2,769 research outputs found

    Datamining for Web-Enabled Electronic Business Applications

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    Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

    Support for collaborative component-based software engineering

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    Collaborative system composition during design has been poorly supported by traditional CASE tools (which have usually concentrated on supporting individual projects) and almost exclusively focused on static composition. Little support for maintaining large distributed collections of heterogeneous software components across a number of projects has been developed. The CoDEEDS project addresses the collaborative determination, elaboration, and evolution of design spaces that describe both static and dynamic compositions of software components from sources such as component libraries, software service directories, and reuse repositories. The GENESIS project has focussed, in the development of OSCAR, on the creation and maintenance of large software artefact repositories. The most recent extensions are explicitly addressing the provision of cross-project global views of large software collections and historical views of individual artefacts within a collection. The long-term benefits of such support can only be realised if OSCAR and CoDEEDS are widely adopted and steps to facilitate this are described. This book continues to provide a forum, which a recent book, Software Evolution with UML and XML, started, where expert insights are presented on the subject. In that book, initial efforts were made to link together three current phenomena: software evolution, UML, and XML. In this book, focus will be on the practical side of linking them, that is, how UML and XML and their related methods/tools can assist software evolution in practice. Considering that nowadays software starts evolving before it is delivered, an apparent feature for software evolution is that it happens over all stages and over all aspects. Therefore, all possible techniques should be explored. This book explores techniques based on UML/XML and a combination of them with other techniques (i.e., over all techniques from theory to tools). Software evolution happens at all stages. Chapters in this book describe that software evolution issues present at stages of software architecturing, modeling/specifying, assessing, coding, validating, design recovering, program understanding, and reusing. Software evolution happens in all aspects. Chapters in this book illustrate that software evolution issues are involved in Web application, embedded system, software repository, component-based development, object model, development environment, software metrics, UML use case diagram, system model, Legacy system, safety critical system, user interface, software reuse, evolution management, and variability modeling. Software evolution needs to be facilitated with all possible techniques. Chapters in this book demonstrate techniques, such as formal methods, program transformation, empirical study, tool development, standardisation, visualisation, to control system changes to meet organisational and business objectives in a cost-effective way. On the journey of the grand challenge posed by software evolution, the journey that we have to make, the contributory authors of this book have already made further advances

    Interoperable intelligent environmental decision support systems: a framework proposal

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    In this paper, an approach for the development of Interoperable Intelligent Environmental Decision Support Systems (IEDSS) is proposed. The framework is based upon the cognitive-oriented approach for the development of IEDSS proposed in (Sànchez-Marrè et al., 2008), where three kind of tasks must be built: analysis tasks, synthesis tasks and prognosis tasks. Now, a fourth level will be proposed: the model construction layer, which is normally an off-line task. At each level, interoperability should be possible and inter-level interoperability must be als o achieved. This interoperability is proposed to be obtained using data interchange protocols like Predictive Model Markup Language (PMML), which is a model interc hange protocol based on XML language, using an ontology of data and AI models to characterize data types and AI models and to set-up a common terminology, and using workflows of the whole interoperation scheme. In the future, a Multi-Agent System will be used to implement the software components. An example of use of the pro posed methodology applied to the supervision of a Wastewater Treatment Plant is provided. This Interoperable IEDSS framework will be the first step to an actual interoperability of AI models which will make IEDSS more reliable and accurate to solve complex environmental problems.Peer ReviewedPostprint (published version

    TSML: A XML-based Format for Exchange of Training Samples for Pattern Recognition in Remote Sensing Images

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    The availability of large and complex data sets has shifted the focus of pattern recognition towards developing techniques that can efficiently handle these types of data sets. For example, Multiple Classifier Systems claim their ability in reducing the error and complexity of classification by partitioning the data space and combining classifiers predictions. However, it is not an easy task to generate several partitions and moreover to use them in an efficient manner. Another difficult aspect is related to the exchange of training data in different formats among systems to combine classifiers of different and heterogeneous systems. This paper presents a model and structure of training samples based on XML (eXtensible Markup Language) to facilitate the partitioning and exchange among different image classification system. The main contribution is to apply the flexibility of XML that addresses interoperability and communication among heterogeneous systems in partitioning data sets as well as to facilitate interchange of such sets among image processing and pattern recognition systems

    Machine Understandable Contracts with Deep Learning

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    This research investigates the automatic translation of contracts to computer understandable rules trough Natural Language Processing. The most challenging aspect, which is studied throughout this paper, is to understand the meaning of the contract and express it into a structured format. This problem can be reduced to the Named Entity Recognition and Rule Extraction tasks, the latter handles the extraction of terms and conditions. These two problems are difficult, but deep learning models can tackle them. We think that this paper is the first work to approach Rule Extraction with deep learning. This method is data-hungry, so the research also introduces data sets for these two tasks. Additionally, it contributes to the literature by introducing Law-Bert, a model based on BERT which is pre-trained on unlabelled contracts. The results obtained on Named Entity Recognition and Rule Extraction show that pre-training on contracts has a positive effect on performance for the downstream tasks

    Machine Translation and Neural Networks for a multilingual EU

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    This paper presents an overview of the current developments and use of Machine Translation (MT) and Neural Machine Translation (NMT), specifically eTranslation, in the European Institutions. An insight into the state-of-the-art of NMT as currently in development in the Directorate-General for Translation (DG TRAD) of the European Parliament is provided by Pascale Chartier-Brun. Problems in machine translation support requiring further research and development for processing languages with complex morphosyntax are discussed in the outlook. This paper was developed from the presentation “IT integrated environment for optimising the translation of legislative documents in the EP“ by Pascale Chartier-Brun at the workshop “Europäische Rechtslinguistik und Digitale Möglichkeiten / EU Legal Linguistics and Digital Perspectives“, held at the University of Cologne July 7th/8th, 2017

    Data Mining for Web-Enabled Electronic Business Applications

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    Web-enabled electronic business is generating massive amounts of data on customer purchases, browsing patterns, usage times, and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for Web-enabled electronicbusiness. Copyright Idea Group Inc

    An Ontology-Driven Methodology To Derive Cases From Structured And Unstructured Sources

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    The problem-solving capability of a Case-Based Reasoning (CBR) system largely depends on the richness of its knowledge stored in the form of cases, i.e. the CaseBase (CB). Populating and subsequently maintaining a critical mass of cases in a CB is a tedious manual activity demanding vast human and operational resources. The need for human involvement in populating a CB can be drastically reduced as case-like knowledge already exists in the form of databases and documents and harnessed and transformed into cases that can be operationalized. Nevertheless, the transformation process poses many hurdles due to the disparate structure and the heterogeneous coding standards used. The featured work aims to address knowledge creation from heterogeneous sources and structures. To meet this end, this thesis presents a Multi-Source Case Acquisition and Transformation Info-Structure (MUSCATI). MUSCATI has been implemented as a multi-layer architecture using state-of-the-practice tools and can be perceived as a functional extension to traditional CBR-systems. In principle, MUSCATI can be applied in any domain but in this thesis healthcare was chosen. Thus, Electronic Medical Records (EMRs) were used as the source to generate the knowledge. The results from the experiments showed that the volume and diversity of cases improves the reasoning outcome of the CBR engine. The experiments showed that knowledge found in medical records (regardless of structure) can be leveraged and standardized to enhance the (medical) knowledge of traditional medical CBR systems. Subsequently, the Google search engine proved to be very critical in “fixing” and enriching the domain ontology on-the-fly
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