systems and developed a class framework to build business objects whose performance rates comparably to handmade implementations; the development time, however, decreases significantly.Comisión Interministerial de Ciencia y Tecnología TIC2000-1106-C02-0

Advances in a DSL for Application Integration

Enterprise Application Integration (EAI) is currently one of the big challenges for Software Engineering. According to a recent report, for each dollar spent on developing an application, companies usually spend from 5 to 20 dollars to integrate it. In this paper, we propose a Domain Specific Language (DSL) for designing application integration solutions. It builds on our experience on two real-world integration projects

Introduction to the Semantic Web

La Web Semántica se presenta con frecuencia como una gran revolución que permitirá a los ordenadores entender los contenidos de la Web clásica. El objetivo de este documento es servir de introducción a los conceptos y tecnologías fundamentales sobre los que se sustenta. Está pensado para personas con conocimientos informáticos que aún no están familiarizadas con este tema, por lo que no se trata de un documento formal y detallado, sino de un texto eminentemente introductorio y didáctico. Se proporcionan también varias referencias a la bibliografía y herramientas que ayudarán al lector a profundizar y entender mejor todos los detalles de la Web Semántica

Error-detection in enterprise application integration solutions

Enterprise Application Integration (EAI) is a field of Sofware Engineering. Its focus is on helping software engineers integrate existing applications at a sensible costs, so that they can easily implement and evolve business processes. EAI solutions are distributed in nature, which makes them inherently prone to failures. In this paper, we report on a proposal to address error detection in EAI solutions. The main contribution is that it can deal with both choreographies and orchestrations and that it is independent from the execution model used

On Extracting Information from Semi-structured Deep Web Documents

Some software agents need information that is provided by some web sites, which is difficult if they lack a query API. Information extractors are intended to extract the information of interest automati cally and offer it in a structured format. Unfortunately, most of them rely on ad-hoc techniques, which make them fade away as the Web evolves. In this paper, we present a proposal that relies on an open catalogue of features that allows to adapt it easily; we have also devised an optimi sation that allows it to be very efficient. Our experimental results prove that our proposal outperforms other state-of-the-art proposals.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-

On Learning Web Information Extraction Rules with TANGO

The research on Enterprise Systems Integration focuses on proposals to support business processes by re-using existing systems. Wrappers help re-use web ap plications that provide a user interface only. They emulate a human user who interacts with them and extracts the information of interest in a structured for mat. In this article, we present TANGO, which is our proposal to learn rules to extract information from semi-structured web documents with high precision and recall, which is a must in the context of Enterprise Systems Integration. It relies on an open catalogue of features that helps map the input documents into a knowledge base in which every DOM node is represented by means of HTML, DOM, CSS, relational, and user-defined features. Then a procedure with many variation points is used to learn extraction rules from that knowledge base; the variation points include heuristics that range from how to select a condition to how to simplify the resulting rules. We also provide a systematic method to help re-configure our proposal. Our exhaustive experimentation proves that it beats others regarding effectiveness and is efficient enough for practical purposes. Our proposal was devised to be as configurable as possible, which helps adapt it to particular web sites and evolve it when necessary.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-

Trinity: On Using Trinary Trees for Unsupervised Web Data Extraction

Web data extractors are used to extract data from web documents in order to feed automated processes. In this article, we propose a technique that works on two or more web documents generated by the same server-side template and learns a regular expression that models it and can later be used to extract data from similar documents. The technique builds on the hypothesis that the template introduces some shared patterns that do not provide any relevant data and can thus be ignored. We have evaluated and compared our technique to others in the literature on a large collection of web documents; our results demonstrate that our proposal performs better than the others and that input errors do not have a negative impact on its effectiveness; furthermore, its efficiency can be easily boosted by means of a couple of parameters, without sacrificing its effectiveness.Ministerio de Ciencia y Tecnología TIN2007-64119Junta de Andalucía P07- TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010- 21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-

On validating web information extraction proposals

Many people who have to make informed decisions in today’s always-on culture use information extractors to feed their systems with information that comes from human-friendly documents. Unfortunately, many proposals that validate information extractors have deficiencies that make it difficult to perform homogeneous comparisons, confirm or refute performance hypotheses, or draw unbiased conclusions. Consequently, it is very difficult to select the best-performing proposal on a sound basis. The state-of-the-art validation method overcomes many deficiencies in the previous proposals, but still overlooks the following issues: completeness of the validation datasets, that is, whether they provide a complete set of annotations or not; structure of the information, that is, whether they check the structure of the record instances extracted or just the attribute instances; and, finally, how extractions and annotations are matched. The decisions made regarding the previous issues have an impact on the effectiveness results. In this article, we have exhaustively analysed the literature and we have also highlighted the main weaknesses to tackle. We present a guideline and a method to compute the effectiveness, which complements and enhances the state-of-the-art validation method.Ministerio de Economía y Competitividad TIN2016-75394-RMinisterio de Ciencia e Innovación PID2020-112540RB-C44Junta de Andalucía P18-RT-1060Junta de Andalucía US-138137

A deep-learning approach to mining conditions

A condition is a constraint that determines when a consequent holds. Mining them in text is paramount to understand many sentences properly. In the literature, there are a few pattern-based proposals that fall short regarding recall because it is not easy to characterise unusual ways to express conditions with hand-crafted patterns; there is one machine-learning proposal that is bound to the Japanese language, requires specific-purpose dictionaries, taxonomies, and heuristics, works on opinion sentences only, and was evaluated very shallowly. In this article, we present a deep-learning proposal to mine conditions that does not have any of the previous drawbacks; furthermore, we have performed a comprehensive experimental study on a large multi-lingual dataset on many common topics; our conclusion is that our proposals are similar to the state of the art in terms of precision, but improve recall enough to beat them in terms of F1 score.Ministerio de Economía y Competitividad TIN2013-40848-RMinisterio de Economía y Competitividad TIN2016-75394-