This thesis presents a starburst model for far-infrared/sub-millimeter/millimeter (FIR/sub-mm/mm) line emission of molecular and atomic gas in an evolving starburst region, which is treated as an ensemble of non-interacting hot bubbles which drive spherical shells of swept-up gas into a surrounding uniform gas medium. These bubbles and shells are driven by winds and supernovae within massive star clusters formed during an instantaneous starburst. The underlying stellar radiation from the evolving clusters affects the properties and structure of photodissociation regions (PDRs) in the shells, and hence the spectral energy distributions (SEDs) of the molecular and atomic line emission from these swept-up shells and the associated parent giant molecular clouds (GMCs) contains a signature of the stage evolution of the starburst. The physical and chemical properties of the shells and their structure are computed using a a simple well known similarity solution for the shell expansion, a stellar population synthesis code, and a time-dependent PDR chemistry model. The SEDs for several molecular and atomic lines ($^{12}$CO and its isotope $^{13}$CO, HCN, HCO$^+$, C, O, and C$^+$) are computed using a non-local thermodynamic equilibrium (non-LTE) line radiative transfer model. By comparing our models with the available observed data of nearby infrared bright galaxies, especially M 82, we constrain the models and in the case of M 82, provide estimates for the age of the recent starburst activity. We also derive the total H$_2$ gas mass in the measured regions of the central 1 kpc starburst disk of M 82. In addition, we apply the model to represent various stages of starburst evolution in a well known sample of nearby luminous infrared galaxies (LIRGs). In this way, we interpret the relationship between the degree of molecular excitation and ratio of FIR to CO luminosity to possibly reflect different stages of the evolution of star-forming activity within their nuclear regions. We conclude with an assessment of the strengths and weaknesses of this approach to dating starbursts, and suggest future work for improving the model.

In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single words or single terms, whereas other methods have tried to use phrases instead of keywords, based on the hypothesis that the information carried by a phrase is considered more than that by a single term. Nevertheless, these phrase-based methods did not yield significant improvements due to the fact that the patterns with high frequency (normally the shorter patterns) usually have a high value on exhaustivity but a low value on specificity, and thus the specific patterns encounter the low frequency problem. This thesis presents the research on the concept of developing an effective Pattern Taxonomy Model (PTM) to overcome the aforementioned problem by deploying discovered patterns into a hypothesis space. PTM is a pattern-based method which adopts the technique of sequential pattern mining and uses closed patterns as features in the representative. A PTM-based information filtering system is implemented and evaluated by a series of experiments on the latest version of the Reuters dataset, RCV1. The pattern evolution schemes are also proposed in this thesis with the attempt of utilising information from negative training examples to update the discovered knowledge. The results show that the PTM outperforms not only all up-to-date data mining-based methods, but also the traditional Rocchio and the state-of-the-art BM25 and Support Vector Machines (SVM) approaches.

Dans toutes les espèces, les mutations et les réarrangements chromosomiques constituent des moteurs de l’évolution des génomes. Ils génèrent une diversité génétique à l’origine de la variation phénotypique observée entre les individus d’une même espèce. Cette variation est particulièrement importante chez les levures. Elles constituent donc d’excellents modèles pour déterminer les origines génétiques de la variation intra-spécifique. C’est dans ce contexte que ce travail s’est focalisé sur l’étude de la variation de résistance à l’ammonium et aux antifongiques azolés chez deux espèces de levures : Saccharomyces cerevisiae et Lachancea kluyveri. L’analyse des origines génétiques de la résistance à ces composés à mis en évidence que les variations génétiques pouvaient avoir lieu à plusieurs niveaux : séquence codante pour la résistance à l’ammonium et séquence régulatrice pour la résistance aux antifongiques. De plus, la réalisation d’expériences d’évolution adaptative a permis de mettre en évidence que l’adaptation à un nouvel environnement se faisait par dosage génique via l’acquisition d’un chromosome supplémentaire chez les espèces étudiées. / In all species, mutations and chromosomal rearrangements are drivers of genomes evolution. These processes generate the genetic diversity at the origin of the phenotypic variations observed between the individuals of the same species. This variation is essential for their adaptation to a new environment. The yeasts are isolated from various ecological and geographical niches and show an important phenotypic variation. According to these characteristics, they are excellent modelorganisms to determine the genetic origins of the observed phenotypic variation. In this context, the study focused on the variation of resistance to ammonium and azole antifungals within two yeast species: Saccharomyces cerevisiae and Lachancea kluyveri. The analyses of the genetic origin of the resistance to these compounds show that this genetic variation could occur at several levels: coding sequence for resistance to ammonium and regulatory sequence for resistance to antifungal agents. In addition, evolving experiments have showed that the adaptation to a new environment was done by gene dosage, through the acquisition of extrachromosomes in both species studied.

Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental characteristics of such environments may evolve, and so cause dangerous consequences, e.g., putting people lives at stake, if no reaction is adopted. Therefore, learning systems need to apply intelligent algorithms to monitor evolvement in their environments and update themselves effectively. Further, we may experience fluctuations regarding the performance of learning algorithms due to the nature of incoming data as it continuously evolves. That is, the current efficient learning approach may become deprecated after a change in data or environment. Hence, the question 'how to have an efficient learning algorithm over time against evolving data?' has to be addressed. In this thesis, we have made two contributions to settle the challenges described above. In the machine learning literature, the phenomenon of (distributional) change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, adaptive learners have been devised which may utilize drift detection methods to locate the drift points in dynamic and changing data streams. A drift detection method able to discover the drift points quickly, with the lowest false positive and false negative rates, is preferred. False positive refers to incorrectly alarming for concept drift, and false negative refers to not alarming for concept drift. In this thesis, we introduce three algorithms, called as the Fast Hoeffding Drift Detection Method (FHDDM), the Stacking Fast Hoeffding Drift Detection Method (FHDDMS), and the McDiarmid Drift Detection Methods (MDDMs), for detecting drift points with the minimum delay, false positive, and false negative rates. FHDDM is a sliding window-based algorithm and applies Hoeffding’s inequality (Hoeffding, 1963) to detect concept drift. FHDDM slides its window over the prediction results, which are either 1 (for a correct prediction) or 0 (for a wrong prediction). Meanwhile, it compares the mean of elements inside the window with the maximum mean observed so far; subsequently, a significant difference between the two means, upper-bounded by the Hoeffding inequality, indicates the occurrence of concept drift. The FHDDMS extends the FHDDM algorithm by sliding multiple windows over its entries for a better drift detection regarding the detection delay and false negative rate. In contrast to FHDDM/S, the MDDM variants assign weights to their entries, i.e., higher weights are associated with the most recent entries in the sliding window, for faster detection of concept drift. The rationale is that recent examples reflect the ongoing situation adequately. Then, by putting higher weights on the latest entries, we may detect concept drift quickly. An MDDM algorithm bounds the difference between the weighted mean of elements in the sliding window and the maximum weighted mean seen so far, using McDiarmid’s inequality (McDiarmid, 1989). Eventually, it alarms for concept drift once a significant difference is experienced. We experimentally show that FHDDM/S and MDDMs outperform the state-of-the-art by representing promising results in terms of the adaptation and classification measures. Due to the evolving nature of data streams, the performance of an adaptive learner, which is defined by the classification, adaptation, and resource consumption measures, may fluctuate over time. In fact, a learning algorithm, in the form of a (classifier, detector) pair, may present a significant performance before a concept drift point, but not after. We define this problem by the question 'how can we ensure that an efficient classifier-detector pair is present at any time in an evolving environment?' To answer this, we have developed the Tornado framework which runs various kinds of learning algorithms simultaneously against evolving data streams. Each algorithm incrementally and independently trains a predictive model and updates the statistics of its drift detector. Meanwhile, our framework monitors the (classifier, detector) pairs, and recommends the efficient one, concerning the classification, adaptation, and resource consumption performance, to the user. We further define the holistic CAR measure that integrates the classification, adaptation, and resource consumption measures for evaluating the performance of adaptive learning algorithms. Our experiments confirm that the most efficient algorithm may differ over time because of the developing and evolving nature of data streams.

Os direitos sociais são fruto das lutas dos indivíduos por melhores condições de trabalho e de vida ao longo dos séculos XVIII e XIX, embora os direitos mínimos dos trabalhadores somente tenham sido sistematicamente inseridos nas Constituições e albergados pelos diplomas internacionais ao no decorrer do século XX. No Brasil, os direitos fundamentais do trabalhador foram elencados na Constituição Federal de 1934 e, a partir de então, foram sendo ampliados até a Constituição Federal de 1988, nomeada de cidadã, que inaugurou um marco na constitucionalização desses direitos sociais, integrando-os efetivamente ao rol dos direitos fundamentais, conferindo-lhes aplicabilidade imediata e natureza de cláusulas pétreas. Toda essa sistemática traçada pelo legislador constituinte exige que os aplicadores do Direito tratem esses direitos trabalhistas como realmente fundamentais, inclusive no que tange às questões relativas à eficácia jurídica, efetividade e aplicabilidade. Ao lado do dilema da falta de efetividade das normas que estabelecem esses direitos, pela cultura de seu descumprimento reiterado pelos empregadores, existe outro problema igualmente grave: a ausência de implementação ou a implementação restritiva de vários direitos fundamentais trabalhistas, tanto pela ausência de leis infraconstitucionais que regulamentem as normas que os estatuem, quanto pela interpretação jurisdicional que lhes é conferida. Embora com alguns avanços no campo hermenêutico, a atuação do Poder Judiciário ainda tem sido insuficiente para a implementação plena dos direitos fundamentais sociais, tanto em virtude das resistências externas a uma postura mais ativa do Judiciário, quanto pela tendência de auto-restrição dos juízes em se aceitarem como órgãos legítimos para concretizar os direitos sociais esculpidos na Constituição. Ambos os problemas têm fulcro em uma concepção teórica restritiva de cidadania e, consequentemente, do exercício efetivo dos direitos fundamentais sociais pelos seus titulares, e em uma leitura desatualizada da teoria da separação dos poderes de Montesquieu, que desconsidera o Poder Judiciário como destinatário das normas de direitos fundamentais sociais. Sem embargo, a Constituição de 1988 adotou uma concepção de cidadania ampla, que pode ser denominada de cidadania social, pois o cidadão tem não apenas a prerrogativa de exercer os seus direitos políticos e civis, como também os seus direitos sociais, além de poder requerer ao Judiciário a implementação dos direitos cujo exercício se encontra limitado, inclusive pela interpretação involutiva dos dispositivos constitucionais, totalmente desvinculada da realidade social. O cidadão tem garantido constitucionalmente o acesso a uma ordem jurídica justa, no sentido do acesso aos tribunais, do exercício do direito de ação, com todas as garantias concernentes ao devido processo legal, e de uma prestação jurisdicional adequada e em tempo razoável que concretize os direitos reconhecidos em juízo. Para isso, o juiz deve se valer não apenas da utilização de mecanismos processuais adequados, mas também, em se tratando de pleitos que envolvam direitos fundamentais, da interpretação evolutiva, isto é, da atribuição de novos conteúdos à norma constitucional, sem a alteração do texto do dispositivo constitucional, em virtude de mudanças sócio-econômico-políticas não previstas pelo constituinte. Embora existam exemplos de decisões, majoritárias ou pontuais proferidas por juízes ou pelos Tribunais do Trabalho, em que se vislumbra a interpretação constitucional evolutiva de alguns direitos fundamentais trabalhistas, para a implementação plena desses direitos a atuação desse ramo especializado do Judiciário deve ser mais incisiva e abrangente. Dessa forma, o intento da presente tese é demonstrar que, para garantir a implementação efetiva de vários direitos dos trabalhadores estabelecidos nos artigos 7º a 11 da CF/88 e artigo 10 do ADCT, a Justiça do Trabalho deverá adotar uma hermenêutica jurisdicional pautada na interpretação evolutiva das normas constitucionais e na concepção ampliativa do exercício dos direitos fundamentais, fundada no princípio da cidadania social. / Social rights are the result of individuals\' struggles for better working and living conditions in the eighteenth and nineteenth centuries, although the basic rights of workers have only been systematically inserted in the Constitution and encompassed by international treaties, covenants and declarations in the twentieth century. In Brazil, the fundamental rights of workers were listed in the Constitution of 1934 and, thereafter, have been extended to the Federal Constitution of 1988, referred to as \"citizen\", which represented a milestone for the constitutionalization of social rights, integrating them effectively to the role of fundamental rights and giving them instant applicability and the quality of entrenched clauses. The same procedure drafted by the constitutional legislators requires that lawenforcers see these labor rights as something really fundamental, including subjects related to the legal effectiveness, efficacy and applicability. Next to the dilemma of lack of effectiveness of the rules that determine these rights, that is to say the employers culture of a repeated failure to comply with them, there is another equally serious problem: the lack of implementation or putting into effect, in a restrictive way, various fundamental labor rights, both because of the absence of infraconstitutional laws which regulate the rules that set them up, as well as the judicial interpretation they were given. Despite some advances in the hermeneutic field, the judiciary has still been not enough for the full implementation of fundamental social rights, both because of external opposition to a more active role of the judiciary, and by the self-restraint judges tendency to accept themselves as a right and proper means for achieving the social rights guaranteed by the Brazilian Constitution. Both problems have a restrictive theoretical fulcrum conception of citizenship and, consequently, the effective exercise of fundamental social rights by their holders, and an outdated interpretation of the Montesquieus theory of separation of powers, which disregards the judiciary as a recipient of the fundamental social rights standards. Nevertheless, the Constitution of 1988 adopted a broad conception of citizenship, which can be called \"social\" citizenship since citizens has not only the prerogative of exercising their civil and political rights, but also their social rights, as well as requesting the Judiciary for the implementation of rights which exercise is limited, even because of the involuting interpretation of constitutional provisions, totally divorced from social reality. Citizens have a constitutionally guaranteed access to a fair legal system in the sense of accessing courts, exercising the right of action, with all the guarantees pertaining to a due legal procedure and proper adjudication in a reasonable term that makes available the rights recognized in court. For that, judges must not only rely on the use of appropriate procedural mechanisms, but also, in case of claims involving fundamental rights, on the evolutionary interpretation, that is, assigning new content to the constitutional rules, without changing the text of the constitution because of socio-economic and political changes not foreseen by the constituent. Although there are examples of majoritarian or specific decisions taken by judges or by the Labor Courts, which are able to glimpse the evolving constitutional interpretation of some fundamental labor rights, for the full implementation of these rights, the performance of that specialized branch of the judiciary should be more incisive and comprehensive. Thus, the goal of this thesis is to demonstrate that to ensure the effective implementation of various workers\' rights, as laid down in Articles 7 to 11 and Article 10 of CF/88 ADCT, the Labor Court should adopt judicial hermeneutics guided by the evolving interpretation of constitutional rules and the ampliative conception of exercising fundamental rights, based on the principle of social citizenship.

Orientadores: Fernando Antonio Campos Gomide, Rosangela Ballini / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-12T14:32:10Z (GMT). No. of bitstreams: 1 Lima_EltonMariode_M.pdf: 1259231 bytes, checksum: 7a910e84bfb43d6c13b2deb8b6f511c2 (MD5) Previous issue date: 2008 / Resumo: Este trabalho propõe um modelo fuzzy funcional evolutivo que utiliza uma aplicação do aprendizado participativo para a construção de uma base de regras. O aprendizado participativo é um modelo de aprendizado baseado na noção de compatibilidade para a atualização do conhecimento do sistema. O aprendizado participativo pode ser traduzido em um algoritmo de agrupamento não supervisionado conhecido como agrupamento participativo. O algoritmo intitulado Aprendizado Participativo Evolutivo é proposto para construir um modelo fuzzy funcional evolutivo no qual as regras são obtidas a partir de um algoritmo de agrupamento não supervisionado. O algoritmo utiliza uma versão do agrupamento participativo para a determinação de uma base de regras correspondente ao modelo funcional do tipo Takagi-Sugeno evolutivo. A partir de uma noção generalizada, o modelo proposto é aplicado em problemas de previsão de séries temporais e os resultados são obtidos para a conhecida série Box-Jenkis, além da previsão de uma série de carga horária de energia elétrica. Os resultados são comparados com o modelo Takagi-Sugeno evolutivo que utiliza a noção de função potencial para agrupar os dados dinâmicamente e com duas abordagens baseadas em redes neurais. Os resultados mostram que o modelo proposto é eficiente e parcimonioso, abrindo potencial para aplicações e estudos futuros. / Abstract: This work introduces an approach to develop evolving fuzzy rule-based models using participatory learning. Participatory learning assumes that learning and beliefs about a system depend on what the learning mechanism knows about the system itself. Participatory learning naturally augments clustering and yields an e_ective unsupervised fuzzy clustering algorithms for on-line, real time domains and applications. Clustering is an essential step to construct evolving fuzzy models and plays a key role in modeling performance and model quality. A least squares recursive approach to estimate the consequent parameters of the fuzzy rules for on-line modeling is emphasized. Experiments with the classic Box-Jenkins benchmark are conducted to compare the performance of the evolving participatory learning with the evolving fuzzy system modeling approach and alternative fuzzy modeling and neural methods. The experiments show the e_ciency of evolving participatory learning to handle the benchmark problem. The evolving participatory learning method is also used to forecast the average hourly load of an electric generation plant and compared against the evolving fuzzy system modeling using actual data. The results confirm the potential of the evolving fuzzy participatory method to solve real world modeling problems. / Mestrado / Automação Industrial / Mestre em Engenharia Elétrica

I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired Spiking Neural Network (SNN) model. The encoding of SNNs in GReaNs was inspired by the encoding of gene regulatory networks. As proof-of-principle, I used GReaNs to evolve SNNs to obtain a network with an output neuron which generates a predefined spike train in response to a specific input. Temporal pattern recognition was one of the main tasks during my studies. It is widely believed that nervous systems of biological organisms use temporal patterns of inputs to encode information. The learning technique used for temporal pattern recognition is not clear yet. I studied the ability to evolve spiking networks with different numbers of interneurons in the absence and the presence of noise to recognize predefined temporal patterns of inputs. Results showed, that in the presence of noise, it was possible to evolve successful networks. However, the networks with only one interneuron were not robust to noise. The foraging behaviour of many small animals depends mainly on their olfactory system. I explored whether it was possible to evolve SNNs able to control an agent to find food particles on 2-dimensional maps. Using ring rate encoding to encode the sensory information in the olfactory input neurons, I managed to obtain SNNs able to control an agent that could detect the position of the food particles and move toward it. Furthermore, I did unsuccessful attempts to use GReaNs to evolve an SNN able to control an agent able to collect sound sources from one type out of several sound types. Each sound type is represented as a pattern of different frequencies. In order to use the computational power of neuromorphic hardware, I integrated GReaNs with the SpiNNaker hardware system. Only the simulation part was carried out using SpiNNaker, but the rest steps of the genetic algorithm were done with GReaNs.

Magister Legum - LLM

Cette thèse aborde la problématique de la classification non supervisée de données lorsque les caractéristiques des classes sont susceptibles d'évoluer au cours du temps. On parlera également, dans ce cas, de classification dynamique de données temporelles non stationnaires. Le cadre applicatif des travaux concerne le diagnostic par reconnaissance des formes de systèmes complexes dynamiques dont les classes de fonctionnement peuvent, suite à des phénomènes d'usures, des déréglages progressifs ou des contextes d'exploitation variables, évoluer au cours du temps. Un modèle probabiliste dynamique, fondé à la fois sur les mélanges de lois et sur les modèles dynamiques à espace d'état, a ainsi été proposé. Compte tenu de la structure complexe de ce modèle, une variante variationnelle de l'algorithme EM a été proposée pour l'apprentissage de ses paramètres. Dans la perspective du traitement rapide de flux de données, une version séquentielle de cet algorithme a également été développée, ainsi qu'une stratégie de choix dynamique du nombre de classes. Une série d'expérimentations menées sur des données simulées et des données réelles acquises sur le système d'aiguillage des trains a permis d'évaluer le potentiel des approches proposées / Nowadays, diagnosis and monitoring for predictive maintenance of railway components are important key subjects for both operators and manufacturers. They seek to anticipate upcoming maintenance actions, reduce maintenance costs and increase the availability of rail network. In order to maintain the components at a satisfactory level of operation, the implementation of reliable diagnostic strategy is required. In this thesis, we are interested in a main component of railway infrastructure, the railway switch; an important safety device whose failure could heavily impact the availability of the transportation system. The diagnosis of this system is therefore essential and can be done by exploiting sequential measurements acquired successively while the state of the system is evolving over time. These measurements consist of power consumption curves that are acquired during several switch operations. The shape of these curves is indicative of the operating state of the system. The aim is to track the temporal dynamic evolution of railway component state under different operating contexts by analyzing the specific data in order to detect and diagnose problems that may lead to functioning failure. This thesis tackles the problem of temporal data clustering within a broader context of developing innovative tools and decision-aid methods. We propose a new dynamic probabilistic approach within a temporal data clustering framework. This approach is based on both Gaussian mixture models and state-space models. The main challenge facing this work is the estimation of model parameters associated with this approach because of its complex structure. In order to meet this challenge, a variational approach has been developed. The results obtained on both synthetic and real data highlight the advantage of the proposed algorithms compared to other state of the art methods in terms of clustering and estimation accuracy

Atualmente, muitas redes com características dinâmicas estão em funcionamento (por exemplo MANETs, DTNs, redes oportunistas, etc). Neste trabalho, estudamos um modelo para estas redes chamado de Grafos Evolutivos, que permite expressar a dinamicidade das conexões entre nós por meio de uma simples extensão da estrutura comum de grafos. Esta modelagem é utilizada no arcabouço proposto por Casteigts et al. para definir algoritmos distribuídos em redes dinâmicas, que utiliza grafos evolutivos para representar a topologia da rede e renomeação de rótulos para expressar a comunicação entre os nós. Utilizamos esta abordagem para estudar o problema da exclusão mútua distribuída em redes dinâmicas e diversos algoritmos propostos para ele, a fim de definir e validar suas condições necessárias e suficientes de conectividade em redes dinâmicas. Além da formalização de algoritmos, o modelo de grafos evolutivos também pode ser utilizado para analisar redes dinâmicas. Rastros de redes dinâmicas reais são amplamente utilizados na literatura para estudos de algoritmos pois estes geram resultados mais realísticos do que redes simuladas com padrões de movimento. A partir dos detalhes de cada conexão entre nós de um destes rastros, é possível construir um grafo evolutivo, do qual se pode extrair dados como jornadas ótimas entre nós, variação da conectividade no tempo, estabilidade, e periodicidade. Com as informações mencionadas, um pesquisador pode observar com maior precisão as características do rastro, o que facilita na escolha da rede mais apropriada para sua necessidade. Além disso, o conhecimento prévio de tais características de uma rede auxilia no estudo do comportamento de algoritmos executados sobre ela e provém uma validação para suposições geralmente feitas pelos pesquisadores. Para fornecer estas informações, desenvolvemos uma ferramenta Web que analisa rastros de redes dinâmicas e agrega os dados em um formato de fácil visualização. Descrevemos, neste trabalho, a implementação e a utilidade de todos os serviços da ferramenta. / Lately, several networks with dynamic properties (for instance MANETs, DTNs, opportunistic networks, etc) are functioning. In this work, we studied a model for these networks called Evolving Graphs, which allows the expression of the dynamicity of the conections between nodes through a simple extension of the common graph structure. This model is used by the framework proposed by Casteigts et al. to define distributed algorithms in dynamic networks, which uses evolving graphs to represent the network topology and graph relabelling to express the communication between nodes. Using this approach, we study the distributed mutual exclusion problem in dynamic networks and several algorithms proposed to solve it, in order to define and validate their necessary and sufficient connectivity conditions. Apart from the formalization of algorithms, the evolving graphs model can also be used to analyze dynamic networks. Dynamic network traces are widely used in the literature in order to study algorithms, as they generate better results than simulated networks with movement patterns. From the details of every connection between nodes in a trace, it is possible to build an evolving graph, from which a large amount of information can be extracted, such as optimal journeys between nodes, variation of the conectivity over time, stability and periodicity. With the aforementioned information, a researcher might observe the characteristics of a trace more precisely, which facilitates the process of choosing the most appropriate trace for his needs. Furthermore, the early knowledge of such characteristics of a network helps in the study of the behavior of the algorithms exected over it and provides a validation for the assumptions usually made by the researchers. In order to provide this information, we developed a web tool which analyzes dynamic network traces and aggregates the data in an easily readable format. In this work, we describe the implementation and usefulness of every service in the tool.