Dissertations / Theses on the topic 'Temporal data analysis'
To see the other types of publications on this topic, follow the link: Temporal data analysis.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Temporal data analysis.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Aßfalg, Johannes. "Advanced Analysis on Temporal Data." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-87985.
2
Goodwin, David Alexander. "Wavelet analysis of temporal data." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/102/.
Abstract:
This thesis considers the application of wavelets to problems involving multiple series of temporal data. Wavelets have proven to be highly effective at extracting frequency information from data. Their multi-scale nature enables the efficient description of both transient and long-term signals. Furthermore, only a small number of wavelet coefficients are needed to describe complicated signals and the wavelet transform is computationally efficient. In problems where frequency properties are known to be important, it is proposed that a modelling approach which attempts to explain the response in terms of a multi-scale wavelet representation of the explanatory series will be an improvement on standard regression techniques. The problem with classical regression is that differing frequency characteristics are not exploited and make the estimates of the model parameters less stable. The proposed modelling method is presented with application to examples from seismology and tomography. In the first part of the thesis, we investigate the use of the non-decimated wavelet transform in the modelling of data produced from a simulated seismology study. The fact that elastic waves travel with different velocities in different rock types is exploited and wavelet models are proposed to avoid the complication of predictions being unstable to small changes in the input data, that is an inverse problem. The second part of the thesis uses the non-decimated wavelet transform to model electrical tomographic data, with the aim of process control. In industrial applications of electrical tomography, multiple voltages are recorded between electrodes attached to the boundary of, for example, a pipe. The usual first step of the analysis is then to reconstruct the conductivity distribution within the pipe. The most commonly used approaches again lead to inverse problems, and wavelet models are again used here to overcome this difficulty. We conclude by developing the non-decimated multi-wavelet transform for use in the modelling processes and investigate the improvements over scalar wavelets.
3
Hönel, Sebastian. "Temporal data analysis facilitating recognition of enhanced patterns." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-51864.
Abstract:
Assessing the source code quality of software objectively requires a well-defined model. Due to the distinct nature of each and every project, the definition of such a model is specific to the underlying type of paradigms used. A definer can pick metrics from standard norms to define measurements for qualitative assessment. Software projects develop over time and a wide variety of re-factorings is applied tothe code which makes the process temporal. In this thesis the temporal model was enhanced using methods known from financial markets and further evaluated using artificial neural networks with the goal of improving the prediction precision by learning from more detailed patterns. Subject to research was also if the combination of technical analysis and machine learning is viable and how to blend them. An in-depth selection of applicable instruments and algorithms and extensive experiments were run to approximate answers. It was found that enhanced patterns are of value for further processing by neural networks. Technical analysis however was not able to improve the results, although it is assumed that it can for an appropriately sizedproblem set.
4
Kim, Kihwan. "Spatio-temporal data interpolation for dynamic scene analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47729.
Abstract:
Analysis and visualization of dynamic scenes is often constrained by the amount of spatio-temporal information available from the environment. In most scenarios, we have to account for incomplete information and sparse motion data, requiring us to employ interpolation and approximation methods to fill for the missing information. Scattered data interpolation and approximation techniques have been widely used for solving the problem of completing surfaces and images with incomplete input data. We introduce approaches for such data interpolation and approximation from limited sensors, into the domain of analyzing and visualizing dynamic scenes. Data from dynamic scenes is subject to constraints due to the spatial layout of the scene and/or the configurations of video cameras in use. Such constraints include: (1) sparsely available cameras observing the scene, (2) limited field of view provided by the cameras in use, (3) incomplete motion at a specific moment, and (4) varying frame rates due to different exposures and resolutions.
In this thesis, we establish these forms of incompleteness in the scene, as spatio-temporal uncertainties, and propose solutions for resolving the uncertainties by applying scattered data approximation into a spatio-temporal domain.
The main contributions of this research are as follows: First, we provide an efficient framework to visualize large-scale dynamic scenes from distributed static videos. Second, we adopt Radial Basis Function (RBF) interpolation to the spatio-temporal domain to generate global motion tendency. The tendency, represented by a dense flow field, is used to optimally pan and tilt a video camera. Third, we propose a method to represent motion trajectories using stochastic vector fields. Gaussian Process Regression (GPR) is used to generate a dense vector field and the certainty of each vector in the field. The generated stochastic fields are used for recognizing motion patterns under varying frame-rate and incompleteness of the input videos. Fourth, we also show that the stochastic representation of vector field can also be used for modeling global tendency to detect the region of interests in dynamic scenes with camera motion. We evaluate and demonstrate our approaches in several applications for visualizing virtual cities, automating sports broadcasting, and recognizing traffic patterns in surveillance videos.
5
Sutherland, Alasdair. "Analogue VLSI for temporal frequency analysis of visual data." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/11441.
Abstract:
When viewed with an electronic imager, any variation in light intensity over time can provide valuable information regarding the nature of the object or light source causing the intensity change. By estimating the frequency of such light intensity variations, temporal frequencies can be extracted from the visual data, which may prove useful in a variety of applications. For instance, certain objects exhibit unique temporal frequencies, which could facilitate identification or classification. Other potential applications include remote, early failure detection for rotating machinery, as well as the possible detection of cancerous breasts using infra-red imaging techniques. The aim of the research reported in this thesis is the development of a CMOS image-processor, capable of extracting such temporal frequencies from any scene it is exposed to. In addition to finding the fundamental frequency, the sensor aims to extract the relative strength of up to the first four harmonics, performing a Fourier style decomposition of the incident light intensity into a temporal frequency signature. A heavy emphasis was placed on low power operation, leading to an investigation of analogue signal processing techniques with transistors biased in the subthreshold region of operation. The parallel processing advantages of combining light sensitive elements with signal processing elements in each pixel were also investigated, resulting in a system incorporating focal-plane computation. Software simulations of various novel system level algorithms are reported, with the successful approach used to create fundamental frequency maps of test data. The approach was also simulated to prove its robustness to noise commonly found in CMOS imager implementations. Circuits are presented which accurately extract the fundamental frequency of variations in light intensity, while benefiting from the low power consumption of subthreshold analogue circuitry. A novel algorithm which places a band pass filter onto the fundamental frequency of any incident light intensity with an accuracy of 3 % is also presented. The system can tune from 20 Hz to 10 kHz at a maximum rate of 9 kHz/s, and can be considered the first step in the creation of a single-chip pseudo-Fourier light intensity processing unit.
6
Wu, Elizabeth. "Spatio-Temporal Data Mining and Analysis of Precipitation Extremes." Thesis, The University of Sydney, 2008. https://hdl.handle.net/2123/28120.
Abstract:
The work in this thesis aimed to discover new ways to analyse and mine information about precipitation extremes in South America from spatio-temporal data. This was done in two ways. First, analysis was performed through the use of statistical measures that provided insight into the behaviour of precipitation extremes between regions. Second, a new spatio-temporal outlier detection algorithm was introduced to discover moving outliers from the data.
7
Tao, Yufei. "Indexing and query processing of spatio-temporal data /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.
Abstract:
Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
8
Chen, Feng. "Efficient Algorithms for Mining Large Spatio-Temporal Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19220.
Abstract:
Knowledge discovery on spatio-temporal datasets has attractedgrowing interests. Recent advances on remote sensing technology mean
that massive amounts of spatio-temporal data are being collected,
and its volume keeps increasing at an ever faster pace. It becomes
critical to design efficient algorithms for identifying novel and
meaningful patterns from massive spatio-temporal datasets. Different
from the other data sources, this data exhibits significant
space-time statistical dependence, and the assumption of i.i.d. is
no longer valid. The exact modeling of space-time dependence will
render the exponential growth of model complexity as the data size
increases. This research focuses on the construction of efficient
and effective approaches using approximate inference techniques for
three main mining tasks, including spatial outlier detection, robust
spatio-temporal prediction, and novel applications to real world
problems.
Spatial novelty patterns, or spatial outliers, are those data points
whose characteristics are markedly different from their spatial
neighbors. There are two major branches of spatial outlier detection
methodologies, which can be either global Kriging based or local
Laplacian smoothing based. The former approach requires the exact
modeling of spatial dependence, which is time extensive; and the
latter approach requires the i.i.d. assumption of the smoothed
observations, which is not statistically solid. These two approaches
are constrained to numerical data, but in real world applications we
are often faced with a variety of non-numerical data types, such as
count, binary, nominal, and ordinal. To summarize, the main research
challenges are: 1) how much spatial dependence can be eliminated via
Laplace smoothing; 2) how to effectively and efficiently detect
outliers for large numerical spatial datasets; 3) how to generalize
numerical detection methods and develop a unified outlier detection
framework suitable for large non-numerical datasets; 4) how to
achieve accurate spatial prediction even when the training data has
been contaminated by outliers; 5) how to deal with spatio-temporal
data for the preceding problems.
To address the first and second challenges, we mathematically
validated the effectiveness of Laplacian smoothing on the
elimination of spatial autocorrelations. This work provides
fundamental support for existing Laplacian smoothing based methods.
We also discovered a nontrivial side-effect of Laplacian smoothing,
which ingests additional spatial variations to the data due to
convolution effects. To capture this extra variability, we proposed
a generalized local statistical model, and designed two fast forward
and backward outlier detection methods that achieve a better balance
between computational efficiency and accuracy than most existing
methods, and are well suited to large numerical spatial datasets.
We addressed the third challenge by mapping non-numerical variables
to latent numerical variables via a link function, such as logit
function used in logistic regression, and then utilizing
error-buffer artificial variables, which follow a Student-t
distribution, to capture the large valuations caused by outliers. We
proposed a unified statistical framework, which integrates the
advantages of spatial generalized linear mixed model, robust spatial
linear model, reduced-rank dimension reduction, and Bayesian
hierarchical model. A linear-time approximate inference algorithm
was designed to infer the posterior distribution of the error-buffer
artificial variables conditioned on observations. We demonstrated
that traditional numerical outlier detection methods can be directly
applied to the estimated artificial variables for outliers
detection. To the best of our knowledge, this is the first
linear-time outlier detection algorithm that supports a variety of
spatial attribute types, such as binary, count, ordinal, and
nominal.
To address the fourth and fifth challenges, we proposed a robust
version of the Spatio-Temporal Random Effects (STRE) model, namely
the Robust STRE (R-STRE) model. The regular STRE model is a recently
proposed statistical model for large spatio-temporal data that has a
linear order time complexity, but is not best suited for
non-Gaussian and contaminated datasets. This deficiency can be
systemically addressed by increasing the robustness of the model
using heavy-tailed distributions, such as the Huber, Laplace, or
Student-t distribution to model the measurement error, instead of
the traditional Gaussian. However, the resulting R-STRE model
becomes analytical intractable, and direct application of
approximate inferences techniques still has a cubic order time
complexity. To address the computational challenge, we reformulated
the prediction problem as a maximum a posterior (MAP) problem with a
non-smooth objection function, transformed it to a equivalent
quadratic programming problem, and developed an efficient
interior-point numerical algorithm with a near linear order
complexity. This work presents the first near linear time robust
prediction approach for large spatio-temporal datasets in both
offline and online cases.
Ph. D.
9
Wang, Zilong. "Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models." UKnowledge, 2012. http://uknowledge.uky.edu/statistics_etds/3.
Abstract:
Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time.
10
Yang, Kit-ling, and 楊潔玲. "Statistical analysis of temporal and spatial variations in suicide data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841811.
11
Yang, Kit-ling. "Statistical analysis of temporal and spatial variations in suicide data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42841811.
12
Feng, Lingbing. "Essays on spatio-temporal data analysis : imputation, modelling and prediction." Phd thesis, Canberra, ACT : The Australian National University, 2015. http://hdl.handle.net/1885/149790.
13
Zhang, Xiang. "Analysis of Spatial Data." UKnowledge, 2013. http://uknowledge.uky.edu/statistics_etds/4.
Abstract:
In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously.
Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial-temporal lattice data. We propose a general asymptotic framework for spatial-temporal linear models and investigate the property of maximum likelihood estimates under such framework. Mild regularity conditions on the spatial-temporal weight matrices will be put in order to derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates.
For spatial data, aside from traditional likelihood-based method, a variety of literature has discussed Bayesian approach to estimate the correlation (auto-covariance function) among spatial data, especially Zheng et al. (2010) proposed a nonparametric Bayesian approach to estimate a spectral density. We will also discuss nonparametric Bayesian approach in analyzing spatial data. We will propose a general procedure for constructing a multivariate Feller prior and establish its theoretical property as a nonparametric prior. A blocked Gibbs sampling algorithm is also proposed for computation since the posterior distribution is analytically manageable.
14
Nunes, Santiago Augusto. "Análise espaço-temporal de data streams multidimensionais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102016-152137/.
Abstract:
Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resultam em alterações na distribuição dos dados). Além disso, esses conjuntos de dados podem possuir características espaciais, como a localização geográfica de sensores, que podem ser úteis no processo de análise. A detecção dessas variações de comportamento que considere os aspectos da evolução temporal, assim como as características espaciais dos dados, é relevante em alguns tipos de aplicação, como o monitoramento de eventos climáticos extremos em pesquisas na área de Agrometeorologia. Nesse contexto, esse projeto de mestrado propõe uma técnica para auxiliar a análise espaço-temporal em data streams multidimensionais que contenham informações espaciais e não espaciais. A abordagem adotada é baseada em conceitos da Teoria de Fractais, utilizados para análise de comportamento temporal, assim como técnicas para manipulação de data streams e estruturas de dados hierárquicas, visando permitir uma análise que leve em consideração os aspectos espaciais e não espaciais simultaneamente. A técnica desenvolvida foi aplicada a dados agrometeorológicos, visando identificar comportamentos distintos considerando diferentes sub-regiões definidas pelas características espaciais dos dados. Portanto, os resultados deste trabalho incluem contribuições para a área de mineração de dados e de apoio a pesquisas em Agrometeorologia.Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology.
15
Luo, Ling. "Temporal Modelling for Customer Behaviour." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17554.
Abstract:
The customer behaviour analysis is a critical component of business intelligence and marketing. Understanding the customer behaviour can support businesses to develop appropriate strategies and communicate with the right customers at the right time. In this thesis, we propose advanced temporal modelling techniques for customer behaviour analysis, in order to track customer behaviour changes, identify different types of customers and compare customer responses to marketing strategies and programs. We design temporal modelling techniques based on the temporal collaborative filtering and stochastic processes for customer behaviour analysis. To handle the sparse and noisy purchase records, our methods utilise latent variable modelling to segment customers into groups and discover shared purchase patterns. Our methods can detect the long-term and short-term behaviour patterns due to various factors such as seasonal effects and preference drifts. Our methods can also monitor the evolution of customer groups and track how individual customers shift across groups. We conduct four case studies on a real-world supermarket health program, which encourages participants to adopt a healthier lifestyle. The participants can access a supporting website with personalised tools for the health program. They can also get discount on fruits and vegetables when purchasing in the supermarket. Through the case studies, we show the advantages of our techniques to model the customer behaviour. We also evaluate the impact of promotions and the health program on different types of customers. Our models and results can be used by business stakeholders to increase the customer engagement and optimise their promotional campaigns. Importantly, the proposed methods contribute to the body of knowledge in data mining and user behaviour analytics. These methods can be applied to model other types of behaviour such as activities on social networks and educational systems.
16
Oliveira, Guilherme do Nascimento. "Ordered stacks of time series for exploratory analysis of large spatio-temporal datasets." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/130557.
Abstract:
O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras-The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.
17
Jiang, Yexi. "Temporal Mining for Distributed Systems." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/1909.
Abstract:
Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent.
With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required.
From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached.
This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.
18
Cheng, Tao. "Automated safety analysis of construction site activities using spatio-temporal data." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47564.
Abstract:
During the past 10 years, construction was the leading industry of occupational fatalities when compared to other goods producing industries in the US. This is partially attributed to ineffective safety management strategies, specifically lack of automated construction equipment and worker monitoring. Currently, worker safety performance is measured and recorded manually, assessed subjectively, and the resulting performance information is infrequently shared among selected or all project stakeholders. Accurate and emerging remote sensing technology provides critical spatio-temporal data that has the potential to automate and advance the safety monitoring of construction processes.
This doctoral research focuses on pro-active safety utilizing radio-frequency location tracking (Ultra Wideband) and real-time three-dimensional (3D) immersive data visualization technologies. The objective of the research is to create a model that can automatically analyze the spatio-temporal data of the main construction resources (personnel, materials, and equipment), and automatically measure, assess, and visualize worker's safety performance. The research scope is limited to human-equipment interaction in a complex construction site layout where proximities among construction resources are omnipresent. In order to advance the understanding of human-equipment proximity issues, extensive data has been collected in various field trials and from projects with multiple scales. Computational algorithms developed in this research process the data to provide spatio-temporal information that is crucial for construction activity monitoring and analysis. Results indicate that worker's safety performance of selected activities can be automatically and objectively measured using the developed model.
The major contribution of this research is the creation of a proximity hazards assessment model to automatically analyze spatio-temporal data of construction resources, and measure, evaluate, and visualize their safety performance. This research will significantly contribute to transform safety measures in construction industry, as it can determine and communicate automatically safe and unsafe conditions to various project participants located on the field or remotely.
19
Pedreira, Gallego Carlos. "Recordings in the human temporal lobe : data analysis and spike sorting." Thesis, University of Leicester, 2010. http://hdl.handle.net/2381/8925.
Abstract:
In the present thesis we study the recording of neurons in the human medial temporal lobe (MTL). We take a dual approach to the topic concordant to the scientific nature of the problem. On the one hand, the MTL is one of the most studied structures on the brain, strongly correlated to the formation and retrieval of conscious memories. On the other hand, the direct recording of neurons is a challenging operation requiring advanced methods of signal processing. We used recordings from electrodes implanted in epileptic patients to study the behaviour of MTL neurons with strong responses to visual stimuli. We studied how the repeated stimulus presentation modulated the firing of these neurons. The results showed decreased activity with each presentation and differences between areas in the line of the suggested roles in previous works. We analysed the performance provided by the algorithms used for the extraction of single unit activity out of the signal recorded by electrodes in the brain: a process called spike sorting. The results quantified an inherent limitation to these algorithms, with a maximum number of detected units and significantly reduced performance for those neurons with low firing rate. In summary, the work presented here contributes to the understanding of the ongoing processes in MTL neurons and the problematic of recording the activity of neurons, in especial the ones with low levels of activity.
20
Jagdish, Deepak. "IMMERSION : a platform for visualization and temporal analysis of email data." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95606.
Abstract:
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 75-76).
Visual narratives of our lives enable us to reflect upon our past relationships, collaborations and significant life events. Additionally, they can also serve as digital archives, thus making it possible for others to access, learn from and reflect upon our life's trajectory long after we are gone. In this thesis, I propose and develop a webbased platform called Immersion, which reveals the network of relationships woven by a person over time and also the significant events in their life. Using only metadata from a person's email history, Immersion creates a visual account of their life that they can interactively explore for self-reflection or share it with others as a digital archive. In the first part of this thesis, I discuss the design, technical and privacy aspects of Immersion, lessons learnt from its large-scale deployment and the reactions it elicited from people. In the second part of this thesis, I focus on the technical anatomy of a new feature of Immersion called Storyline - an interactive timeline of significant life events detected from a person's email metadata. This feature is inspired by feedback obtained from people after the initial launch of the platform.
by Deepak Jagdish.
S.M.
21
Mello, Marcio Pupin. "Spectral-temporal and Bayesian methods for agricultural remote sensing data analysis." Instituto Nacional de Pesquisas Espaciais (INPE), 2013. http://urlib.net/sid.inpe.br/mtc-m19/2013/09.17.18.58.
Abstract:
Informações agrícolas confiáveis tem se tornado cada vez mais importantes para os tomadores de decisões. Especialmente quando são obtidas em tempo hábil, essas informações são altamente relevantes para o planejamento estratégico do país. Apesar de o sensoriamento remoto mostrar-se promissor para aplicações em mapeamento agrícola, com potencial de melhorar as estatísticas agrícolas oficiais, esse potencial não tem sido amplamente explorado. Existem poucos exemplos bem sucedidos do uso operacional do sensoriamento remoto para mapeamento sistemático de culturas agrícolas e, para garantir resultados precisos, eles são fortemente baseados em interpretação visual de imagens. De fato, apesar dos substanciais avanços em análise de dados de sensoriamento remoto, novas técnicas para automatizar a análise de dados em sensoriamento remoto com aplicações agrícolas são desejáveis, especialmente no propósito de manter a consistência e a precisão dos resultados. Neste contexto, existe uma demanda crescente pelo desenvolvimento e implementação de métodos automatizados de análise de dados de sensoriamento remoto com aplicações em agricultura. Assim, o principal objetivo desta tese é propor o desenvolvimento e a implementação de métodos para automatizar a análise de dados de sensoriamento remoto em aplicações agrícolas, com foco na consistência e precisão dos resultados. Este documento foi escrito como uma coleção de dois artigos, cada um com foco nos seguintes pontos: (i) análise multitemporal, multiespectral e multisensor, permitindo a descrição das variações espectrais de alvos agrícolas ao longo do tempo; e (ii) inteligência artificial na modelagem de fenômenos usando dados de sensoriamento remoto e informações complementares de maneira integrada. Dois estudos de caso referentes ao mapeamento da colheita da cana em São Paulo e ao mapeamento da soja no Mato Grosso foram usados para testar as metodologias batizadas de STARS e BayNeRD, respectivamente. Os resultados dos testes confirmaram que ambos os métodos propostos foram capazes de automatizar processos de análises de dados de sensoriamento remoto com aplicações agrícolas, com consistência e precisão.Reliable agricultural statistics has become increasingly important to decision makers. Especially when timely obtained, agricultural information is highly relevant to the strategic planning of the country. Although remote sensing shows to be of great potential for agricultural mapping applications, with the benefit of further improving official agricultural statistics, its potential has not been fully explored. There are very few successful examples of operational remote sensing application for systematic mapping of agricultural crops, and they are strongly supported by visual image interpretation to allow accurate results. Indeed, despite the substantial advances in remote sensing data analysis, techniques to automate remote sensing data analysis focusing on agricultural mapping applications are highly valuable but have to maintain consistency and accuracy. In this context, there continues to be a demand for development and implementation of computer aided methods to automate the processes of analyzing remote sensing datasets for agriculture applications. Thus, the main objective of this thesis is to propose implementation of computer aided methodologies to automate, maintaining consistency and accuracy, processes of remote sensing data analyses focused on agricultural thematic mapping applications. This thesis was written as a collection of two papers related to a core theme, each addressing the following main points: (i) multitemporal, multispectral and multisensor image analysis that allow the description of spectral changes of agricultural targets over time; and (ii) artificial intelligence in modeling phenomena using remote sensing and ancillary data. Study cases of sugarcane harvest in São Paulo and soybean mapping in Mato Grosso were used to test the proposed methods named STARS and BayNeRD, respectively. The two methods developed and tested confirm that remotely sensed (and ancillary) data analysis can be automated with computer aided methods to model a range of cropland phenomena for agriculture applications, maintaining consistency and accuracy.
22
Rajamanoharan, Georgia. "Towards spatial and temporal analysis of facial expressions in 3D data." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/31549.
Abstract:
Facial expressions are one of the most important means for communication of emotions and meaning. They are used to clarify and give emphasis, to express intentions, and form a crucial part of any human interaction. The ability to automatically recognise and analyse expressions could therefore prove to be vital in human behaviour understanding, which has applications in a number of areas such as psychology, medicine and security. 3D and 4D (3D+time) facial expression analysis is an expanding field, providing the ability to deal with problems inherent to 2D images, such as out-of-plane motion, head pose, and lighting and illumination issues. Analysis of data of this kind requires extending successful approaches applied to the 2D problem, as well as the development of new techniques. The introduction of recent new databases containing appropriate expression data, recorded in 3D or 4D, has allowed research into this exciting area for the first time. This thesis develops a number of techniques, both in 2D and 3D, that build towards a complete system for analysis of 4D expressions. Suitable feature types, designed by employing binary pattern methods, are developed for analysis of 3D facial geometry data. The full dynamics of 4D expressions are modelled, through a system reliant on motion-based features, to demonstrate how the different components of the expression (neutral-onset-apex-offset) can be distinguished and harnessed. Further, the spatial structure of expressions is harnessed to improve expression component intensity estimation in 2D videos. Finally, it is discussed how this latter step could be extended to 3D facial expression analysis, and also combined with temporal analysis. Thus, it is demonstrated that both spatial and temporal information, when combined with appropriate 3D features, is critical in analysis of 4D expression data.
23
Horton, Michael. "Algorithms for the Analysis of Spatio-Temporal Data from Team Sports." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/17755.
Abstract:
Modern object tracking systems are able to simultaneously record trajectories—sequences of time-stamped location points—for large numbers of objects with high frequency and accuracy. The availability of trajectory datasets has resulted in a consequent demand for algorithms and tools to extract information from these data. In this thesis, we present several contributions intended to do this, and in particular, to extract information from trajectories tracking football (soccer) players during matches. Football player trajectories have particular properties that both facilitate and present challenges for the algorithmic approaches to information extraction. The key property that we look to exploit is that the movement of the players reveals information about their objectives through cooperative and adversarial coordinated behaviour, and this, in turn, reveals the tactics and strategies employed to achieve the objectives. While the approaches presented here naturally deal with the application-specific properties of football player trajectories, they also apply to other domains where objects are tracked, for example behavioural ecology, traffic and urban planning.
24
Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.
Abstract:
Data mining is the extraction of interesting non-trivial, implicit, previously unknown and
potentially useful information or patterns from data in large databases. Association rule
mining is a data mining method that seeks to discover associations among transactions encoded
within a database. Data mining on spatio-temporal data takes into consideration the
dynamics of spatially extended systems for which large amounts of spatial data exist, given
that all real world spatial data exists in some temporal context. We need fuzzy sets in mining
association rules from spatio-temporal databases since fuzzy sets handle the numerical
data better by softening the sharp boundaries of data which models the uncertainty embedded
in the meaning of data. In this thesis, fuzzy association rule mining is performed
on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed
for fuzzy spatio-temporal data cube construction. Besides the performance criteria
interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined
to be the metrics for the comparison of association rule mining techniques. Fuzzy association
rule mining using spatio-temporal data cubes and Apriori algorithm performed
within the scope of this thesis are compared using these metrics. Real meteorological data
(precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed
using data cube and Apriori algorithm in order to generate the fuzzy association rules.
25
Gupta, Prakriti. "Spatio-Temporal Analysis of Urban Data and its Application for Smart Cities." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/87418.
Abstract:
With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing
cities, data is being generated, collected and analyzed to solve urban problems in the areas
of transportation, epidemiology, emergency management, economics, and sustainability etc.
The work in this area basically involves analyzing one or more types of data to identify
and characterize their impact on other urban phenomena like traffic speed and ride-sharing,
spread of diseases, emergency evacuation, share market and electricity demand etc. In this
work, we perform spatio-temporal analysis of various urban datasets collected from different
urban application areas. We start with presenting a framework for predicting traffic demand
around a location of interest and explain how it can be used to analyze other urban activities.
We use a similar method to characterize and analyze spatio-temporal criminal activity in
an urban city. At the end, we analyze the impact of nearby traffic volume on the electric
vehicle charging demand at a charging station.Master of Science
26
Turner, Joanne. "Temporal and spatial host-pathogen models with diverse types of transmission." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343605.
27
Soale, Abdul-Nasah. "Spatio-Temporal Analysis of Point Patterns." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3120.
Abstract:
In this thesis, the basic tools of spatial statistics and time series analysis are applied to the case study of the earthquakes in a certain geographical region and time frame. Then some of the existing methods for joint analysis of time and space are described and applied. Finally, additional research questions about the spatial-temporal distribution of the earthquakes are posed and explored using statistical plots and models. The focus in the last section is in the relationship between number of events per year and maximum magnitude and its effect on how clustered the spatial distribution is and the relationship between distances in time and space in between consecutive events as well as the distribution of the distances.
28
Persson, Mattias. "A Survey of Methods for Visualizing Spatio-temporal Data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168089.
Abstract:
Olika typer av data genereras kontinuerligt varje sekund och för att kunna analysera denna data måste den transformeras till någon typ av visuell representation. En vanlig typ av data är spatio-temporal data, vilket är data som existerar i både rymd och tid. Hur denna typ av data kan visualiseras har undersökts under en lång period och området är fortfarande relevant idag. Ett antal metoder har undersökts i detta arbete och en genomgående litteraturstudie har genomförts. En applikation som implementerar ett antal av dessa undersökta metoder för att visualisera klimatdata har även skapats.Different kinds of data is generated continuously every second and in order to be ableto analyze this data it has to be transformed into some kind of visual representation. Onecommon type of data is spatio-temporal data, which is data that exists in both space andtime. How to visualize this kind of data have been researched for a long time and is still avery relevant subject to expand on today. A number of approaches have been explored inthis work. An extensive literature study has also been performed and can be read in thisreport. The study has been divided into different classifications of spatio-temporal dataand the visual representations are structured by these classes.Another contribution of this thesis is a climate data application to visualize spatiotemporaldata sets of temperatures collected for several countries in the world. This applicationimplements several of the visual representations presented in the survey includedin this thesis. This resulted in a four display application, each showing a different aspect ofthe chosen data sets that consisted of climate data. The result shows how effective multiplelinked views are in order to understand different characteristics of the data.
29
Pradhananga, Nipesh. "Construction site safety analysis for human-equipment interaction using spatio-temporal data." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52326.
Abstract:
The construction industry has consistently suffered the highest number of fatalities among all human involved industries over the years. Safety managers struggle to prevent injuries and fatalities by monitoring at-risk behavior exhibited by workers and equipment operators. Current methods of identifying and reporting potential hazards on site involve periodic manual inspection, which depends upon personal judgment, is prone to human error, and consumes enormous time and resources. This research presents a framework for automatic identification and analysis of potential hazards by analyzing spatio-temporal data from construction resources. The scope of the research is limited to human-equipment interactions in outdoor construction sites involving ground workers and heavy equipment. A grid-based mapping technique is developed to quantify and visualize potentially hazardous regions caused by resource interactions on a construction site. The framework is also implemented to identify resources that are exposed to potential risk based on their interaction with other resources. Cases of proximity and blind spots are considered in order to create a weight-based scoring approach for mapping hazards on site. The framework is extended to perform ``what-if'' safety analysis for operation planning by iterating through multiple resource configurations. The feasibility of using both real and simulated data is explored. A sophisticated data management and operation analysis platform and a cell-based simulation engine are developed to support the process. This framework can be utilized to improve on-site safety awareness, revise construction site layout plans, and evaluate the need for warning or training workers and equipment operators. It can also be used as an education and training tool to assist safety managers in making better, more effective, and safer decisions.
30
Corcoran, Jonathan. "Computational techniques for the geo-temporal analysis of crime and disorder data." Thesis, University of South Wales, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289394.
31
Yin, Jiangyong. "Bayesian Analysis of Non-Gaussian Stochastic Processes for Temporal and Spatial Data." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406928537.
32
KHALIQ, ALEEM. "Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2839838.
33
Gautier, Jacques. "GrAPHiSTUne approche d’analyse exploratoire pour l’identification des dynamiques des phénomènes spatio-temporels." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAS025/document.
Abstract:
Les données permettant de décrire des phénomènes spatio-temporels sont de plus en plus nombreuses. Ces nouvelles données peuvent alors être éloignées de celles habituellement observées pour l'étude de certains phénomènes. Leur analyse, selon une approche hypothético-déductive telle qu'elle est majoritairement effectuée en statistique et dans les SIG, peut ainsi passer sous silence certaines informations insoupçonnées, mais pertinentes, sur les dynamiques de ces phénomènes spatio-temporels.Il peut alors être intéressant de simplement donner à voir les données, pour observer ce qu'elles ont à montrer, avant de les analyser. Ce principe est celui de l'analyse exploratoire: le procédé est de permettre à un utilisateur d'effectuer une exploration libre des données, au moyen de représentations visuelles, afin de mettre en lumière des structures ou des relations insoupçonnées. Aujourd'hui, l'analyse exploratoire est notamment possible au moyen d'environnements de visualisation, intégrant différentes représentations graphiques et cartographiques interactives.Les environnements de visualisation sont majoritairement développés de manière ad hoc, dans le cadre d'une thématique particulière. Or l'émergence constante de nouvelles données incite à promouvoir des méthodes d'analyse applicables à des phénomènes de différentes natures. En fonction de la problématique dans laquelle s'insèrent ces derniers, les dynamiques sur lesquelles va se focaliser l'analyse diffèrent. Analyser un phénomène météorologique dans un but de prévision implique de s’intéresser aux récurrences cycliques du phénomène. Analyser l'évolution d'une population pour la mise en place de politiques publiques implique d’analyser ce phénomène sur le temps long et selon différentes zones de l’espace.Notre objectif est de proposer une méthode d'analyse exploratoire des phénomènes spatio-temporels et de leurs dynamiques, indépendante du thème traité. Pour cela, nous proposons un environnement de géovisualisation, GrAPHiST (Géovisualisation pour l'Analyse des PHenomenes Spatio-Temporels), permettant l'analyse de différentes dynamiques, selon différentes échelles spatiales et temporelles (linéaires ou cycliques). Développer cet environnement implique de s’interroger sur la modélisation du changement dans l’espace, la nature des dynamiques spatio-temporelles à étudier, et les outils visuels et interactifs permettant de les identifier.Ainsi, les contributions de notre recherche se situent à plusieurs niveaux :- une modélisation générique des phénomènes spatio-temporels, sous la forme de séries événementielles;- de nouvelles méthodes de représentations graphiques et interactives, autorisant la recherche et l'identification des dynamiques spatio-temporelles, notamment: l'introduction de diagrammes temporels interactifs permettant la recherche visuelle de récurrences cycliques dans les données spatio-temporelles; l'utilisation de règles de symbologie permettant la visualisation des relations entre les composantes temporelle et spatiale des phénomènes; de nouvelles méthodes de représentations des agrégats d'événements proches, permettant d'identifier des structures dans leur distribution spatio-temporelle;- la formalisation d’une approche d'analyse exploratoire des dynamiques spatio-temporelles, déclinée en plusieurs scénarios selon l’objectif poursuivi.Nous validons notre approche en l'appliquant à l'analyse de différents jeux de données. L'objectif est de vérifier la possibilité d'identifier des dynamiques, relatives au temps linéaire ou cyclique, au moyen de GrAPHiST, et d'illustrer le caractère générique de l'approche, ainsi que les opportunités d'analyse offertes par l'environnementDatasets allowing the description of spatio-temporal phenomena are becoming ever more numerous. These new data can be very different from those usually observed for studying spatio-temporal phenomena. An analysis through a hypothetico-deductive approach, like is mainly done in statistic and GIS domains, can ignore some unsuspected, but relevant, information about the dynamics of these spatio-temporal phenomena.It can be interesting then, to just present the data, to observe what they have to show, before analysing them. This is the principle of the exploratory data analysis: the process is to allow a user to freely explore data, through visual representations, in order to highlight unsuspected structures or relationships. Today, exploratory analysis is possible through visualization environments, which integrate different graphic or cartographic interactive representations.Visualization environments are mainly developed in an ad hoc manner, in the context of a particular thematic field. However, the constant appearance of new data encourages promoting analysis methods, which could be applied to several types of phenomena. According to the domain related to these phenomena, the analysis will be focused on different dynamics. Analysing a meteorological phenomenon, in a forecasting purpose, implies a focus on the cyclic recurrences of the phenomenon. Analysing the increase of a population, for the purpose of deciding public policies, implies an analysis of the phenomenon on a long-term, through different spatial areas.Our objective is to propose a method for the exploratory analysis of spatio-temporal phenomena and their dynamics, which would be independent of the topic. In order to achieve this, we propose a geovisualization environment, GrAPHiST (Géovisualisation pour l'Analyse des PHenomenes Spatio-Temporels; Geovisualization for spatio-temporal phenomena analysis), allowing the analysis of several dynamics, through different spatial and temporal (linear or cyclic) scales. Developing this environment implies to focus on how spatial changes are modelled, on the nature of the spatio-temporal dynamics we have to study, and on the visual and interactive tools, which allow the identification of these dynamics.So, the contributions of our research can be found at several levels:a generic modelling approach of spatio-temporal phenomena, in the form of event series;new graphical and interactive representation methods, which allow the searching and the identification of spatio-temporal dynamics, including: the introduction of interactive temporal diagrams, which allow the visual searching of cyclic recurrences in spatio-temporal data; the use of symbology rules, which allow the visualization of relationships between the spatial and temporal components of phenomena; new methods to represent aggregated closed events, which allow to identify structures in their spatio-temporal distribution;the formalization of an exploratory approach for the spatio-temporal dynamics analysis, divided into several scenarios, according to the purpose of the analysis.We validate our proposition by applying it to the analysis of several datasets. The objective is to verify the possibility to identify dynamics, related to linear or cyclic time, through the use of GrAPHiST, and to illustrate the generic aspect of the approach, as well as the analysis opportunities given by the environment
34
Kerracher, Natalie. "Tasks and visual techniques for the exploration of temporal graph data." Thesis, Edinburgh Napier University, 2017. http://researchrepository.napier.ac.uk/Output/977758.
Abstract:
This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same. The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way. While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated. Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques. The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified. The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems.
35
Yang, Yimin. "Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2254.
Abstract:
Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.
36
Woodring, Jonathan Lee. "Visualization of Time-varying Scientific Data through Comparative Fusion and Temporal Behavior Analysis." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243549189.
37
Colin, Brigitte. "Prediction of large spatio-temporal data using machine learning methods." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132263/1/Brigitte_Colin_Thesis.pdf.
Abstract:
This project was a step forward in statistical methodology for predicting green vegetation land cover in homogeneous grazing land. A supervised machine learning method, namely Boosted Regression Tree, was applied to satellite imagery. The predictive capabilities of the method was established using different data sets and approaches. Four research aims were achieved, including improved land-use prediction in a semi-arid region sensitive to climate variability.
38
Li, Han. "Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/70862.
Abstract:
Water temperature is a critical factor for the quality and biological condition of streams. Among various factors affecting stream water temperature, air temperature is one of the most important factors related to water temperature. To appropriately quantify the relationship between water and air temperatures over a large geographic region, it is important to accommodate the spatial and temporal information of the steam temperature. In this dissertation, I devote
effort to several statistical modeling techniques for analyzing bivariate spatial-temporal data in a stream temperature study.
In the first part, I focus our analysis on the individual stream. A time varying coefficient model (VCM) is used to study the relationship between air temperature and water temperature for each stream. The time varying coefficient model enables dynamic modeling of the relationship, and therefore can be used to enhance the understanding of water and air temperature relationships. The proposed model is applied to 10 streams in Maryland, West Virginia, Virginia, North Carolina and Georgia using daily maximum temperatures. The VCM approach increases the prediction accuracy by more than 50% compared to the simple linear regression model and the nonlinear logistic model.
The VCM that describes the relationship between water and air temperatures for each stream is represented by slope and intercept curves from the fitted model. In the second part, I consider water and air temperatures for different streams that are spatial correlated. I focus on clustering multiple streams by using intercept and slope curves estimated from the VCM. Spatial information is incorporated to make clustering results geographically meaningful. I further propose a weighted distance as a dissimilarity measure for streams, which provides a flexible framework to interpret the clustering results under different weights. Real data analysis shows that streams in same cluster share similar geographic features such as solar radiation, percent forest and elevation.
In the third part, I develop a spatial-temporal VCM (STVCM) to deal with missing data. The STVCM takes both spatial and temporal variation of water temperature into account. I develop a novel estimation method that emphasizes the time effect and treats the space effect as a varying coefficient for the time effect. A simulation study shows that the performance of the STVCM on missing data imputation is better than several existing methods such as the neural network and the Gaussian process. The STVCM is also applied to all 156 streams in this study to obtain a complete data record.Ph. D.
39
MARZI, DAVID. "Analysis of multi-temporal spaceborne Earth observation data to map selected land cover classes." Doctoral thesis, Università degli studi di Pavia, 2023. https://hdl.handle.net/11571/1470898.
Abstract:
Oggigiorno, per poter affrontare efficacemente i problemi ambientali su larga scala, la necessità di disporre di mappe di copertura del suolo affidabili e ad alta risoluzione spaziale e temporale è più che mai urgente. Infatti, numerosi contesti potrebbero trarre beneficio da tali prodotti come, ad esempio, il cambiamento climatico, la desertificazione, l'inverdimento dell'artico, la deforestazione, l'urbanizzazione, l'erosione del suolo, il monitoraggio delle foreste, la conservazione della biodiversità, la gestione delle aree urbane, la gestione delle risorse idriche, l'agricoltura, la sicurezza alimentare e molti altri. Siccome le variabili di interesse tendono a cambiare molto rapidamente nel tempo e nello spazio, la disponibilità di mappe di copertura del suolo frequenti e di buona qualità suscita un grande interesse.
Negli ultimi anni sono state prodotte diverse mappe tematiche e di copertura del suolo su scala regionale/globale le quali, tuttavia, spesso non soddisfano i requisiti imposti dalle applicazioni; ciò è dovuto principalmente al fatto che i prodotti esistenti sono stati generati da diversi sensori satellitari (ottici, radar o entrambi), diverse strategie di campionamento, diverse legende, diversi protocolli di validazione, ecc. Inoltre, la risoluzione spaziale e/o temporale di tali prodotti è spesso insufficiente per diverse applicazioni.
In questo lavoro di tesi è stato studiato come sfruttare dati multitemporali di tipo ottico e SAR (Synthetic Aperture Radar) per caratterizzare un insieme molto ristretto di classi, piuttosto che un'ampia gamma di tipi di copertura del suolo. Il lavoro presentato si concentra sulla vegetazione (tra cui specie arboree, praterie, arbusti ed altri), corpi idrici (tra cui laghi, mari, fiumi ed altri) e colture biologiche (in particolare, pratiche di agricoltura biologica).
Per quanto riguarda la vegetazione, la letteratura scientifica offre numerose metodologie consolidate, finalizzate alla mappatura delle coperture vegetative. Al contrario, gli approcci che sfruttano i sensori SAR come principale fonte di dati sono decisamente più rari. Per questo motivo, parte di questa tesi è dedicata all'analisi della potenzialità dei dati SAR multitemporali nel caratterizzare diversi tipi di vegetazione naturale.
Per quanto riguarda la mappatura dei corpi idrici, la letteratura tecnica fornisce diverse soluzioni basate sia su dati ottici che SAR. Tuttavia, la maggioranza delle metodologie analizzate presentano alcune limitazioni legate principalmente alla mancanza di automatismo degli algoritmi, l'impossibilità di utilizzare il modello in altre regioni di interesse, alla risoluzione spaziale relativamente bassa ed altri. Dal momento che la comunità sul cambiamento climatico necessita di informazioni tempestive relative allo stato dei corpi idrici a livello non solo locale/regionale ma anche globale, e che sia indipendente dalle condizioni meteorologiche delle diverse aree del mondo, in questa tesi si propone una metodologia volta a mappare i corpi idrici sfruttando sequenze temporali di dati SAR, che sia in grado di superare le limitazioni più gravi presenti negli approcci esistenti.
Infine, per quanto concerne la caratterizzazione dei terreni agricoli biologici, occorre rilevare e monitorare diversi aspetti, tra cui le operazioni di diserbo, le attività di fertilizzazione e le tecniche di lavorazione del terreno. A tal fine, sia i dati ottici multitemporali che i dati SAR vengono sfruttati per costruire piccoli blocchi che faranno parte di un sistema di monitoraggio dell'agricoltura biologica più complesso, volto a migliorare la trasparenza e la tracciabilità all'interno della catena di approvvigionamento alimentare biologica.
In generale, i risultati hanno dimostrato che le sequenze temporali di dati SAR e multispettrali possono essere impiegate con successo nella classificazione dei diversi tipi di copertura del suolo di cui sopra.Nowadays, the need for reliable, timely, high-resolution land cover maps is more than urgent if large-scale environmental problems are to be tackled effectively. Many different contexts would in fact benefit from such products, such as climate change, desertification, arctic greening, deforestation, urbanization, soil erosion, forest monitoring, conservation of biodiversity, urban area management, water resources management, agriculture, food security and many others. Due to the fact that the involved variables tend to change very rapidly in time and space, the availability of frequent and good quality global land cover products raises great interest. Several regional/global thematic and land cover maps have been delivered and other are expected, but they often do not meet the specific requirements of various applications; this is mainly due to the fact that all the existing products have been generated from different satellite sensors (optical, radar or both), different sampling strategies, different types of mapped land cover types, different validation protocols, etc. Moreover, the spatial and/or temporal resolution of these products is often insufficient for some applications. In this thesis work, we investigated how to leverage multitemporal optical and SAR data to characterize a very small set of classes rather than a full range of land cover types. Our work focuses on vegetation (including tree species, grasslands, shrublands and others), water bodies (including lakes, seas, rivers and others) and organic croplands (specifically, organic farming practices). Regarding vegetation, the technical literature offers numerous well-established methodologies aimed at mapping vegetated land covers. On the contrary, approaches that use SAR sensors as the main source of data are definitely more scarce. For this reason, part of this thesis work will be devoted to analyze the potential of multitemporal SAR data to characterize several types of natural vegetation. Regarding mapping of water bodies, the scientific literature provides several solutions based on optical and SAR data. However, almost all the analyzed methodologies have some limitations, mainly related to lack of automatism, impossibility to use the proposed method in other regions of interest, relatively low spatial resolution and others. Given the climate change community's need for timely information on the status of water bodies at the global level regardless of weather conditions, in this thesis a methodology aimed at mapping water bodies using sequences of SAR data, that is able to overcome the most severe limitations of the existing methodologies, is proposed. Finally, to characterize organic farmland, several aspects must be detected and monitored, including weed-killer operations, fertilization activities and tillage techniques. To do so, both multitemporal optical and SAR data are exploited to build small detection blocks, that will be part of a more complex organic farming monitoring system aimed at improving transparency and traceability within the organic food supply chain. In general, results showed that SAR and multispectral time series can be successfully employed to classify these land cover types.
40
Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.
Abstract:
My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data.
The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services.
The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time functions which are similar in shape. The time functions are decomposed into spatial global and time-dependent cluster effects using a semi-parametric model. We also assume that
the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across curves from nearby locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small number of time points, high noise level and varying dependence structures. Over all simulation settings, the spatial-functional clustering method outperforms existing model-based clustering methods. In the case study presented in this project, we focus on estimates and classifies service accessibility patterns varying over a large geographic area (California and Georgia) and over a period of 15 years. The focus of this study is on financial services but it generally applies to any other service operation.
The second research project of this part studies an association analysis of space-time varying processes, which is rigorous, computational feasible and implementable with standard software. We introduce general measures to model different aspects of the temporal and spatial association between processes varying in space and time. Using a nonparametric spatiotemporal model, we show that the proposed association estimators are asymptotically unbiased and consistent. We complement the point association estimates with simultaneous confidence bands to assess the uncertainty in the point estimates. In a simulation study, we evaluate the accuracy of the association estimates with respect to the sample size as well as the coverage of the confidence bands. In the case study in this project, we investigate the association between service accessibility and income level. The primary objective of this association analysis is to assess whether there are significant changes in the income-driven equity of financial service accessibility over time and to identify potential under-served markets.
The second part of the thesis discusses novel statistical methodology for analyzing multilevel functional data including a clustering method based on a functional ANOVA model and a spatio-temporal model for functional data with a nested hierarchical structure. In this part, I introduce and compare a series of clustering approaches for multilevel functional data. For brevity, I present the clustering methods for two-level data: multiple samples of random functions, each sample corresponding to a case and each random function within a sample/case corresponding to a measurement type. A cluster consists of cases which have similar within-case means (level-1 clustering) or similar between-case means (level-2 clustering). Our primary focus is to evaluate a model-based clustering to more straightforward hard clustering methods. The clustering model is based on a multilevel functional principal component analysis. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small vs. moderate number of time points, high noise level and small number of measurement types. We demonstrate the applicability of the clustering analysis to a real data set consisting of time-varying sales for multiple products sold by a large retailer in the U.S.
My ongoing research work in multilevel functional data analysis is developing a statistical model for estimating temporal and spatial associations of a series of time-varying variables with an intrinsic nested hierarchical structure. This work has a great potential in many real applications where the data are areal data collected from different data sources and over geographic regions of different spatial resolution.
41
Pedro, Alexandra Aguiar. "Análise temporal dos setores de aglomerados subnormais dos censos 2000 e 2010: o estudo de caso da subprefeitura de São Mateus no município de São Paulo-SP." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/8/8135/tde-29082016-110802/.
Abstract:
O propósito deste trabalho é investigar as características dos dados sobre aglomerados subnormais, na subprefeitura de São Mateus - município de São Paulo - SP, tendo em vista a realização de análises temporais. Os censos demográficos, por meio dos setores de aglomerados subnormais, são uma importante fonte de dados socioeconômicos e demográficos sobre favelas, coletados periodicamente e disponibilizados pelo IBGE para todos os municípios brasileiros. No entanto, pesquisadores e poder público estão sujeitos às controvérsias metodológicas e questões cartográficas que ainda dificultam o uso dessas informações. No presente estudo, foram identificadas e sistematizadas as características dos aglomerados subnormais. Posteriormente, essas informações foram verificadas quanto à sua ocorrência e significância por meio do estudo de caso, com sobreposição das malhas censitárias dos censos 2000 e 2010, e das bases cadastrais da prefeitura em um sistema de informação geográfica (SIG). Aprimoramentos foram observados nos processos e resultados do censo de 2010, comparado com o censo de 2000. Entretanto, os dados sobre aglomerados subnormais ainda apresentam características que dificultam a realização de análises temporais, destacando-se os seguintes resultados: a) 96,5% dos setores subnormais do estudo de caso, identificados como tal somente em 2010 são constituídos por favelas implantadas até 1999; b) 42,5% dos setores subnormais de 2010 referem-se à novas identificações de favelas em relação a 2000; c) 55% dos setores subnormais identificados em 2010 são resultantes de subdivisão ou agregação dos setores do censo 2000; d) em 26% dos setores subnormais em 2010 há conjuntos habitacionais ou residências não consideradas favelas pela prefeitura; e) três setores subnormais em 2010 não possuem em sua área, favelas cadastradas pela PMSP e quatro favelas cadastradas na PMSP não foram demarcadas como setores subnormais no censo 2010. As contradições nos dados censitários entre 2000 e 2010 e destes em relação às favelas da prefeitura tornam as análises temporais pouco precisas. O fato de que os dados estão agregados em setores que foram muito modificados entre um censo e outro agrava esta situação, na medida em que limita a comparação dos dados entre os períodos.The purpose of this research is to analyze the data on subnormal agglomerates (special enumeration districts to identify the most common kind of slum in Brazil, the favelas), provided by the Brazilian Statistics Office, through the case study area, São Mateus borough, located in Sao Paulo city, with regard to achieving temporal analysis. The population census, through the subnormal agglomerates enumeration districts, are an important source of socioeconomic and demographic data on favelas, collected periodically and available to all Brazilian municipalities. However, researchers and public authorities have been subject to methodological controversies and cartographic issues that still complicate the use of this information. This research identifies and systematizes the characteristics relating to subnormal agglomerates. In the sequence, this information is verified according to its occurrence and significance through the case study area, by overlaying the vector census zoning 2000-2010 and the cadastral databases from Sao Paulo city hall, in geographic information system (GIS). Improvements in the processes and results of 2010 census were observed, compared to the 2000 census. However, the subnormal agglomerates data still present characteristics that complicates the accomplishment of temporal analysis. The highlighted results are: a) 96.5% of the subnormal enumeration districts in the case study, identified only in 2010 are favelas implemented until 1999; b) 42.5% of subnormal enumeration districts in 2010 were new favelas identifications; c) 55% of subnormal enumeration districts identified in 2010 are the result of subdivision or aggregation of the 2000 census enumeration districts; d) in 26% of subnormal enumeration districts in 2010 there are social housing buildings or houses not considered favelas by the municipal cadastre; e) three subnormal enumeration districts in 2010 have no correspondence with favelas registered by the municipality and four favelas registered by the municipality were not delineated as subnormal agglomerates in the 2010 census. The contradictions in census data between 2000 and 2010, and the differences found in comparison to the favela municipal database become the temporal analysis less accurate. This imprecision tends to expand due the deep modifications on the enumeration districts, which limit the data comparison.
42
Hilley, David B. "Temporal streams programming abstractions for distributed live stream analysis applications /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31695.
Abstract:
Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.Committee Chair: Ramachandran, Umakishore; Committee Member: Clark, Nathan; Committee Member: Haskin, Roger; Committee Member: Pu, Calton; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.
43
Gao, Yuan. "Network analysis of international patent data: technology cohort, temporal dynamics and the role of trade." Thesis, IMT Alti Studi Lucca, 2018. http://e-theses.imtlucca.it/261/1/Gao_phdthesis.pdf.
Abstract:
Access to online electronic database and international patent
data harmonization has enabled more researchers to work
with patent data. This thesis joins the more recent studies
to apply network methodologies to patent data analysis in
order to understand the multi-variable interactions in innovation, knowledge flows, and technological trends.
The first chapter focuses on technology cohort in the patent
family networks and citation networks to investigate how technologies are integrated in utility invention and the patterns
over time. The consistent technological groups found in this
analysis are compared to the authority-defined system and
provide a more complete coverage.
Chapter 2 focuses on community evolution over time. The
stabilized Louvain method is adopted to improve consistency
and stability of community detection. The majority mapping
algorithm is incorporated for community tracking across time
slices. A new method is developed to identify sets of central
nodes. A case study of patent filed by applicants in Germany
is used to demonstrate and verify the method and the results.
In the last chapter, I apply the methods developed from the
first two chapters to the pharmaceutical sector. Motivated
by the needs to encourage pharmaceutical R&D and promote
globalized innovation, I focus on the effects of being central
with a favorable balance in international market on being central in global pharmaceutical R&D collaboration. The descriptive results and a further regression analysis shows positive
effects from export “Coreness” on co-invention centrality. Despite causality is not fully resolved, our results show a fundamental relationship between knowledge production and
trade in pharmaceuticals.
44
Hung, Hayley Shi Wen. "From visual saliency to video behaviour understanding." Thesis, Queen Mary, University of London, 2007. http://qmro.qmul.ac.uk/xmlui/handle/123456789/15027.
Abstract:
In a world of ever increasing amounts of video data, we are forced to abandon traditional methods of scene interpretation by fully manual means. Under such circumstances, some form of automation is highly desirable but this can be a very open ended issue with high complexity. Dealing with such large amounts of data is a non-trivial task that requires efficient selective extraction of parts of a scene which have the potential to develop a higher semantic meaning, alone, or in combination with others. In particular, the types of video data that are in need of automated analysis tend to be outdoor scenes with high levels of activity generated from either foreground or background. Such dynamic scenes add considerable complexity to the problem since we cannot rely on motion energy alone to detect regions of interest. Furthermore, the behaviour of these regions of motion can differ greatly, while still being highly dependent, both spatially and temporally on the movement of other objects within the scene. Modelling these dependencies, whilst eliminating as much redundancy from the feature extraction process as possible are the challenges addressed by this thesis. In the first half, finding the right mechanism to extract and represent meaningful features from dynamic scenes with no prior knowledge is investigated. Meaningful or salient information is treated as the parts of a scene that stand out or seem unusual or interesting to us. The novelty of the work is that it is able to select salient scales in both space and time in which a particular spatio-temporal volume is considered interesting relative to the rest of the scene. By quantifying the temporal saliency values of regions of motion, it is possible to consider their importance in terms of both the long and short-term. Variations in entropy over spatio-temporal scales are used to select a context dependent measure of the local scene dynamics. A method of quantifying temporal saliency is devised based on the variation of the entropy of the intensity distribution in a spatio-temporal volume over incraeasing scales. Entropy is used over traditional filter methods since the stability or predictability of the intensity distribution over scales of a local spatio-temporal region can be defined more robustly relative to the context of its neighbourhood, even for regions exhibiting high intensity variation due to being extremely textured. Results show that it is possible to extract both locally salient features as well as globally salient temporal features from contrasting scenerios. In the second part of the thesis, focus will shift towards binding these spatio-temporally salient features together so that some semantic meaning can be inferred from their interaction. Interaction in this sense, refers to any form of temporally correlated behaviour between any salient regions of motion in a scene. Feature binding as a mechanism for interactive behaviour understanding is particularly important if we consider that regions of interest may not be treated as particularly significant individually, but represent much more semantically when considered in combination. Temporally correlated behaviour is identified and classified using accumulated co-occurrences of salient features at two levels. Firstly, co-occurrences are accumulated for spatio-temporally proximate salient features to form a local representation. Then, at the next level, the co-occurrence of these locally spatio-temporally bound features are accumulated again in order to discover unusual behaviour in the scene. The novelty of this work is that there are no assumptions made about whether interacting regions should be spatially proximate. Furthermore, no prior knowledge of the scene topology is used. Results show that it is possible to detect unusual interactions between regions of motion, which can visually infer higher levels of semantics. In the final part of the thesis, a more specific investigation of human behaviour is addressed through classification and detection of interactions between 2 human subjects. Here, further modifications are made to the feature extraction process in order to quantify the spatiotemporal saliency of a region of motion. These features are then grouped to find the people in the scene. Then, a loose pose distribution model is extracted for each person for finding salient correlations between poses of two interacting people using canonical correlation analysis. These canonical factors can be formed into trajectories and used for classification. Levenshtein distance is then used to categorise the features. The novelty of the work is that the interactions do not have to be spatially connected or proximate for them to be recognised. Furthermore, the data used is outdoors and cluttered with non-stationary background. Results show that co-occurrence techniques have the potential to provide a more generalised, compact, and meaningful representation of dynamic interactive scene behaviour.
45
IACOBELLO, GIOVANNI. "Spatio-temporal analysis of wall-bounded turbulence: A multidisciplinary perspective via complex networks." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2829683.
46
Correia, Andrew William. "Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10828.
Abstract:
In the field of environmental epidemiology, there is a great deal of care required in constructing models that accurately estimate the effects of environmental exposures on human health. This is because the nature of the data that is available to researchers to estimate these effects is almost always observational in nature, making it difficult to adequately control for all potential confounders - both measured and unmeasured. Here, we tackle three different problems in which the goal is to accurately estimate the effect of an environmental exposure on various health outcomes.
47
Lee, Shang-Jung. "Prediction of graft-versus-host disease based on supervised temporal analysis on high-throughput flow cytometry data." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31670.
Abstract:
Despite recent advancements in human immune-genetics, graft-versus-host
disease (GvHD) continues to be the major and potentially fatal complication of
hematopoietic stem cell transplantations affecting up to 80% of transplant patients
[1]. Very little is known regarding the pathophysiologic mechanisms behind the
manifestation of either acute or chronic GvHD. Diagnosis and treatment assessment
are often hindered as they rely primarily on ambiguous clinical symptoms, such as
tissue inflammation. It is likely that the outcome for patients diagnosed with GvHD
could be improved if they were treated in a pre-emptive fashion, before the
development of full-scale clinical symptoms.
Using flow cytometry high content screening [2], 123 subsets of immune cells
were identified from blood samples taken at multiple time points from 31 patients
who underwent allogenic bone marrow transplantations. I assembled a novel
analysis pipeline specifically designed to process this high-throughput clinical flow
cytometry dataset. The pipeline included a novel quality assurance test [3] and
temporal classification via functional linear discriminant analysis [4]. Temporal
patterns of multiple immune cell abundances both after the transplantation and
around the acute GvHD diagnosis were screened for potential discriminative power
for either acute or chronic GvHD.
Among many potential discriminative patterns: higher proportion values in
immune cell with CD3⁺CD4⁺CD8β⁺ phenotype were found in acute GvHD patients
(21), compared to the patients unaffected by GvHD (3), between zero and 120 days
post-transplant. I also generated a list of recommendations for an extended study
designed to validate the current findings. The global approach of the highthroughput
flow cytometry technique and the novel temporal analysis pipeline,
implemented according to the list of recommendations would be beneficial in elucidating pathophysiologic mechanisms of complex immunologically based
diseases including GvHD.Science, Faculty of
Graduate
48
Kisilevich, Slava [Verfasser]. "Analysis of user generated spatio-temporal data : Learning from collections of geotagged photos [[Elektronische Ressource]] / Slava Kisilevich." Konstanz : Bibliothek der Universität Konstanz, 2012. http://d-nb.info/1026012724/34.
49
Khatamian, Yasha. "Investigating the limits of temporal clustering analysis for detecting epileptic activity in functional magnetic resonance imaging data." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96861.
Abstract:
Precise localization of epileptic activity is a necessity for those patients who may benefit from resective surgery. One common localization technique, EEG functional MRI (EEG-fMRI), localizes activity in an fMRI recording by finding blood oxygen level dependent (BOLD) signal correlates to epileptic events detected in a simultaneously recorded EEG. 2D temporal clustering analysis (2D-TCA) is a relatively new fMRI-based epileptic activity localization technique that breaks BOLD activity into components based on timing, finding epileptic activity without simultaneously recorded EEG. This study evaluated the ability of 2D-TCA to detect both simulated epileptic activity and activity detected in patients using EEG-fMRI. Although it was found that 2D-TCA could effectively detect epileptic activity with certain characteristics, it also detected activity not associated with epilepsy. As such, it was determined that 2D-TCA can only be used to validate epileptic activity localization by other means or to create hypotheses as to where activity may occur.Une localisation précise de l'activité épileptique est une nécessité pour les patients qui pourraient bénéficier d'une opération résective. L'EEG-IRM fonctionnelle (EEG-IRMf) est une nouvelle technique de localisation qui localise l'activité épileptique dans un enregistrement IRMf en trouvant un signal « blood oxygen level dependent » (BOLD) qui correspond à des événements épileptiques détectés simultanément par un enregistrement EEG. L'analyse temporelle groupée 2D (ATG-2D) est une technique de localisation de l'activité épileptique relativement nouvelle, qui est basée sur des données IRMf. Pour trouver l'activité épileptique, elle décompose l'activité BOLD en différentes composantes selon le moment où elles surviennent sans recourir à un enregistrement EEG simultané. Cette étude évalue la capacité de la technique ATG-2D à détecter une activité épileptique simulée ainsi que sa capacité à détecter une activité épileptique précédemment détectée chez des patients avec EEG-IRMf. Même nous avons montré que la technique ATG-2D pouvait détecter de façon efficace une activité épileptique ayant certaines caractéristiques, il a aussi été trouvé qu'elle détectait de l'activité non épileptique. Il a été déterminé que la technique ATG-2D pouvait seulement être utilisée pour valider une activité épileptique localisée par d'autres moyens ou pour formuler des hypothèses concernant l'endroit où l'activité pourrait survenir.
50
Yasumiishi, Misa. "Spatial and temporal analysis of human movements and applications for disaster response management| Using cell phone data." Thesis, State University of New York at Buffalo, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1600848.
Abstract:
This survey study examines cell phone usage data and focuses on the application of the data to disaster response management. Through the course of this study, the structure of cell phone usage data and its characteristics will be reviewed. Cell phone usage data provides us with valuable information about human movements and their activities. The uniqueness of the data is that it contains both spatial and temporal information and this information is free of fixed routes such as roads or any preset data capturing timing. In short, it is a very fluid kind of data which reflects our activities as humans with freedom of movement. Depending on data extraction methods, the data server can provide additional information such as application activities, battery level and charge activities. However, cell phone usage data contains shortcomings including data inconsistency and sparseness. Both the richness and the shortcomings of the data expose the hurdles required in data processing and force us to devise new ways to analyze this kind of data. Once the data has been properly analyzed, the findings can be applied to our real life problems including disaster response. By understanding human movement patterns using cell phone usage data, we will be able to allocate limited emergency resources more adequately. Even more, when disaster victims lose their cell phone functionality during a disaster, we might be able to identify or predict the locations of victims or evacuees and supply them with necessary assistance. The results of this study provide some insights to cell phone usage data and human movement patterns including the concentration of cell phone activities in specific zones and rather universal cell phone charging patterns. The potential of the data as a movement analysis resource and the application to disaster response is apparent. As a base to leverage the study to the next level, a possible conceptual model of human movement factors and data processing methods will be presented.