How to Improve the Production Process for interactive TV with semi-formal Methods

The central question for this paper is how to improve the production process by closing the gap between industrial designers and software engineers of television(TV)-based User Interfaces (UI) in an industrial environment. Software engineers are highly interested whether one UI design can be converted into several fully functional UIs for TV products with different screen properties. The aim of the software engineers is to apply automatic layout and scaling in order to speed up and improve the production process. However, the question is whether a UI design lends itself for such automatic layout and scaling. This is investigated by analysing a prototype UI design done by industrial designers. In a first requirements study, industrial designers had created meta-annotations on top of their UI design in order to disclose their design rationale for discussions with software engineers. In a second study, five (out of ten) industrial designers assessed the potential of four different meta-annotation approaches. The question was which annotation method industrial designers would prefer and whether it could satisfy the technical requirements of the software engineering process. One main result is that the industrial designers preferred the method they were already familiar with, which therefore seems to be the most effective one although the main objective of automatic layout and scaling could still not be achieved.

Introduction: Ontology Engineering in a Networked World

While ontology engineering is rapidly entering the mainstream, expert ontology engineers are a scarce resource. Hence, there is a need for practical methodologies and technologies, which can assist a variety of user types with ontology development tasks. To address this need, this book presents a scenario-based methodology, the NeOn Methodology, which provides guidance for all main activities in ontology engineering. The context in which we consider these activities is that of a networked world, where reuse of existing resources is commonplace, ontologies are developed collaboratively, and managing relationships between ontologies becomes an essential aspect of the ontological engineering process. The description of both the methodology and the ontology engineering activities is grounded in a comprehensive software environment, the NeOn Toolkit and its plugins, which provides integrated support for all the activities described in the book. Here we provide an introduction for the whole book, while the rest of the content is organized into 4 parts: (1) the NeOn Methodology Framework, (2) the set of ontology engineering activities, (3) the NeOn Toolkit and plugins, and (4) three use cases. Primary goals of this book are (a) to disseminate the results from the NeOn project in a structured and comprehensive form, (b) to make it easier for students and practitioners to adopt ontology engineering methods and tools, and (c) to provide a textbook for undergraduate and postgraduate courses on ontology engineering.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Process optimization of an aerobic upflow sludge blanket reactor system

Response of an aerobic upflow sludge blanket (AUSB) reactor system to the changes in operating conditions was investigated by varying two principle operating variables: the oxygenation pressure and the flow recirculation rate. The oxygenation pressure was varied between 0 and 25 psig (relative), while flow recirculation rates were between 1,300 and 600% correspondingly. The AUSB reactor system was able to handle a volumetric loading of as high as 3.8 kg total organic carbon (TOC)/m(3) day, with a removal efficiency of 92%. The rate of TOC removal by AUSB was highest at a pressure of 20 psig and it decreased when the pressure was increased to 25 psig and the flow recirculation rate was reduced to 600%. The TOC removal rate also decreased when the operating pressure was reduced to 0 and 15 psig, with corresponding increase in flow recirculation rates to 1,300 and 1,000%, respectively. Maintenance of a high dissolved oxygen level and a high flow recirculation rate was found to improve the substrate removal capacity of the AUSB system. The AUSB system was extremely effective in retaining the produced biomass despite a high upflow velocity and the overall sludge yield was only 0.24-0.32 g VSS/g TOC removed. However, the effluent TOC was relatively high due to the system's operation at a high organic loading.

It's all in the game: Teaching software process concepts

A major challenge in teaching software engineering to undergraduates is that most students have limited industry experience, so the problems addressed are unknown and hence unappreciated. Issues of scope prevent a realistic software engineering experience, and students often graduate with a simplistic view of software engineering’s challenges. Problems and Programmers (PnP) is a competitive, physical card game that simulates the software engineering process from requirements specification to product delivery. Deliverables are abstracted, allowing a focus on process issues and for lessons to be learned in a relatively short time. The rules are easy to understand and the game’s physical nature allows for face-to-face interaction between players. The game’s developers have described PnP in previous publications, but this paper reports the game’s use within a larger educational scheme. Students learn and play PnP, and then are required to create a software requirements specification based on the game. Finally, students reflect on the game’s strengths and weaknesses and their experiences in an individual essay. The paper discusses this approach, students’ experiences and overall outcomes, and offers an independent, critical look at the game, its use, and potential improvements.

Traceability Management Architectures Supporting Total Traceability in the Context of Software Engineering

In the area of Software Engineering, traceability is defined as the capability to track requirements, their evolution and transformation in different components related to engineering process, as well as the management of the relationships between those components. However the current state of the art in traceability does not keep in mind many of the elements that compose a product, specially those created before requirements arise, nor the appropriated use of traceability to manage the knowledge underlying in order to be handled by other organizational or engineering processes. In this work we describe the architecture of a reference model that establishes a set of definitions, processes and models which allow a proper management of traceability and further uses of it, in a wider context than the one related to software development.

Framework for enterprise systems engineering

This research aimed at developing a research framework for the emerging field of enterprise systems engineering (ESE). The framework consists of an ESE definition, an ESE classification scheme, and an ESE process. This study views an enterprise as a system that creates value for its customers. Thus, developing the framework made use of system theory and IDEF methodologies. This study defined ESE as an engineering discipline that develops and applies systems theory and engineering techniques to specification, analysis, design, and implementation of an enterprise for its life cycle. The proposed ESE classification scheme breaks down an enterprise system into four elements. They are work, resources, decision, and information. Each enterprise element is specified with four system facets: strategy, competency, capacity, and structure. Each element-facet combination is subject to the engineering process of specification, analysis, design, and implementation, to achieve its pre-specified performance with respect to cost, time, quality, and benefit to the enterprise. This framework is intended for identifying research voids in the ESE discipline. It also helps to apply engineering and systems tools to this emerging field. It harnesses the relationships among various enterprise aspects and bridges the gap between engineering and management practices in an enterprise. The proposed ESE process is generic. It consists of a hierarchy of engineering activities presented in an IDEF0 model. Each activity is defined with its input, output, constraints, and mechanisms. The output of an ESE effort can be a partial or whole enterprise system design for its physical, managerial, and/or informational layers. The proposed ESE process is applicable to a new enterprise system design or an engineering change in an existing system. The long-term goal of this study aims at development of a scientific foundation for ESE research and development.

Framework for Enterprise Systems Engineering

This research aimed at developing a research framework for the emerging field of enterprise systems engineering (ESE). The framework consists of an ESE definition, an ESE classification scheme, and an ESE process. This study views an enterprise as a system that creates value for its customers. Thus, developing the framework made use of system theory and IDEF methodologies. This study defined ESE as an engineering discipline that develops and applies systems theory and engineering techniques to specification, analysis, design, and implementation of an enterprise for its life cycle. The proposed ESE classification scheme breaks down an enterprise system into four elements. They are work, resources, decision, and information. Each enterprise element is specified with four system facets: strategy, competency, capacity, and structure. Each element-facet combination is subject to the engineering process of specification, analysis, design, and implementation, to achieve its pre-specified performance with respect to cost, time, quality, and benefit to the enterprise. This framework is intended for identifying research voids in the ESE discipline. It also helps to apply engineering and systems tools to this emerging field. It harnesses the relationships among various enterprise aspects and bridges the gap between engineering and management practices in an enterprise. The proposed ESE process is generic. It consists of a hierarchy of engineering activities presented in an IDEF0 model. Each activity is defined with its input, output, constraints, and mechanisms. The output of an ESE effort can be a partial or whole enterprise system design for its physical, managerial, and/or informational layers. The proposed ESE process is applicable to a new enterprise system design or an engineering change in an existing system. The long-term goal of this study aims at development of a scientific foundation for ESE research and development.

Process inventory and pollution prevention overview for the citrus industry

Florida citrus represents approximately 70 percent of the industry production in the United States; therefore, any associated agricultural and industrial contamination is of concern and a focus of attention. The use of synthetic organic chemicals has become a farmer's necessity in order to supply consumers with high quality products, free of pest damage. However, industrial citrus wastes and chemical residual levels worry not only government agencies but also consumers since they indicate a serious habitat risk. This study assesses citrus industrial processes and the paths that chemical substances follow from the time the citrus seed is planted until consumers get a final product as either fresh fruit or processed product. The study is built on information from United States Environmental Protection Agency (US EPA) manuals, Dade County Environmental Resources Management (DERM) inspection records, United States Food and Drug Administration (US FDA) regulations, Florida standards, journal publications, and research reports. Pollution prevention (P2 or prevention-of-pollution) alternatives are identified; alternatives are proposed, evaluated, and included. Strategies are described and pollution prevention opportunities proposed to minimize citrus wastes generation, chemical residuals in products, their environmental impact and health risk aspects while maximizing product quality.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.

COORDINSPECTOR: a tool for extracting coordination data from legacy code

More and more current software systems rely on non trivial coordination logic for combining autonomous services typically running on different platforms and often owned by different organizations. Often, however, coordination data is deeply entangled in the code and, therefore, difficult to isolate and analyse separately. COORDINSPECTOR is a software tool which combines slicing and program analysis techniques to isolate all coordination elements from the source code of an existing application. Such a reverse engineering process provides a clear view of the actually invoked services as well as of the orchestration patterns which bind them together. The tool analyses Common Intermediate Language (CIL) code, the native language of Microsoft .Net Framework. Therefore, the scope of application of COORDINSPECTOR is quite large: potentially any piece of code developed in any of the programming languages which compiles to the .Net Framework. The tool generates graphical representations of the coordination layer together and identifies the underlying business process orchestrations, rendering them as Orc specifications

On the discovery of business processes orchestration patterns

COORDINSPECTOR is a Software Tool aiming at extracting the coordination layer of a software system. Such a reverse engineering process provides a clear view of the actually invoked services as well as the logic behind such invocations. The analysis process is based on program slicing techniques and the generation of, System Dependence Graphs and Coordination Dependence Graphs. The tool analyzes Common Intermediate Language (CIL), the native language of the Microsoft .Net Framework, thus making suitable for processing systems developed in any .Net Framework compilable language. COORDINSPECTOR generates graphical representations of the coordination layer together with business process orchestrations specified in WSBPEL 2.0

Melhoria do processo produtivo na indústria do mobiliário

Com o aumento da concorrência, a crise económica que atravessamos e com a conquista de novos mercados no horizonte, surge a necessidade de responder a essas situações com uma melhor prestação do sistema produtivo no sentido de reduzir tempos de produção e de baixar os custos dos mesmos, podendo assim dar uma resposta mais rápida e competitiva às expectativas dos clientes. Esta dissertação tem como objectivo analisar todo o sistema produtivo da empresa, desenvolvendo um estudo adequado ao sistema actual de fabrico de mobiliário, propondo melhorias que proporcionem uma melhor organização e uma redução dos custos de fabrico. Fez-se também um levantamento do sistema de planeamento existente, detectando pontos de melhoria que, para além de um maior controlo e organização, permitem, em alguns aspectos, a melhoria do sistema produtivo. Foi apresentada uma proposta de um processo de reengenharia aplicado ao sistema produtivo, tendo em vista implementar as medidas de melhoria, indo ao encontro das situações identificadas, tornando assim o sistema produtivo mais eficiente.