Changes in power system operating conditions cause dynamic changes in angle and frequency. These disturbances propagate throughout the system area with finite speed. This propagation takes the form of a traveling wave whose arrival time at a particular point in the system can be observed using a wide-area measurement system (WAMS). Observations of these waves both through simulation and measurement data have demonstrated several factors that influence the speed at which a disturbance propagates through a system. Results of this testing are presented which demonstrate dependence on generator inertia, damping and line impedance. Considering a power system as an area with and uneven distribution of these parameters it is observed that a disturbance will propagate throughout a system at different rates in differing directions. This knowledge has applications in locating the originating point of a system disturbance, understanding the overall dynamic response of a power system, and determining the dependencies between various parts of that system. A simplified power system simulator is developed using the swing equation and system power flow equations. This simplified modeling technique captures the phenomenon of traveling electromechanical waves and demonstrates the same dependencies as data derived from measurements and commercial power system simulation packages. The ultimate goal of this research is develop a methodology to approximate a real system with this simplified wave propagation model. In this architecture each measurement point would represent a pseudo-bus in the model. This procedure effectively lumps areas of the system into one equivalent bus with appropriately sized generators and loads. With the architecture of this reduced network determined its parameters maybe estimated so as to provide a best fit to the measurement data. Doing this effectively derives a data-driven equivalent system model. With an appropriate equivalent model for a given system determined, incoming measurement data can be processed in real time to provide an indication of the system operating point. Additionally as the system state is read in through measurement data future measurements values along the same trajectory can be estimated. These estimates of future system values can provide information for advanced control and protection schemes. Finally a procedure for the identification and extraction of inter-area oscillations is developed. The dominant oscillatory frequency is identified from an event region then fit across the surrounding dataset. For each segment of this data set values of amplitude, phase and damping are derived for each measurement vector. Doing this builds up a picture of how the oscillation evolves over time and responds to system conditions. These results are presented in a graphical format as a movie tracking the modal phasors over time. Examples derived from real world measurement data are presented. / Ph. D.

Master of Science / Department of Statistics / Leigh Murray / Using remotely sensed reflectance data is an appealing tool for controlling invasive species of grasses by rangeland managers. Recent developments in functional data analysis include the functional boxplot (FBP) which is shown here to be a useful tool in the visualization of reflectance data. Functional boxplots are a novel method of visually inspecting functional data and determining the presence of outliers in the data. Implementation and interpretation of FBPs are both straightforward and intuitive. The goal of this study is to examine the use of FBPs for visualizing reflectance data, and to determine the efficacy of using the FBP to distinguish between native tall grasses and invasive Old World Bluestem (OWB, Bothriochloa spp.) monocultures in a Kansas prairie. Validation trials were conducted in order to determine the stability of the FBP when used to analyze spectral data. FBPs were shown to be highly stable for use with both native and OWB grasses at all times and subsets of wavelengths tested. Identification trials were conducted by introducing a single OWB observation to a test set of native tall grass observations and constructing a FBP. Results indicate that using observations recorded early in the growing season, the functional boxplot is able to successfully identify the OWB observation as an outlier in a test set of native tall grass observations with an estimated probability 100% and 95.45% when considering the visible and cellular spectrums, respectively. A 95% lower bound for the probability of successfully identifying the OWB observation using the cellular spectrum in May is found to be 89.67%.

Visualização de dados significa o mapeamento e a apresentação de dados em gráficos através da manipulação de variáveis visuais - altura, largura, frequência, cor, posição da forma gráfica - para informar e comunicar um assunto subjacente. A presente investigação é o estudo de como a informação, visualização e divulgação de dados e estatísticas relativas ao bem-estar humano podem conscientizar a sociedade civil e gestores públicos a promoverem melhorias na qualidade de vida e em políticas públicas. Bem-estar é um conceito multidimensional que envolve todas as dimensões da vida - desnível social, estresse, início da vida, exclusão social, pobreza, mobilidade. Existem muitos métodos para mensurar o bem-estar humano, pode ser avaliado pelas abordagens objetiva e subjetiva. Sendo assim, nesta investigação é estudado o bem-estar humano: origem, significado, definições e descrições. Os instrumentos usados para mensurar os indicadores de bem-estar são investigados. A aplicação da pesquisa consiste na criação de um modelo de dados de indicadores de bem-estar aplicado à cidade de São Paulo baseado nos Objetivos de Desenvolvimento Sustentável da ONU. Dados são coletados de instituições governamentais para a criação de um sistema de visualização - na internet <usp.br/mappingwellbeing/visualize> - através de gráficos de dados dinâmicos. / Data visualization is the mapping and representation of data into graphs by manipulating visual variables - height, width, frequency, color, position of the graphical form - to inform and communicate an underlying information. This research studies how information, visualization, and dissemination of human well-being data e statistics can raise awareness among civil society and public managers to promote improvements in quality of life and public policies. Well-being is a multidimensional concept that involves all dimensions of life - social gap, stress, beginning of life, social exclusion, poverty, mobility. There are many methods to measure human well-being, can be analyzed as objective well-being and subjective well-being. Thus, in this research human well-being is studied: origin, meaning, definitions and descriptions; In the same way, the instruments used in its measurement. An inventory of indicators is created from the indexing and analysis of reports of national e international organizations. The applied research consists in the creation of a visualization system based on data model - with indicators of well-being applied to the São Paulo city - grounded from the UN Sustainable Development Goals. Data are collected from government institutions using the proposed framework to create an internet platform <usp.br/mappingwellbeing/visualize>.

Within the Swedish Police Authority, physical users’ actions within all systems that manage sensitive information, are registered and sent to an audit log. The audit log contains log entries that consist of information regarding the events that occur by the performing user. This means that the audit log continuously manages massive amounts of data which is collected, processed and stored. For the police authority, the audit log may be useful for proving a digital trail of something that has occurred. An audit log is based upon the collected data from a security log. Security logs can collect datafrom most of the available systems and applications. It provides the availability for the organizationto implement network surveillance over the digital assets where logs are collected in real-time whichenables the possibility to detect any intrusion over the network. Furthermore, additional assets thatlog events are generated from are security software, firewalls, operating systems, workstations,networking equipment, and applications. The actors in a court of law usually don’t possess the technical knowledge required to interpret alog events since they can contain variable names, unparsed data or undefined values. Thisemphasizes the need for a user-friendly artifact of the audit log events that facilitates its use. Researching a way of displaying the current data format and displaying it in an improvedpresentable manner would be beneficial as an academic research by producing a generalizablemodel. In addition, it would prove useful for the internal investigations of the police authority sinceit was formed by their needs.

Técnicas de projeção desempenham papel importante na análise e exploração de dados multidimensionais, já que permitem visualizar informações muitas vezes ocultas na alta dimensão. Esta tese explora o potencial destas técnicas para resolver problemas relacionados à: 1) identificação de agrupamentos e 2) busca por similaridade em dados multidimensionais. Para identificação de agrupamentos foi desenvolvida uma técnica de projeção local e interativa que, além de projetar dados com ótima preservação de distâncias, permite que o usuário modifique o layout da projeção, agrupando um número reduzido de amostras representativas no espaço visual, de acordo com suas características. Os mapeamentos produzidos tendem a seguir o layout das amostras organizadas pelo usuário, facilitando a organização dos dados e identificação de agrupamentos. Contudo, nem sempre é possível selecionar ou agrupar amostras com base em suas características visuais de forma confiável, principalmente quando os dados não são rotulados. Para estas situações, um novo método para identificação de agrupamentos baseado em projeção foi proposto, o qual opera no espaço visual, garantindo que os grupos obtidos não fiquem fragmentados durante a visualização. Além disso, é orientado por um mecanismo de amostragem determinístico, apto a identificar instâncias que representam bem o conjunto de dados como um todo e capaz de operar mesmo em conjuntos de dados desbalanceados. Para o segundo problema: busca por similaridade em dados multidimensionais, uma família de métricas baseada em classes foi construída para projetar os dados, com o objetivo de minimizar a dissimilaridade entre pares de objetos pertencentes à mesma classe e, ao mesmo tempo, maximizá-la para objetos pertencentes a classes distintas. As métricas classes-específicas são avaliadas no contexto de recuperação de imagens com base em conteúdo. Com o intuito de aumentar a precisão da família de métricas classes-específicas, outra técnica foi desenvolvida, a qual emprega a teoria dos conjuntos fuzzy para estimar um valor de incerteza que é transferido para a métrica, aumentando sua precisão. Os resultados confirmam a efetividade das técnicas desenvolvidas, as quais representam significativa contribuição na tarefa de identificação de grupos e busca por similaridade em dados multidimensionais. / Projection techniques play an important role in multidimensional data analysis and exploration, since they allow to visualize information frequently hidden in high-dimensional spaces. This thesis explores the potential of those techniques to solve problems related to: 1) clustering and 2) similarity search in multidimensional data. For clustering data, a local and interactive projection technique capable of projecting data with effective preservation of distances was developed. This one allows the user to manipulate a reduced number of representative samples in the visual space so as to better organize them. The final mappings tend to follow the layout of the samples organized by the user, therefore, the user can interactively steer the projection. This makes it easy to organize and group large data sets. However, it is not always possible to select or group samples visually, in a reliable manner, mainly when handling unlabeled data. For these cases, a new clustering method based on multidimensional projection was proposed, which operates in the visual space, ensuring that clusters are not fragmented during the visualization. Moreover, it is driven by a deterministic sampling mechanism, able to identify instances that are good representatives for the whole data set. The proposed method is versatile and robust when dealing with unbalanced data sets. For the second problem: similarity search in multidimensional data, we build a family of class-specific metrics to project data. Such metrics were tailored to minimize the dissimilarity measure among objects from the same class and, simultaneously to maximize the dissimilarity among objects in distinct classes. The class-specific metrics are assessed in the context of content-based image retrieval. With the aim of increasing the precision of the class-specific metrics, another technique was developed. This one, uses the fuzzy set theory to estimate a degree of uncertainty, which is embedded in the metric, increasing its precision. The results confirm the effectiveness of the developed techniques, which represent significant contributions for clustering and similarity search in multidimensional data.

Com o progresso da computação e popularização da Internet, a sociedade entrou na era da informação. Esta nova fase é marcada pela forma como produzimos e lidamos com a informação. Diariamente, são produzidos e armazenados milhares de Gigabytes de dados cujo valor é reduzido se a informação ali contida não puder ser transformada em conhecimento. Concomitante a este fato, o padrão da computação está caminhando para a miniaturização e acessibilidade com os dispositivos móveis. Estes novos equipamentos estão mudando o padrão de comportamento dos usuários que passam de leitores passivos a geradores de conteúdo. Neste contexto, este projeto de mestrado propõe a técnica de visualização de dados NMap e a ferramenta de visualização de dados web aplicável a dispositivo móvel SPCloud. A técnica NMap utiliza todo o espaço visual disponível para transmitir informações de grupos preservando a metáfora distância-similaridade. Teste comparativos com as principais técnicas do estado da arte mostraram que a técnica NMap tem melhores resultados em preservação de vizinhança com um tempo de processamento significativamente melhor. Este fato coloca a NMap como uma das principais técnicas de ocupação do espaço visual. A ferramenta SPCloud utiliza a NMap para visualizar notícias disponíveis na web. A ferramenta foi desenvolvida levando em consideração as características inerentes aos dispositivos moveis o que possibilita utiliza-la nestes equipamentos. Teste informais com usuários demonstraram que a ferramenta tem um bom desempenho para sumarizar grandes quantidades de notícias em um pequeno espaço visual. / With the development of computers and the increasing popularity of the Internet, our society has entered the information age. This era is marked by the way we produce and deal with information. Everyday, thousand of Gigabytes are stored, but their value is reduced if the data cannot be transformed into knowledge. Concomitantly, computing is moving towards miniaturization and affordability of mobile devices, which are changing users behavior who move from passive readers to content generators. In this context, in this master thesis we propose and develop a data visualization technique, called NMap, and a web data visualization tool for mobile devices, called SPCloud. NMap uses all available visual space to transmit information preserving the metaphor of distance-similarity between elements. Comparative evaluations were performed with the state of the art techniques and the result has shown that NMap produces better results of neighborhood preservation with a significant improvement in processing time. Our results place NMap as a major space-filling technique establishing a new state of the art. SPCloud, a tool which uses NMap to present news available on the web, was developed taking into account the inherent characteristics of mobile devices. Informal user tests revealed that SPCloud performs well to summarize large amounts of news in a small visual space.

A rápida evolução dos recursos computacionais vem permitindo que grandes conjuntos de dados sejam armazenados e recuperados. No entanto, a exploração, compreensão e extração de informação útil ainda são um desafio. Com relação às ferramentas computacionais que visam tratar desse problema, a Visualização de Informação possibilita a análise de conjuntos de dados por meio de representações gráficas e a Mineração de Dados fornece processos automáticos para a descoberta e interpretação de padrões. Apesar da recente popularidade dos métodos de visualização de informação, um problema recorrente é a baixa escalabilidade visual quando se está analisando grandes conjuntos de dados, resultando em perda de contexto e desordem visual. Com intuito de representar grandes conjuntos de dados reduzindo a perda de informação relevante, o processo de agregação visual de dados vem sendo empregado. A agregação diminui a quantidade de dados a serem representados, preservando a distribuição e as tendências do conjunto de dados original. Quanto à mineração de dados, visualização de informação vêm se tornando ferramental essencial na interpretação dos modelos computacionais e resultados gerados, em especial das técnicas não-supervisionados, como as de agrupamento. Isso porque nessas técnicas, a única forma do usuário interagir com o processo de mineração é por meio de parametrização, limitando a inserção de conhecimento de domínio no processo de análise de dados. Nesta dissertação, propomos e desenvolvemos uma metáfora visual baseada na TableLens que emprega abordagens baseadas no conceito de agregação para criar representações mais escaláveis para a interpretação de dados tabulares. Como aplicação, empregamos a metáfora desenvolvida na análise de resultados de técnicas de agrupamento. O ferramental resultante não somente suporta análise de grandes bases de dados com reduzida perda de contexto, mas também fornece subsídios para entender como os atributos dos dados contribuem para a formação de agrupamentos em termos da coesão e separação dos grupos formados. / The rapid evolution of computing resources has enabled large datasets to be stored and retrieved. However, exploring, understanding and extracting useful information is still a challenge. Among the computational tools to address this problem, information visualization techniques enable the data analysis employing the human visual ability by making a graphic representation of the data set, and data mining provides automatic processes for the discovery and interpretation of patterns. Despite the recent popularity of information visualization methods, a recurring problem is the low visual scalability when analyzing large data sets resulting in context loss and visual disorder. To represent large datasets reducing the loss of relevant information, the process of aggregation is being used. Aggregation decreases the amount of data to be represented, preserving the distribution and trends of the original dataset. Regarding data mining, information visualization has become an essential tool in the interpretation of computational models and generated results, especially of unsupervised techniques, such as clustering. This occurs because, in these techniques, the only way the user interacts with the mining process is through parameterization, limiting the insertion of domain knowledge in the process. In this thesis, we propose and develop the new visual metaphor based on the TableLens that employs approaches based on the concept of aggregation to create more scalable representations of tabular data. As application, we use the developed metaphor in the analysis of the results of clustering techniques. The resulting framework does not only support large database analysis but also provides insights into how data attributes contribute to clustering regarding cohesion and separation of the composed groups

Loading of data from slow persistent memory (disk storage) to main memory represents a bottleneck for current interactive visual data exploration applications, especially when applied to huge volumnes of data. Semantic caching of queries at the client-side is a recently emerging technology that can significantly improve the performance of such systems, though it may not in all cases fully achieve the near real-time responsiveness required by such interactive applications. We hence propose to augment the semantic caching techniques by applying prefetching. That is, the system predicts the user's next requested data and loads the data into the cache as a background process before the next user request is made. Our experimental studies confirm that prefetching indeed achieves performance improvements for interactive visual data exploration. However, a given prefetching technique is not always able to correctly predict changes in a user's navigation pattern. Especially, as different users may have different navigation patterns, implying that the same strategy might fail for a new user. In this research, we tackle this shortcoming by utilizing the adaptation concept of strategy selection to allow the choice of prefetching strategy to change over time both across as well as within one user session. While other adaptive prefetching research has focused on refining a single strategy, we instead have developed a framework that facilitates strategy selection. For this, we explored various metrics to measure performance of prefetching strategies in action and thus guide the adaptive selection process. This work is the first to study caching and prefetching in the context of visual data exploration. In particular, we have implemented and evaluated our proposed approach within XmdvTool, a free-ware visualization system for visually exploring hierarchical multivariate data. We have tested our technique on real user traces gathered by the logging tool of our system as well as on synthetic user traces. Our results confirm that our adaptive approach improves system performance by selecting a good combination of prefetching strategies that adapts to the user's changing navigation patterns.

Made available in DSpace on 2016-04-26T18:11:59Z (GMT). No. of bitstreams: 1 Carla Marangoni De Bona.pdf: 14200180 bytes, checksum: 45f9d550d9ece30ab44f9ff674211b84 (MD5) Previous issue date: 2012-06-29 / This research aims to investigate expressive practices in projects that make use of content generated by user (s), reflecting on the nature of digital media communication and expressive aspects. Aims also contribute to the reflection on the creative processes in digital networks, their limitations and possibilities. The creative processes that permeate media networks are characterized by multiplying forms of publishing, sharing and organizing information. Moreover, there is a diversity of spaces for interaction among the users leveraging the collective work processes, affective exchange, production and circulation of information. In short, the social construction of knowledge supported by information technology (PRIMO, 2007). Methodologically, is composed of three stages: a literature review, analysis and experimentation: development of a poetic work. The theory is based on references on the Internet and the network society (Manuel Castells, Kazys Varnelis and André Lemos), intimacy/exposure on the network (Paula Sibilia, Guy Debord and André Lemos), collaboration (Karla Schuch Brunet, James and Thelma Surowieki Johnson), authorship (Michel Foucault, Stuart Hall, Cecilia A. Salles), creative processes in networks (Almeida Salles Cecilia and Lucy Lion), aesthetics of database and data visualization (Cristine Paul, Victoria Vesna and Lev Manovich). The corpus of analysis consists of projects designed to media networks that put on the agenda the above mentioned concepts and contribute to the understanding of collaborative poetry in contemporary society. They are: We Feel Fine, Post Secret, and youTAG Tele_bits 2.0. The poetic experimentation, coloroflove.us the project, discusses the procedures that make possible the maintenance of interests on the project and analyzes viral propagation in such networks. Developed in PHP with MySQL database, the coloroflove.us is online since 29/09/10 / A presente pesquisa tem como objetivo investigar práticas expressivas em projetos que usufruem de conteúdos gerados pelo(s) usuário(s), refletindo sobre a natureza comunicacional das mídias digitais e seus aspectos significativos. Busca, igualmente, contribuir para a ponderação acerca dos processos de criação em redes digitais, suas limitações e possibilidades. Os processos criativos que permeiam as redes midiáticas se caracterizam por multiplicarem as formas de publicação, compartilhamento e organização das informações. Além disso, há uma diversificação de espaços para interação entre os usuários, potencializando os processos de trabalho coletivos, trocas afetivas, produção e circulação de informações. Em suma, de construção social de conhecimento apoiada pela informática (PRIMO, 2007). Metodologicamente, é composta por três fases: revisão bibliográfica, análise e experimentação prática: o desenvolvimento de um trabalho poético. A base teórica se fundamenta por referências sobre internet e a sociedade em rede (Manuel Castells, Kazys Varnelis e André Lemos), intimidade/exposição na rede (Paula Sibília, Guy Debord e André Lemos), colaboração (Karla Schuch Brunet, James Surowieki e Telma Johnson), autoria (Michel Focault, Stuart Hall, Cecília A. Salles), processos criativos em redes (Cecília Almeida Salles e Lúcia Leão), estética do banco de dados e visualização de dados (Cristine Paul, Victoria Vesna e Lev Manovich). O corpus de análise é composto por projetos concebidos para redes midiáticas que colocam em pauta os conceitos tratados acima, além de contribuírem para a compreensão das poéticas colaborativas na contemporaneidade. São eles: We Feel Fine, Post Secret, youTAG e Tele_bits 2.0. A experimentação prática-poética, o projeto coloroflove.us, busca discutir os procedimentos que tornam possíveis a manutenção do interesse em torno do mesmo e analisa a propagação viral nas redes telemáticas. Desenvolvido em linguagem PHP com banco de dados MySQL, o coloroflove.us está online desde 29/09/10.

En France, la nécessaire rationalisation des moyens alloués aux hôpitaux a abouti à une concentration des ressources et une augmentation de la complexité des plateaux techniques. Leur pilotage et leur répartition territoriale s’avèrent d’autant plus difficile, soulevant ainsi la problématique de l’optimisation des systèmes de soins. L’utilisation des données massives produites pas ces systèmes pourrait constituer une nouvelle approche en matière d’analyse et d’aide à la décision. Méthode : A partir d’une réflexion sur la notion de performance, différentes approches d’optimisation préexistantes sont d’abord mis en évidence. Le bloc opératoire a été choisi en tant que terrain expérimental. Suit une analyse sur une fusion d’établissements en tant qu’exemple d’une approche d’optimisation par massification.Ces deux étapes permettent de défendre une approche alternative qui associe l’usage de données massives, la science des réseaux et la visualisation des données sous forme cartographique. Deux sets de séjours en chirurgie orthopédique sur la région ex-Midi-Pyrénées sont utilisés. L’enchainement des séjours de soins est considéré en tant en réseau de données. L’ensemble est projeté dans un environnement visuel développé en JavaScript et permettant une fouille dynamique du graphe. Résultats : La possibilité de visualiser des parcours de santé sous forme de graphes NŒUDS-LIENS est démontrée. Les graphes apportent une perception supplémentaire sur les enchainements de séjours et les redondances des parcours. Le caractère dynamique des graphes permet en outre leur fouille. L’approche visuelle subjective est complétée par une série de mesures objectives issues de la science des réseaux. Les plateaux techniques de soins produisent des données massives utiles à leur analyse et potentiellement à leur optimisation. La visualisation graphique de ces données associées à un cadre d’analyse tel que la science des réseaux donne des premiers indicateurs positifs avec notamment la mise en évidence de motifs redondants. La poursuite d’expérimentations à plus large échelle est requise pour valider, renforcer et diffuser ces observations et cette méthode. / In France, the streamlining of means assigned hospitals result in concentration of resources ana growing complexily of heallhcare facilities. Piloting and planning (them turn out to be all the more difficult, thus leading of optimjzation problems. The use of massive data produced by these systems in association with network science an alternative approach for analyzing and improving decision-making support jn healthcare. Method : Various preexisting optimisation are first highblighted based on observations in operating theaters chosen as experirnentai sites. An analysis of merger of two hospitlas also follows as an example of an optimization method by massification. These two steps make it possible to defend an alternative approach that combines the use of big data science of networks data visualization techniques. Two sets of patient data in orthopedic surgery in the ex-Midi-Pyrénées region in France are used to create a network of all sequences of care. The whole is displayed in a visual environment developed in JavaScript allowing a dynamic mining of the graph. Results: Visualizing healthcare sequences in the form of nodes and links graphs has been sel out. The graphs provide an additional perception of' the redundancies of he healthcare pathways. The dynamic character of the graphs also allows their direct rnining. The initial visual approach is supplernented by a series of objcctive measures from the science of networks. Conciusion: Healthcare facilities produce massive data valuable for their analysis and optimization. Data visualizalion together with a framework such as network science gives prelimiaary encouraging indicators uncovering redondant healthcare pathway patterns. Furthev experimentations with various and larger sets of data is required to validate and strengthen these observations and methods.