A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets.

No description available.

[pt] Esta tese aborda o tema da linguagem infográfica, um dos produtos do Design da Informação, no Brasil. Os infográficos são amplamente usados como ferramenta de comunicação pela mídia, com o intuito de transmitir informações de modo sintético, claro e atraente por meio de representações visuais diagramáticas multimodais, onde coexistem diversas modalidades comunicativas, que combinam linguagem verbal e não verbal. A pesquisa analisa o persistente problema da nomenclatura na infografia, bem como tipologias relevantes. Propõe uma linha do tempo da evolução dos infográficos, traça um breve histórico da infografia no Brasil e examina sua participação na maior premiação de infografia mundial (Malofiej) ao longo dos últimos 26 anos. Investiga e analisa dados acerca do ensino da infografia no país, observando a presença de disciplinas ligadas ao tema nos cursos superiores de jornalismo e design. Os aportes teóricos se encontram nas obras de Cairo, De Pablos, Sancho, Tufte, Moraes e Kanno, que desenvolveram investigações acerca da linguagem infográfica e visualização da informação. Foram realizadas pesquisas bibliográficas, documentais e entrevistas junto a profissionais premiados internacionalmente e professores, como forma de mapear conceitos, pressupostos teóricos e questões relacionadas ao atual cenário da infografia. O trabalho visou reunir dados que permitam a construção de um diagnóstico da infografia no Brasil na esfera acadêmica e sua práxis, algo não revelado de forma integrada por pesquisas anteriores, bem como oferecer uma reflexão acerca do papel da infografia e sua perspectiva como ferramenta informativa no cenário contemporâneo nacional. Como resultado, elaborou-se uma série de infográficos que traçam um panorama da infografia brasileira, de modo a disponibilizar dados para a comunidade de infografistas, jornalistas, designers e professores do campo do Design da Informação. / [en] This thesis addresses the theme of infographic language, one of the products of Information Design, in Brazil. Infographics are widely used as a communication tool by the media, with the purpose of transmitting information in a synthetic, clear and attractive way through multimodal diagrammatic visual representations, combining verbal and nonverbal language. The research analyzes the persistent problem of nomenclature of the infographics, as well as relevant typologies. It proposes a timeline of the evolution of infographics, traces a brief history of infographics in Brazil, and examines its participation in the biggest infographics award worldwide (Malofiej) over the last 26 years. It analyzes data on the teaching of infographics in the country, observing the presence of disciplines related to the subject in journalism and design graduations. The theoretical contributions are found in the works of Cairo, De Pablos, Sancho, Tufte, Moraes and Kanno, who have developed research on infographics and information visualization. Bibliographical and documentary research as well as interviews with awarded professionals and professors were carried out as a way of mapping concepts, theoretical assumptions and issues related to the current infographic scenario. The aim of this study was to gather data that allow the construction of a diagnosis of infographics in Brazil in the academic sphere and its praxis, something not revealed in an integrated way by previous researches, as well as offer a reflection about the role of infographics and its perspective as an information tool. As a result, a series of infographics were created, providing data to the community of journalists, designers and professors in the field of Information Design.

A trustworthy and consistent data system is crucial for monitoring and reducing carbon emissions from road maintenance operations. Developing a national data reporting system requires technical support and a systemic plan involving multiple stakeholders to implement the standard. In Sweden, Trafikverket, the Swedish transport agency, recently initiated a project that proposed a solution based on the BEAst standard and outlined the current data collection methods for road maintenance. The BEAst standard is an agreed industry-driven information standard that promotes machine-readable information communication, effectively reduces costs, and increases efficiency by streamlining communication within the industry. This is to address the critical need for a trustworthy data system to monitor and reduce carbon emissions from road maintenance operations. Although the datasystem has high potential to identify the sources of carbon emissions and create mitigation measures by precisely gathering fuel use data throughout operations and maintenance activities. There are many challenges in integrating data from diverse sources into a consistent system revealed several obstacles, including differences in CO2 emissions reported by different systems, human factors affecting data quality,and limited access to cloud services. To address these challenges, this study proposes a new data reporting mechanism which requires a detailed specification of reporting parameters covering content, format, resolution, and reporting frequency using BEAst standards.

Enhancing operational efficiency and competitiveness in modern manufacturing environment requires the incorporation of Industry 4.0 technology. The Digital Twin is one of its enablers, and it is a transformative tool that can be used to optimize systems, processes, and real assets by using virtual models synchronized with real-time data. However, it can be difficult to fully utilize the potential benefits of the massive volumes of data companies generate. By tailoring Digital Twins to the unique data requirements of various user profiles inside companies, this study seeks to overcome this difficulty and enable efficient data access and well-informed decision-making. This study, which was carried out at Robert Bosch España Madrid, aimed to comprehend the unique data requirements of several departments, such as Engineering, Maintenance, and Production. To learn more about department-specific data needs, questionnaires and interviews were used as part of a qualitative research project. The creation of a customized Digital Twin visualizer prototype for the USS6 shopfloor was influenced by these findings. The research findings indicated some differences in the data needs of departments, highlighting the significance of preserving unique profiles in the Digital Twin Visualizer while encouraging cooperation and synergy between departments. Production requires real-time key performance indicator (KPI) monitoring, including cycle time and other production KPIs. The Maintenance department needs to track equipment maintenance events, Mean Time to Repair (MTTR), and Mean Time Between Failures (MTBF). Engineering requires data more related to machines values, status and performance. Most importantly, these findings have significant implications outside of the sensor manufacturing industry; they offer insightful knowledge that is applicable to many different industries. Organizations across diverse industries can enhance their operational performance and decision-making capacities by customizing best practices to suit their unique settings through the application of broader insights gained from these findings. This knowledge-sharing across industries is essential to pushing Industry 4.0 adoption and promoting organizational performance in the digital age. In conclusion, this study enhances knowledge on customized Digital Twin implementations and emphasizes how data-driven insights and well-informed decision-making can be leveraged to create operational excellence across industries. / För att öka den operativa effektiviteten och konkurrenskraften i moderna tillverkningsmiljöer krävs att Industri 4.0-tekniken införlivas. Den Digital Twin (digital tvilling) är ett av verktygen för detta, och det är ett transformativt verktyg som kan användas för att optimera system, processer och egendomar genom att använda virtuella modeller som synkroniseras med realtidsdata. Det kan dock vara svårt att fullt ut utnyttja de potentiella fördelarna med den enorma datavolym som företag genererar. Genom att skräddarsy Digital Twins efter de unika databehov som olika användarprofiler inom företag har, försöker denna studie övervinna denna svårighet och tillhåta effektiv datatillgång och välinformerat beslutsfattande. Målet med den här studien, som genomfördes på Robert Bosch España Madrid, var att förstå de unika databehov på, till exempel, Tekniksavdelningen, Underhållsavdelningen och Tillverksningsavdelningen. För att få veta mer om avdelningsspecifika databehov användes enkäten och intervjuer som delar av ett kvalitativt forskningsprojekt. Dessa resultat påverkade skapandet av en skräddarsydd prototyp av Digital Twin-visualiseraren för USS6:s verkstadsgolv. Forskningsresultaten visade skillnader i avdelningarnas databehov, vilket belyser vikten av att bevara unika profiler i Digital Twin Visualizer och samtidigt som uppmana avdelningarna att samarbeta i synergi mellan avdelningarna. Tillverkningsavdelningen behöver övervakning av KPI:er (Key Performance Indicators, nyckeltal) i realtid, inklusive genomslopptid och andra tillverknings-KPI:er. Underhållsavdelningen behöver overväkning av underhållshändelser för utrustningen, MTTR (eng. Mean Time To Repair, genomsnittlig tid för reparation) och MTBF (eng. Mean Time Between Failures, medeltid mellan fel). Teknikavdelningen behöver data som är relaterade till maskinernas värden, status och prestanda. Dessa resultat har betydande konsekvenser utanför sensortillverkningsindustrin; de erbjuder insiktsfull kunskap som är tillämplig på många olika branscher. Organisationer i olika branscher kan förbättra sina operativa resultat och sin beslutstagande förmåga genom att anpassa bästa praxis till sina unika miljöer med hjälp av de bredare insikter som dessa resultat ger. Detta kunskapsutbyte mellan branscher är avgörande för att driva på införandet av Industri 4.0 och främja organisationers prestanda i den digitala tidsåldern. Till sist ökar denna studie kunskapen om skräddarsydda implementeringar av Digital Twin och betonar hur datadrivna insikter och välinformerat beslutsfattande kan utnyttjas för att skapa operativ excellens i olika branscher.

This thesis was conducted to develop a user interface for the visualization of factory data through HMS Networks' Gateway Communicator. The objective of the project was to enhance decision-making in industrial processes through efficient data transfer and visualization. The project involved the design and implementation of a user-friendly and customizable interface that integrates real-time data from industrial machinery. To achieve these goals, the Angular framework was used in conjunction with HTML, CSS, and TypeScript to create a dynamic and responsive user interface. The methodology included user-centered design, ensuring that the final product not only met technical specifications but was also intuitive for end-users. Among the features implemented were dynamic components that allowed for the visualization of critical data such as temperature and pressure through customizable widgets and dialogue components. The results of the project included successful performance testing, unit tests, and usability tests that ensured the system's effectiveness in visualizing data in a manner understandable to users. The conclusion of the work is that an effective user interface can increase operational efficiency through improved data visualization and interaction, which in turn can lead to more informed decisions in industrial environments. / Detta examensarbete utfördes för att utveckla ett användargränssnitt för visualisering av fabriksdata med hjälp av HMS Networks Gateway Communicator. Syftet med projektet var att förbättra beslutsfattandet i industriella processer med hjälp av visualisering av data. Projektet innefattade design och implementering av ett användarvänligt och anpassningsbart gränssnitt som integrerar realtidsdata från industriella maskiner. För att uppnå dessa mål användes Angular ramverket tillsammans med HTML, CSS och TypeScript för att skapa ett dynamiskt användargränssnitt. Metodiken inkluderade användarcentrerad design, vilket säkerställde att slutprodukten inte bara uppfyllde tekniska specifikationer utan också var intuitiv för slutanvändarna. Bland funktionerna som implementerades finns dynamiska komponenter som tillåter visualisering av kritiska data som temperatur och tryck genom anpassningsbara komponenter. Resultaten från projektet inkluderade ett framgångsrikt prestandatest, enhetstester och användbarhetstest som säkerställde systemets effektivitet i att visualisera data på ett sätt som är begripligt för användarna. Slutsatsen av detta arbete är att ett effektivt användargränssnitt främjar datavisualisering och interaktivitet, vilket i sin tur bidrar till att fatta välinformerade beslut inom industriella miljöer.

Power limits on future high-performance computing (HPC) systems will constrain applications. However, HPC applications do not consume constant power over their lifetimes. Thus, applications assigned a fixed power bound may be forced to slow down during high-power computation phases, but may not consume their full power allocation during low-power I/O phases. This paper explores algorithms that leverage application semantics-phase frequency, duration and power needs-to shift unused power from applications in I/O phases to applications in computation phases, thus improving system-wide performance. We design novel techniques that include explicit staggering of applications to improve power shifting. Compared to executing without power shifting, our algorithms can improve average performance by up to 8% or improve performance of a single, high-priority application by up to 32%.

[pt] A tese discute a relação existente entre a criatividade e o design thinking e o universo da infografia e da visualização de dados, com destaque para a criação de um método de construção de infográficos a partir dos estudos empreendidos nas referidas áreas, baseados em bibliografia compatível e atual. Através principalmente da lógica do visual thinking, é elaborado um método de construção de infográficos. Além disso, o próprio infográfico aparece como possível ferramenta de pensamento visual dentro do design thinking, otimizando as possibilidades desse processo. A infografia e a visualização de dados disponibilizam um novo e vasto campo de trabalho para o designer em parceria com outros profissionais. No entanto, existe uma carência de métodos de construção e desenvolvimento de infográficos. O método de construção proposto pela tese para preencher essa lacuna identificada, até pela minha própria prática profissional de ensino, poderá ser aplicado tanto por designers já atuantes no mercado quanto ensinado a estudantes de Design ou Comunicação. / [en] The thesis discusses the relationship between creativity and design thinking and the universe of infographics and data visualization, proposing a method of construction of infographics from studies in these areas, based on current and compatible bibliography. The method of development of infographics follows the logic that belongs to visual thinking. In addition, the infographics itself appears as a possible tool for visual thinking within the design thinking, optimizing the possibilities of this process. Infographics and data visualization provide a vast new field of work for the designer in partnership with other professionals. However, there is a lack of methods for building and developing infographics. The method of construction proposed by the thesis to fill this gap identified, including my own experience as a professor in this area, can be applied both by designers who are already in the market as can be taught to students of Design and Communication.

Dans de nombreux problèmes d’apprentissage automatique et de reconnaissance des formes, il y a toujours un besoin de fonctions métriques appropriées pour mesurer la distance ou la similarité entre des données. La fonction métrique est une fonction qui définit une distance ou une similarité entre chaque paire d’éléments d’un ensemble de données. Dans cette thèse, nous proposons une nouvelle methode, Triangular Similarity Metric Learning (TSML), pour spécifier une fonction métrique de données automatiquement. Le système TSML proposée repose une architecture Siamese qui se compose de deux sous-systèmes identiques partageant le même ensemble de paramètres. Chaque sous-système traite un seul échantillon de données et donc le système entier reçoit une paire de données en entrée. Le système TSML comprend une fonction de coût qui définit la relation entre chaque paire de données et une fonction de projection permettant l’apprentissage des formes de haut niveau. Pour la fonction de coût, nous proposons d’abord la similarité triangulaire (Triangular Similarity), une nouvelle similarité métrique qui équivaut à la similarité cosinus. Sur la base d’une version simplifiée de la similarité triangulaire, nous proposons la fonction triangulaire (the triangular loss) afin d’effectuer l’apprentissage de métrique, en augmentant la similarité entre deux vecteurs dans la même classe et en diminuant la similarité entre deux vecteurs de classes différentes. Par rapport aux autres distances ou similarités, la fonction triangulaire et sa fonction gradient nous offrent naturellement une interprétation géométrique intuitive et intéressante qui explicite l’objectif d’apprentissage de métrique. En ce qui concerne la fonction de projection, nous présentons trois fonctions différentes: une projection linéaire qui est réalisée par une matrice simple, une projection non-linéaire qui est réalisée par Multi-layer Perceptrons (MLP) et une projection non-linéaire profonde qui est réalisée par Convolutional Neural Networks (CNN). Avec ces fonctions de projection, nous proposons trois systèmes de TSML pour plusieurs applications: la vérification par paires, l’identification d’objet, la réduction de la dimensionnalité et la visualisation de données. Pour chaque application, nous présentons des expérimentations détaillées sur des ensembles de données de référence afin de démontrer l’efficacité de notre systèmes de TSML. / In many machine learning and pattern recognition tasks, there is always a need for appropriate metric functions to measure pairwise distance or similarity between data, where a metric function is a function that defines a distance or similarity between each pair of elements of a set. In this thesis, we propose Triangular Similarity Metric Learning (TSML) for automatically specifying a metric from data. A TSML system is loaded in a siamese architecture which consists of two identical sub-systems sharing the same set of parameters. Each sub-system processes a single data sample and thus the whole system receives a pair of data as the input. The TSML system includes a cost function parameterizing the pairwise relationship between data and a mapping function allowing the system to learn high-level features from the training data. In terms of the cost function, we first propose the Triangular Similarity, a novel similarity metric which is equivalent to the well-known Cosine Similarity in measuring a data pair. Based on a simplified version of the Triangular Similarity, we further develop the triangular loss function in order to perform metric learning, i.e. to increase the similarity between two vectors in the same class and to decrease the similarity between two vectors of different classes. Compared with other distance or similarity metrics, the triangular loss and its gradient naturally offer us an intuitive and interesting geometrical interpretation of the metric learning objective. In terms of the mapping function, we introduce three different options: a linear mapping realized by a simple transformation matrix, a nonlinear mapping realized by Multi-layer Perceptrons (MLP) and a deep nonlinear mapping realized by Convolutional Neural Networks (CNN). With these mapping functions, we present three different TSML systems for various applications, namely, pairwise verification, object identification, dimensionality reduction and data visualization. For each application, we carry out extensive experiments on popular benchmarks and datasets to demonstrate the effectiveness of the proposed systems.

Dans ce travail de recherche, nous nous intéressons à la question de la pérennité de l’usage des systèmes de gestion des connaissances (SGC) dans les entreprises. Les SGC sont ces environnements informatiques qui sont mis en place dans les entreprises pour mutualiser et construire l’expertise commune grâce aux collaborateurs. Le constat montre que, malgré la rigueur employée par les entreprises pour la mise en œuvre de ces SGC, le risque d’échec des initiatives de gestion des connaissances, notamment lié à l’acceptation de ces environnements par les utilisateurs professionnels ainsi qu’à leur usage continu et durable, reste d’actualité. La persistance et l’ampleur de ce constat dans les entreprises a motivé notre intérêt d’apporter une contribution à cette question générale de recherche. Comme propositions de réponse à cette problématique, nous avons donc 1) dégagé à partir de l’état de l’art, quatre facettes qui sont requises pour favoriser l’usage pérenne d’une plateforme gérant la connaissance ; 2) proposé un modèle théorique de régulation mixte qui unifie des outils de stimulation pour l’autorégulation et des outils soutenant l’accompagnement au changement et qui permet la mise en œuvre continue des différents facteurs stimulants l’usage pérenne des SGC ; 3) proposé une méthodologie de conception, adaptée à ce modèle et basée sur les concepts Agile, qui intègre une méthode d’évaluation mixte de la satisfaction et de l’usage effectif ainsi que des outils d’IHM pour l’exécution des différentes itérations de notre méthodologie ; 4) implémenté la méthodologie en contexte réel, à la Société du Canal de Provence, ce qui nous a permis de tester sa faisabilité et de proposer des ajustements/recommandations génériques aux concepteurs pour son application en contexte. L’outil résultant de notre implémentation a reçu un accueil positif par les utilisateurs en termes de satisfaction et d’usages. / In this research, we are interested in the question of sustainability of the use of knowledge management systems (KMS) in companies. KMS are those IT environments that are set up in companies to share and build common expertise through collaborators. Findings show that, despite the rigor employed by companies in the implementation of these KMS, the risk of knowledge management initiatives being unsuccessful, particularly related to the acceptance and continuous use of these environments by users remains prevalent. The persistence of this fact in companies has motivated our interest to contribute to this general research question. As contributions to this problem, we have 1) identified from the state of the art, four facets that are required to promote the perennial use of a platform managing knowledge; 2) proposed a theoretical model of mixed regulation that unifies tools for self-regulation and tools to support change, and allows the continuous implementation of the various factors that stimulate the sustainable use of CMS; 3) proposed a design methodology, adapted to this model and based on the Agile concepts, which incorporates a mixed evaluation methodology of satisfaction and effective use as well as CHI tools for the completion of different iterations of our methodology; 4) implemented the methodology in real context at the Société du Canal de Provence, which allowed us to test its feasibility and propose generic adjustments / recommendations to designers for its application in context. The tool resulting from our implementation was positively received by the users in terms of satisfaction and usages.