Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Made available in DSpace on 2014-06-11T19:35:43Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-04-06Bitstream added on 2014-06-13T19:05:56Z : No. of bitstreams: 1 santos_tg_dr_rcla.pdf: 2207082 bytes, checksum: cd6f4211c558cc08a16cda8ec6665379 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Estudamos a riqueza, composição taxonômica e os padrões de distribuição espaciais e temporais de taxocenoses de anuros do Parque Estadual Morro do Diabo (PEMD), o maior remanescente de Floresta Estacional Semidecídua no estado de São Paulo, Brasil. Registramos 28 espécies de anuros (Apêndice I) de setembro de 2005 a março de 2007, que compreenderam um misto de espécies de Mata Atlântica, do Cerrado e de formas amplamente distribuídas na América do Sul, geralmente consideradas tolerantes a modificações antropogênicas. A baixa riqueza de espécies e de modos reprodutivos, a predominância de espécies habitatgeneralistas e a alta similaridade de espécies de anuros com áreas de Cerrado podem ser explicadas pela sazonalidade climática da área estudada (estação seca pronunciada), além da grande distância em relação a centros de diversificação de anuros, como as montanhas costeiras da Floresta Atlântica úmida. Chuva e fotoperíodo explicaram aproximadamente 77% da atividade de vocalização de toda a taxocenose, enquanto somente a chuva e o fotoperíodo explicaram a temporada de vocalização em ambientes temporários e permanentes, respectivamente. Registramos alta sobreposição na temporada de vocalização dos machos, mas segregação na fase larval. A distribuição das espécies de anuros entre sítios de reprodução (Apêndice II) diferiu da esperada pelo acaso e compreendeu três taxocenoses distintas de anuros que foram explicadas pelo conjunto de variáveis ambientais de riachos permanentes, represas permanentes e poças temporárias. Registramos que 19 espécies de anuros (aproximadamente 83% da riqueza total de espécies registradas nos corpos d’água monitorados) foram indicadoras da heterogeneidade ambiental: três espécies indicaram riachos permanentes, quatro indicaram represas permanentes e 12 espécies indicaram poças temporárias... / We studied richness, composition, and patterns of temporal and spatial distributions of anuran assemblages of Morro do Diabo State Park (MDSP), the major remnant of Mesophytic Semideciduous Forest (MSF) in the state of São Paulo, Brazil. From September 2005 to March 2007 we recorded 28 anuran species (Appendix I), comprising a mix of Atlantic, Cerrado, and South American widespread species, usually considered tolerant to anthropic modifications. The low richness of species and reproductive modes, the predominance of habitat generalist species, and the high similarity with Cerrado areas can be explained by climatic seasonality of the studied area (pronounced dry season), besides its large distance in relation to centers of anuran diversification, such as coastal mountains of the wet Atlantic Forest. Rainfall and photoperiod explained about 77% of calling activity of the whole assemblage, while rainfall alone in temporary habitats and photoperiod in permanent ones explained the calling season. We recorded high temporal overlap for calling males, but segregation for tadpoles. Spatial distribution of anuran species among breeding sites of the MDSP (Appendix II) differed of expected by chance and comprised three distinct anuran assemblages that were explained by the suite environmental variables of permanent streams, permanent dams, and temporary ponds. We recorded that 19 species (about 83% of total anuran species recorded in monitored sites) were indicators of environmental heterogeneity: three anuran species indicated permanent streams, four indicated permanents dams, and 12 anuran species indicated temporary ponds. Regarding to micro-spatial distribution of anuran species at two temporary ponds of MDSP, we recorded that males of most pairs of species (96%) used distinct sites for calling activities. The best combination of variables discriminating anuran species regarding male... (Complete abstract click electronic access below)

Submitted by Kilvya Braga (kilvyabraga@hotmail.com) on 2018-04-24T12:36:47Z No. of bitstreams: 1 GEORGE MARCELO RODRIGUES ALVES - DISSERTAÇÃO (PPGCC) 2016.pdf: 2788195 bytes, checksum: 45c2b3c7089a4adbd7443b1c08cd4881 (MD5) / Made available in DSpace on 2018-04-24T12:36:47Z (GMT). No. of bitstreams: 1 GEORGE MARCELO RODRIGUES ALVES - DISSERTAÇÃO (PPGCC) 2016.pdf: 2788195 bytes, checksum: 45c2b3c7089a4adbd7443b1c08cd4881 (MD5) Previous issue date: 2016-02-26 / Devido ao crescimento constante da quantidade de textos disponíveis na Web, existe uma necessidade de catalogar estas informações que surgem a cada instante. No entanto, trata-se de uma tarefa árdua e na qual seres humanos são incapazes de realizar esta tarefa de maneira manual, tendo em vista a quantidade incontável de dados que são disponibilizados a cada segundo. Inúmeras pesquisas têm sido realizadas no intuito de automatizar este processo de catalogação. Uma vertente de grande utilidade para as várias áreas do conhecimento humano é a indexação de documentos com base nos contextos temporais presentes nestes documentos. Esta não é uma tarefa trivial, pois envolve a análise de informações não estruturadas presentes em linguagem natural, disponíveis nos mais diversos idiomas, dentre outras dificuldades. O objetivo principal deste trabalho é criar uma abordagem capaz de permitir a indexação de documentos, determinando mapas de tópicos enriquecidos com conceitos e as respectivas informações temporais relacionadas. Tal abordagem deu origem ao RISO-GCT (Geração de Contextos Temporais), componente do Projeto RISO (Recuperação da Informação Semântica de Objetos Textuais), que tem como objetivo criar um ambiente de indexação e recuperação semântica de documentos possibilitando uma recuperação mais acurada. O RISO-GCT utilizou os resultados de um módulo preliminar, o RISO-TT (Temporal Tagger), responsável por etiquetar informações temporais presentes em documentos e realizar o processo de normalização das expressões temporais encontradas. Deste processo foi aperfeiçoada a abordagem responsável pela normalização de expressões temporais, para que estas possam ser manipuladas mais facilmente na determinação dos contextos temporais. . Foram realizados experimentos para avaliar a eficácia da abordagem proposta nesta pesquisa. O primeiro, com o intuito de verificar se o Topic Map previamente criado pelo RISO-IC (Indexação Conceitual), foi enriquecido com as informações temporais relacionadas aos conceitos de maneira correta e o segundo, para analisar a eficácia da abordagem de normalização das expressões temporais extraídas de documentos. Os experimentos concluíram que tanto o RISO-GCT, quanto o RISO-TT incrementado obtiveram resultados superiores aos concorrentes. / Due to the constant growth of the number of texts available on the Web, there is a need to catalog that information which appear at every moment. However, it is an arduous task in which humans are unable to perform this task manually, given the increased amount of data available at every second. Numerous studies have been conducted in order to automate the cataloging process. A research line with utility for various areas of human knowledge is the indexing of documents based on temporal contexts present in these documents. This is not a trivial task, as it involves the analysis of unstructured information present in natural language, available in several languages, among other difficulties. The main objective of this work is to create a model to allow indexing of documents, creating topic maps enriched with the concepts in text and their related temporal information. This approach led to the RISO-GCT (Temporal Contexts Generation), a part of RISO Project (Semantic Information Retrieval on Text Objects), which aims to create a semantic indexing environment and retrieval of documents, enabling a more accurate recovery. RISO-GCT uses the results of a preliminary module, the RISO-TT (Temporal Tagger) responsible the labeling temporal information contained in documents and carrying out the process of normalization of temporal expressions. Found. In this module the normalization of temporal expressions has been improved, in order allow a richer temporal context determination. Experiments were conducted to evaluate the effectiveness of the approach proposed a in this research. The first, in order to verify that the topic map previously created by RISO-IC has been correctly enriched with temporal information related to the concepts correctly, and the second, to analyze the effectiveness of the normalization of expressions extracted from documents. The experiments concluded that both the RISO-GCT, as the RISO-TT, which was evolved during this work, obtained better results than similar tools.

In contrast to established approaches that analyze networks based on their structural properties, networks can also be studied by investigating the patterns that are evolved by a discrete dynamical system built upon them, such as cellular automata (CAs). Combined with networks these tools can be used to map the relationship between the network architecture and its impact on the patterns evolved by the governing spatially discrete dynamical system. This thesis focuses on the investigation of discrete spatially explicit models (SEMs), among which are CAs, for network analysis and characterization. The relationship between network architecture and its dynamic aspects concerning pattern formation is studied. Additionally, this work aims at the development of evolutionary methods that can be employed for extracting features from such patterns and then be used as network descriptors. In order to achieve this goal, methods that integrate the network structure with the SEMs were proposed, implemented and analyzed. The proposed family of network automata is characterized by birth-survival dynamics that results in different categories of spatio-temporal patterns. Such patterns were quantitatively assessed and used to characterize different network topologies and perform classification tasks in the context of pattern recognition. Inspired by the classic Life-like CA, the proposed Life-like Network Automata (LLNA) illustrate how such tasks can be performed in real-world applications. In addition, the rock-paper-scissors (RPS) model, normally implemented on square lattices, was investigated by defining it on networks. The obtained results confirm the potential of the proposed quantitative analysis of the spatio-temporal patterns for network classification. This quantitative analysis was performed for a set of different pattern recognition tasks and for the majority of them, the classification performance improved. In addition, the reliability of LLNA as a general tool for pattern recognition applications was demonstrated in a diverse scope of classification tasks. The applicability of structural network descriptors was also highlighted in the context of shape characterization in computer vision. Through the proposed approach, the link between these network descriptors and the shape properties, such as angle and curvature, was illustrated. Moreover, when chosen adequately, the network descriptors led to a better classification performance for different shape recognition tasks. Regarding the RPS model, we demonstrated that the presence of long-range correlations in some networks directly influence the RPS dynamics. Finally, it was shown how a commuter network can be used to predict influenza outbreaks. All the proposed methods use different aspects of network analysis and contribute to the study of CAs and other SEMs on irregular tessellations, in contrast to the commonly used regular topologies. In addition, new insights were obtained concerning pattern recognition in networks through the use of spatio-temporal patterns as network descriptors. / Em contraste às abordagens clássicas que analisam redes com base em suas propriedades estruturais, as redes também podem ser estudadas investigando-se os padrões desenvolvidos por um sistema dinâmico discreto construído sobre essas redes, como os autômatos celulares (CAs). Combinadas às redes, essas ferramentas podem ser usadas para se mapear a relação entre a arquitetura da rede e seu impacto nos padrões obtidos pelo sistema dinâmico subjacente. Esta tese está focada na investigação de modelos discretos espacialmente explícitos (SEMs), entre os quais os CAs, para análise e caracterização de redes. A relação entre a arquitetura da rede e seu aspecto dinâmico em relação à formação de padrões é investigada. Além disso, este trabalho visa o desenvolvimento de métodos evolutivos que podem ser usados para extrair características de tais padrões para, então, serem usados como descritores de redes. Para atingir este objetivo, métodos que integram a estrutura da rede com os SEMs foram propostos, implementados e analisados. A família de redes-autômatos proposta é caracterizada por uma dinâmica de nascimento-sobrevivência que resulta em diferentes categorias de padrões espaço-temporais. Tais padrões foram avaliados quantitativamente e utilizados para caracterizar diferentes topologias de redes e realizar tarefas de classificação no contexto do reconhecimento de padrões. Inspirados pelo clássico Life-Like CA, a rede-autômato proposta, Life-like (LLNA), ilustra como tais tarefas podem ser realizadas em aplicações mais realistas. Além disso, o modelo de rock-paper-scissors (RPS), normalmente implementado em reticulados quadrados, foi investigado usando-se redes como tesselações. Os resultados obtidos confirmam o potencial da análise quantitativa proposta dos padrões espaço-temporais para classificação de redes. Essa análise quantitativa foi realizada para um conjunto de tarefas de reconhecimento de padrões, e, para a maioria dessas tarefas, o desempenho da classificação melhorou. Além disso, a confiabilidade do LLNA como uma ferramenta genérica para reconhecimento de padrões foi demonstrada para várias tarefas de classificação de diferentes escopos. A aplicabilidade de descritores estruturais de redes também foi destacada no contexto de caracterização de formas em visão computacional. Através da abordagem proposta, a ligação entre esses descritores de rede e as propriedades da forma, como ângulo e curvatura, foi ilustrada. Além disso, quando escolhidos adequadamente, os descritores de rede levam a um melhor desempenho de classificação para diferentes tarefas de categorização de formas. No que diz respeito ao modelo RPS, demonstramos que a presença de correlações de longo alcance nas redes afeta diretamente a dinâmica do modelo. Finalmente, foi apresentado como uma rede de transporte pode ser usada para prever surtos de gripe. Todos os métodos propostos utilizam diferentes aspectos da análise de redes e contribuem para o estudo de CAs e outras SEMs em tesselações irregulares, uma vez que estes modelos são geralmente descritos em topologias regulares. Além disso, uma nova metodologia foi proposta em relação ao reconhecimento de padrões em redes através do uso de padrões espaço-temporais como descritores da rede.

Práce pojednává o fonotaktickém a akustickém přístupu pro automatické rozpoznávání jazyka. První část práce pojednává o fonotaktickém přístupu založeném na výskytu fonémových sekvenci v řeči. Nejdříve je prezentován popis vývoje fonémového rozpoznávače jako techniky pro přepis řeči do sekvence smysluplných symbolů. Hlavní důraz je kladen na dobré natrénování fonémového rozpoznávače a kombinaci výsledků z několika fonémových rozpoznávačů trénovaných na různých jazycích (Paralelní fonémové rozpoznávání následované jazykovými modely (PPRLM)). Práce také pojednává o nové technice anti-modely v PPRLM a studuje použití fonémových grafů místo nejlepšího přepisu. Na závěr práce jsou porovnány dva přístupy modelování výstupu fonémového rozpoznávače -- standardní n-gramové jazykové modely a binární rozhodovací stromy. Hlavní přínos v akustickém přístupu je diskriminativní modelování cílových modelů jazyků a první experimenty s kombinací diskriminativního trénování a na příznacích, kde byl odstraněn vliv kanálu. Práce dále zkoumá různé druhy technik fúzi akustického a fonotaktického přístupu. Všechny experimenty jsou provedeny na standardních datech z NIST evaluaci konané v letech 2003, 2005 a 2007, takže jsou přímo porovnatelné s výsledky ostatních skupin zabývajících se automatickým rozpoznáváním jazyka. S fúzí uvedených technik jsme posunuli state-of-the-art výsledky a dosáhli vynikajících výsledků ve dvou NIST evaluacích.

Pinhole leaks in copper plumbing pipes are a big concern for the homeowners. The problem is spread across the nation and remains a threat to plumbing systems of all ages. Due to the absence of a single acceptable mechanistic theory no preventive measure is available to date. Most of the present mechanistic theories are based on analysis of failed pipe samples however an objective comparison with other pipes that did not fail is seldom made. The variability in hydraulic and water quality parameters has made the problem complex and unquantifiable in terms of plumbing susceptibility to pinhole leaks. The present work determines the spatial and temporal spread of pinhole leaks across United States. The hotspot communities are identified based on repair histories and surveys. An assessment of variability in water quality is presented based on nationwide water quality data. A synthesis of causal factors is presented and a scoring system for copper pitting is developed using goal programming. A probabilistic model is presented to evaluate optimal replacement time for plumbing systems. Methodologies for mechanistic modeling based on corrosion thermodynamics and kinetics are presented. / Master of Science

A energia elétrica de uso doméstico no cenário mundial é um fenômeno recente que incorporou modulações temporais na expressão dos ritmos biológicos, como os hábitos alimentares e de sono. Após sua popularização, tecnologias eletrônicas foram criadas e incorporadas às nossas rotinas diárias. O acesso à iluminação elétrica promove uma maior flexibilidade na organização temporal das nossas atividades por facilitar o controle de extensão da fase de atividade para horas mais tardias da noite. Assim, essa situação pode constituir um desafio temporal ao organismo, gerado pelas oscilações das pistas temporais, adiantando ou atrasando a ocorrência de eventos rítmicos como a alimentação/jejum, atividade/repouso e metabolismo. O estudo foi realizado com 27 universitários entre 18 a 30 anos (17 mulheres; 10 homens) nos quais o comportamento alimentar e de sono foi registrado por meio do uso de diários e os ciclos de atividade/repouso e claro/escuro por meio de actímetros. Observada uma associação indireta entre o uso de equipamentos eletrônicos e o aumento no consumo alimentar. A associação observada em relação ao uso dos equipamentos com a ingesta está mais amparada no atraso do início do sono que propicia o aumento observado da ingesta média na fase noturna. No longo prazo o atraso da fase do sono e a diminuição da sua duração pode gerar um desalinhamento na regulação da expressão dos padrões temporais de atividade/repouso e alimentação/jejum que pode alterar o padrão do comportamento alimentar / Electric energy used widely in domestic context is a recent phenomenon that incorporates temporal modulations on the expression of biological rhythms and behaviour, like food ingestion and sleep patterns. After popularization of electric energy, electronic technologies have been created and incorporated to our routines. Access to electric light can promote more flexibility on temporal organization of our activities which allow for an extension of the wake phase to later portions of the night. Thus, that situation could promote temporal challenges to the organisms, generated by oscillations of temporal signals, which may cause phase advances or delays in rhythmic events like feeding/fasting, rest/activity and metabolic functions. This study involved 27 university students between 18 to 30 years old (17 women; 10 men) where feeding and sleep behavior data were collected with feeding and sleep diaries; the rest/activity rhythm and the light/dark cycle data were collected with actimeters. The association observed in relation to the use of the electronic equipments with the food intake is supported by the delay of the beginning of rest that propitiates the increase of the average intake in the nocturnal phase. In the long term, the delay of the rest phase and the decrease of its duration can generate a misalignment in the regulation of the expression of the temporal patterns of rest/activity and feeding/fasting and thus change the temporal pattern of the feeding behavior

Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come up naturally in a wide range of application domains, ranging from bioinformatics to manufacturing processes. Pattern discovery refers to a broad class of data mining techniques in which the objective is to unearth hidden patterns or unexpected trends in the data. In general, pattern discovery is about finding all patterns of 'interest' in the data and one popular measure of interestingness for a pattern is its frequency in the data. The problem of frequent pattern discovery is to find all patterns in the data whose frequency exceeds some user-defined threshold. Discovery of temporal patterns that occur frequently in sequential data has received a lot of attention in recent times. Different approaches consider different classes of temporal patterns and propose different algorithms for their efficient discovery from the data. This thesis is concerned with a specific class of temporal patterns called episodes and their discovery in large sequential data sets. In the framework of frequent episode discovery, data (referred to as an event sequence or an event stream) is available as a single long sequence of events. The ith event in the sequence is an ordered pair, (Et,tt), where Et takes values from a finite alphabet (of event types), and U is the time of occurrence of the event. The events in the sequence are ordered according to these times of occurrence. An episode (which is the temporal pattern considered in this framework) is a (typically) short partially ordered sequence of event types. Formally, an episode is a triple, (V,<,9), where V is a collection of nodes, < is a partial order on V and 9 is a map that assigns an event type to each node of the episode. When < is total, the episode is referred to as a serial episode, and when < is trivial (or empty), the episode is referred to as a parallel episode. An episode is said to occur in an event sequence if there are events in the sequence, with event types same as those constituting the episode, and with times of occurrence respecting the partial order in the episode. The frequency of an episode is some measure of how often it occurs in the event sequence. Given a frequency definition for episodes, the task is to discover all episodes whose frequencies exceed some threshold. This is done using a level-wise procedure. In each level, a candidate generation step is used to combine frequent episodes from the previous level to build candidates of the next larger size, and then a frequency counting step makes one pass over the event stream to determine frequencies of all the candidates and thus identify the frequent episodes. Frequency counting is the main computationally intensive step in frequent episode discovery. Choice of frequency definition for episodes has a direct bearing on the efficiency of the counting procedure. In the original framework of frequent episode discovery, episode frequency is defined as the number of fixed-width sliding windows over the data in which the episode occurs at least once. Under this frequency definition, frequency counting of a set of |C| candidate serial episodes of size N has space complexity O(N|C|) and time complexity O(ΔTN|C|) (where ΔT is the difference between the times of occurrence of the last and the first event in the data stream). The other main frequency definition available in the literature, defines episode frequency as the number of minimal occurrences of the episode (where, a minimal occurrence is a window on the time axis containing an occurrence of the episode, such that, no proper sub-window of it contains another occurrence of the episode). The algorithm for obtaining frequencies for a set of |C| episodes needs O(n|C|) time (where n denotes the number of events in the data stream). While this is time-wise better than the the windows-based algorithm, the space needed to locate minimal occurrences of an episode can be very high (and is in fact of the order of length, n, of the event stream). This thesis proposes a new definition for episode frequency, based on the notion of, what is called, non-overlapped occurrences of episodes in the event stream. Two occurrences are said to be non-overlapped if no event corresponding to one occurrence appears in between events corresponding to the other. Frequency of an episode is defined as the maximum possible number of non-overlapped occurrences of the episode in the data. The thesis also presents algorithms for efficient frequent episode discovery under this frequency definition. The space and time complexities for frequency counting of serial episodes are O(|C|) and O(n|C|) respectively (where n denotes the total number of events in the given event sequence and |C| denotes the num-ber of candidate episodes). These are arguably the best possible space and time complexities for the frequency counting step that can be achieved. Also, the fact that the time needed by the non-overlapped occurrences-based algorithm is linear in the number of events, n, in the event sequence (rather than the difference, ΔT, between occurrence times of the first and last events in the data stream, as is the case with the windows-based algorithm), can result in considerable time advantage when the number of time ticks far exceeds the number of events in the event stream. The thesis also presents efficient algorithms for frequent episode discovery under expiry time constraints (according to which, an occurrence of an episode can be counted for its frequency only if the total time span of the occurrence is less than a user-defined threshold). It is shown through simulation experiments that, in terms of actual run-times, frequent episode discovery under the non-overlapped occurrences-based frequency (using the algorithms developed here) is much faster than existing methods. There is also a second frequency measure that is proposed in this thesis, which is based on, what is termed as, non-interleaved occurrences of episodes in the data. This definition counts certain kinds of overlapping occurrences of the episode. The time needed is linear in the number of events, n, in the data sequence, the size, N, of episodes and the number of candidates, |C|. Simulation experiments show that run-time performance under this frequency definition is slightly inferior compared to the non-overlapped occurrences-based frequency, but is still better than the run-times under the windows-based frequency. This thesis also establishes the following interesting property that connects the non-overlapped, the non-interleaved and the minimal occurrences-based frequencies of an episode in the data: the number of minimal occurrences of an episode is bounded below by the maximum number of non-overlapped occurrences of the episode, and is bounded above by the maximum number of non-interleaved occurrences of the episode in the data. Hence, non-interleaved occurrences-based frequency is an efficient alternative to that based on minimal occurrences. In addition to being superior in terms of both time and space complexities compared to all other existing algorithms for frequent episode discovery, the non-overlapped occurrences-based frequency has another very important property. It facilitates a formal connection between discovering frequent serial episodes in data streams and learning or estimating a model for the data generation process in terms of certain kinds of Hidden Markov Models (HMMs). In order to establish this connection, a special class of HMMs, called Episode Generating HMMs (EGHs) are defined. The symbol set for the HMM is chosen to be the alphabet of event types, so that, the output of EGHs can be regarded as event streams in the frequent episode discovery framework. Given a serial episode, α, that occurs in the event stream, a method is proposed to uniquely associate it with an EGH, Λα. Consider two N-node serial episodes, α and β, whose (non-overlapped occurrences-based) frequencies in the given event stream, o, are fα and fβ respectively. Let Λα and Λβ be the EGHs associated with α and β. The main result connecting episodes and EGHs states that, the joint probability of o and the most likely state sequence for Λα is more than the corresponding probability for Λβ, if and only if, fα is greater than fβ. This theoretical connection has some interesting consequences. First of all, since the most frequent serial episode is associated with the EGH having the highest data likelihood, frequent episode discovery can now be interpreted as a generative model learning exercise. More importantly, it is now possible to derive a formal test of significance for serial episodes in the data, that prescribes, for a given size of the test, a minimum frequency for the episode needed in order to declare it as statistically significant. Note that this significance test for serial episodes does not require any separate model estimation (or training). The only quantity required to assess significance of an episode is its non-overlapped occurrences-based frequency (and this is obtained through the usual counting procedure). The significance test also helps to automatically fix the frequency threshold for the frequent episode discovery process, so that it can lead to what may be termed parameterless data mining. In the framework considered so far, the input to frequent episode discovery process is a sequence of instantaneous events. However, in many applications events tend to persist for different periods of time and the durations may carry important information from a data mining perspective. This thesis extends the framework of frequent episodes to incorporate such duration information directly into the definition of episodes, so that, the patterns discovered will now carry this duration information as well. Each event in this generalized framework looks like a triple, (Ei, ti, τi), where Ei, as earlier, is the event type (from some finite alphabet) corresponding to the ith event, and ti and τi denote the start and end times of this event. The new temporal pattern, called the generalized episode, is a quadruple, (V, <, g, d), where V, < and g, as earlier, respectively denote a collection of nodes, a partial order over this collection and a map assigning event types to nodes. The new feature in the generalized episode is d, which is a map from V to 2I, where, I denotes a collection of time interval possibilities for event durations, which is defined by the user. An occurrence of a generalized episode in the event sequence consists of events with both 'correct' event types and 'correct' time durations, appearing in the event sequence in 'correct' time order. All frequency definitions for episodes over instantaneous event streams are applicable for generalized episodes as well. The algorithms for frequent episode discovery also easily extend to the case of generalized episodes. The extra design choice that the user has in this generalized framework, is the set, I, of time interval possibilities. This can be used to orient and focus the frequent episode discovery process to come up with temporal correlations involving only time durations that are of interest. Through extensive simulations the utility and effectiveness of the generalized framework are demonstrated. The new algorithms for frequent episode discovery presented in this thesis are used to develop an application for temporal data mining of some data from car engine manufacturing plants. Engine manufacturing is a heavily automated and complex distributed controlled process with large amounts of faults data logged each day. The goal of temporal data mining here is to unearth some strong time-ordered correlations in the data which can facilitate quick diagnosis of root causes for persistent problems and predict major breakdowns well in advance. This thesis presents an application of the algorithms developed here for such analysis of the faults data. The data consists of time-stamped faults logged in car engine manufacturing plants of General Motors. Each fault is logged using an extensive list of codes (which constitutes the alphabet of event types for frequent episode discovery). Frequent episodes in fault logs represent temporal correlations among faults and these can be used for fault diagnosis in the plant. This thesis describes how the outputs from the frequent episode discovery framework, can be used to help plant engineers interpret the large volumes of faults logged, in an efficient and convenient manner. Such a system, based on the algorithms developed in this thesis, is currently being used in one of the engine manufacturing plants of General Motors. Some examples of the results obtained that were regarded as useful by the plant engineers are also presented.

<p>Diss. (sammanfattning) Umeå : Umeå universitet, 1995, härtill 6 uppsatser.</p> / digitalisering@umu

Given current rates of anthropogenic environmental change, combined with the increasing lethal and non-lethal mortality threat that human activities pose, there is a vital need to understand wildlife movement and behaviour in human-dominated landscapes to help inform conservation efforts and wildlife management. As long-term monitoring of wildlife populations using Global Positioning System (GPS) telemetry increases, there are new opportunities to quantify change in wildlife movement and behaviour. The objective of this PhD research is to develop novel methodological approaches for quantifying change in spatial-temporal patterns of wildlife movement and habitat by leveraging long time series of GPS telemetry and remotely sensed data. Analyses were focused on the habitat and movement of individuals in the threatened grizzly bear (Ursus arctos) population of Alberta, Canada, which occupies a human-dominated and heterogeneous landscape. Using methods in functional data analysis, a multivariate regionalization approach was developed that effectively summarizes complex spatial-temporal patterns associated with landscape disturbance, as well as recovery, which is often left unaccounted in studies quantifying patterns associated with disturbance. Next, the quasi-experimental framework afforded by a hunting moratorium was used to compare the influence of lethal (i.e., hunting) and non-lethal (i.e., anthropogenic disturbance) human-induced risk on antipredator behaviour of an apex predator, the grizzly bear. In support of the predation risk allocation hypothesis, male bears significantly decrease risky daytime behaviours by 122% during periods of high lethal human-induced risk. Rapid behavioural restoration occurred following the end of the hunt, characterized by diel bimodal movement patterns which may promote coexistence of large predators in human-dominated landscapes. A multi-scale approach using hierarchical Bayesian models, combined with post hoc trend tests and change point detection, was developed to test the influence of landscape disturbance and conditions on grizzly bear home range and movement selection over time. The results, representing the first longitudinal empirical analysis of grizzly bear habitat selection, revealed selection for habitat security at broad scales and for resource availability and habitat permeability at finer spatial scales, which has influenced potential landscape connectivity over time. Finally, combining approaches in movement ecology and conservation physiology, a body condition index was used to characterize how the physiological condition (i.e., internal state) of grizzly bears influences behavioral patterns due to costs and benefits associated with risk avoidance and resource acquisition. The results demonstrated individuals in poorer condition were more likely to engage in risky behaviour associated with anthropogenic disturbance, which highlights complex challenges for carnivore conservation and management of human-carnivore conflict. In summary, this dissertation contributes 1) a multivariate regionalization approach for quantifying spatial-temporal patterns of landscape disturbance and recovery applicable across diverse natural systems, 2) support for the growing theory that apex predators modify behavioural patterns to account for temporal overlap with lethal and non-lethal human-induced risk associated with humans, 3) an integrated approach for considering multi-scale spatial-temporal change in patterns of wildlife habitat selection and landscape connectivity associated with landscape change, 4) a cross-disciplinary framework for considering the impacts of the internal state on behavioural patterns and risk tolerance. / Graduate