Analyzing the Data in OLAP Data Cubes

* Supported partially by the Bulgarian National Science Fund under Grant MM-1405/2004

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility.

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.

Host and network optimizations for performance enhancement and energy efficiency in data center networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Avaliação multidimensional da qualidade de vida em idosos: um estudo no Curimataú ocidental paraibano

The fast growth of the elderly population is a reality throughout the world and has become one of the greatest challenges for contemporary public health. When considering the increased life expectancy and the aging as a multidimensional phenomenon, one should highlight the need to investigate if the increase of longevity is associated with satisfactory levels of Quality of Life (QOL). This study has the objective of assessing the QOL of elderly people from the Paraíba’s Western Curimataú microregion, explained by its health and living conditions. This is a cross-sectional and observational study with quantitative design held with 444 elderly people from five cities: Barra de Santa Rosa, Cuité, Nova Floresta, Remígio e Sossego. In order to obtain information, the following instruments were used: I) Questionnaire for collection data related to the elderly population, for sociodemographic, clinical and behavioral characteristics; and II) WHOQOL-Old questionnaire, with a view to measuring and assessing QOL. Data were processed on the IBM-SPSS Statistics 20.0 software by means of the ANOVA (one-way), Student’s t, Mann-Whitney, Kruskal-Wallis and Pearson’s correlation tests, with p-values<0,05 accepted as being statistically significant. The results indicate a good global QOL (ETT=65,69%), with better assessment by elderly men, aged between 60 and 74 years, married, living with partner and children, without caregiver, physical activity practitioners, with up to one health problem before an aspect of multimorbidity and with very good and/or good assessment of basic needs. The self-reported stress showed a negative significant correlation before the global QOL, where the greater the perception of stress, the worse the assessment of QOL. In the faceted assessment of QOL, the Sensory Operation showed the best performance (ETF= 68,86%) and the Social Participation (SP) the worst (ETF=60,37%). In the multiple linear regression model, SP is singly responsible for 51,8% (R2=0,518) of explanation of the global QOL. In the intercorrelation among the WHOQOL-Old facets, only Death and Dying did not reveal significance. The harmony highlighted among the facets raises the need to ensure a comprehensive health care for the elderly population, especially in understanding the social participation as an intrinsic part of the QOL and that it requires the re-discussion and reconstruction of individual and collective, family and community, political and government actions. Hence, guaranteeing an active, healthy and participatory aging, with QOL, is the major challenge.

Avaliação multidimensional da qualidade de vida em idosos: um estudo no Curimataú ocidental paraibano

The fast growth of the elderly population is a reality throughout the world and has become one of the greatest challenges for contemporary public health. When considering the increased life expectancy and the aging as a multidimensional phenomenon, one should highlight the need to investigate if the increase of longevity is associated with satisfactory levels of Quality of Life (QOL). This study has the objective of assessing the QOL of elderly people from the Paraíba’s Western Curimataú microregion, explained by its health and living conditions. This is a cross-sectional and observational study with quantitative design held with 444 elderly people from five cities: Barra de Santa Rosa, Cuité, Nova Floresta, Remígio e Sossego. In order to obtain information, the following instruments were used: I) Questionnaire for collection data related to the elderly population, for sociodemographic, clinical and behavioral characteristics; and II) WHOQOL-Old questionnaire, with a view to measuring and assessing QOL. Data were processed on the IBM-SPSS Statistics 20.0 software by means of the ANOVA (one-way), Student’s t, Mann-Whitney, Kruskal-Wallis and Pearson’s correlation tests, with p-values<0,05 accepted as being statistically significant. The results indicate a good global QOL (ETT=65,69%), with better assessment by elderly men, aged between 60 and 74 years, married, living with partner and children, without caregiver, physical activity practitioners, with up to one health problem before an aspect of multimorbidity and with very good and/or good assessment of basic needs. The self-reported stress showed a negative significant correlation before the global QOL, where the greater the perception of stress, the worse the assessment of QOL. In the faceted assessment of QOL, the Sensory Operation showed the best performance (ETF= 68,86%) and the Social Participation (SP) the worst (ETF=60,37%). In the multiple linear regression model, SP is singly responsible for 51,8% (R2=0,518) of explanation of the global QOL. In the intercorrelation among the WHOQOL-Old facets, only Death and Dying did not reveal significance. The harmony highlighted among the facets raises the need to ensure a comprehensive health care for the elderly population, especially in understanding the social participation as an intrinsic part of the QOL and that it requires the re-discussion and reconstruction of individual and collective, family and community, political and government actions. Hence, guaranteeing an active, healthy and participatory aging, with QOL, is the major challenge.

Multidimensional Intrusion Detection System for IEC 61850 based SCADA Networks

Emerging cybersecurity vulnerabilities in supervisory control and data acquisition (SCADA) systems are becoming urgent engineering issues for modern substations. This paper proposes a novel intrusion detection system (IDS) tailored for cybersecurity of IEC 61850 based substations. The proposed IDS integrates physical knowledge, protocol specifications and logical behaviours to provide a comprehensive and effective solution that is able to mitigate various cyberattacks. The proposed approach comprises access control detection, protocol whitelisting, model-based detection, and multi-parameter based detection. This SCADA-specific IDS is implemented and validated using a comprehensive and realistic cyber-physical test-bed and data from a real 500kV smart substation.

Estratégias de cuidado de enfermagem singular e multidimensional à criança e a família no processo de hospitalização

No período de 2008 a 2010, o número de internações pediátricas, no Brasil, foi de 2.985.527. As causas desencadeadoras da hospitalização infantil podem ser biológicas, hereditárias, sociais, econômicas e ambientais. É comum o adoecimento ocorrer pela associação de causas, havendo crianças com predisposição para apresentarem múltiplos fatores de risco. Seja qual for a etiologia, a hospitalização frequentemente gera desconforto à criança e à sua família, por vivenciarem um ambiente impessoal e contraditório às condições do cotidiano. Cabe à equipe de enfermagem prestar um cuidado humanizado, singular e ampliado. Os objetivos do estudo são: compreender o significado do cuidado de enfermagem à criança hospitalizada e identificar estratégias de cuidado de enfermagem singular e multidimensional que atendam às necessidades da criança e da família no processo de hospitalização. Trata-se de uma pesquisa qualitativa, do tipo exploratório-descritivo. Os dados foram coletados por meio da técnica de Grupo Focal com a equipe de enfermagem que atuava em unidades pediátricas de duas instituições hospitalares do Rio Grande do Sul. Para tanto, foram realizados cinco encontros, no mês de setembro de 2013. Os dados foram analisados com base na Análise Focal Estratégica, a qual contemplou tanto as potencialidades e oportunidades, quanto as fragilidades e desafios no cuidado à criança hospitalizada, ampliando, assim, novas discussões para a busca de estratégias de cuidado de enfermagem singular e multidimensional. Os resultados foram sustentados por meio de duas produções científicas, quais sejam: “O cuidado à criança/família no processo de hospitalização na perspectiva de equipes de enfermagem”; “Ampliando estratégias de cuidado de enfermagem singular e multidimensional à criança/família em processo de hospitalização”. A primeira apresentou cinco categorias: Significando o cuidado de enfermagem à criança hospitalizada; Reconhecendo que o cuidado vai além do hospital; Relevância das figuras materna e paterna; Lidando com várias coisas; e Importância do cuidado multidimensional. A segunda produção resultou em três categorias: Encontrando estratégias criativas de cuidado de enfermagem à criança hospitalizada; Reconhecendo estratégias de cuidado à família no processo de hospitalização da criança; e Distinguindo estratégias de cuidado relacionadas à equipe de enfermagem no processo de hospitalização infantil. Foram garantidos todos os critérios que fundamentam a Resolução 466/12, que trata das pesquisas envolvendo seres humanos. Conclui-se que a equipe de enfermagem busca alternativas para minimizar os traumas relacionados à hospitalização, por meio do diálogo com a criança e sua família, a brinquedoteca e ludicidade para melhorar a aceitação da hospitalização, dentre outras.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

A Multidimensional Approach to Social Support: the Questionnaire on the Frequency of and Satisfaction with Social Support (QFSSS)

The Questionnaire on the Frequency of and Satisfaction with Social Support (QFSSS) was designed to assess the frequency of and the degree of satisfaction with perceived social support received from different sources in relation to three types of support: emotional, informational, and instrumental. This study tested the reliability of the questionnaire scores and its criterion and structural validity. The data were drawn from survey interviews of 2042 Spanish people. The results show high internal consistency (values of Cronbach's alpha ranged from .763 to .952). The correlational analysis showed significant positive associations between QFSSS scores and measures of subjective well-being and perceived social support, as well as significant negative associations with measures of loneliness (values of Pearson's r correlation ranged from .11 to .97). Confirmatory factor analysis using structural equation modelling verified an internal 4-factor structure that corresponds to the sources of support analysed: partner, family, friends, and community (values ranged from .93 to .95 for the Goodness of Fit Index (GFI); from .95 to .98 for the Comparative Fit Index (CFI); and from .10 to .07 for the Root Mean Square Error of Approximation (RMSEA)). These results confirm the validity of the QFSSS as a versatile tool which is suitable for the multidimensional assessment of social support.

Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores

ABSTRACT Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants' scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputation of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data.

Uma metodologia para a análise do desempenho sustentável dos postos de revenda de combustível na cidade de Natal/RN, com o uso de data envelopment analysis-DEA

The increasing use of fossil fuels in line with cities demographic explosion carries out to huge environmental impact in society. For mitigate these social impacts, regulatory requirements have positively influenced the environmental consciousness of society, as well as, the strategic behavior of businesses. Along with this environmental awareness, the regulatory organs have conquered and formulated new laws to control potentially polluting activities, mostly in the gas stations sector. Seeking for increasing market competitiveness, this sector needs to quickly respond to internal and external pressures, adapting to the new standards required in a strategic way to get the Green Badge . Gas stations have incorporated new strategies to attract and retain new customers whom present increasingly social demand. In the social dimension, these projects help the local economy by generating jobs and income distribution. In this survey, the present research aims to align the social, economic and environmental dimensions to set the sustainable performance indicators at Gas Stations sector in the city of Natal/RN. The Sustainable Balanced Scorecard (SBSC) framework was create with a set of indicators for mapping the production process of gas stations. This mapping aimed at identifying operational inefficiencies through multidimensional indicators. To carry out this research, was developed a system for evaluating the sustainability performance with application of Data Envelopment Analysis (DEA) through a quantitative method approach to detect system s efficiency level. In order to understand the systemic complexity, sub organizational processes were analyzed by the technique Network Data Envelopment Analysis (NDEA) figuring their micro activities to identify and diagnose the real causes of overall inefficiency. The sample size comprised 33 Gas stations and the conceptual model included 15 indicators distributed in the three dimensions of sustainability: social, environmental and economic. These three dimensions were measured by means of classical models DEA-CCR input oriented. To unify performance score of individual dimensions, was designed a unique grouping index based upon two means: arithmetic and weighted. After this, another analysis was performed to measure the four perspectives of SBSC: learning and growth, internal processes, customers, and financial, unifying, by averaging the performance scores. NDEA results showed that no company was assessed with excellence in sustainability performance. Some NDEA higher efficiency Gas Stations proved to be inefficient under certain perspectives of SBSC. In the sequence, a comparative sustainable performance and assessment analyzes among the gas station was done, enabling entrepreneurs evaluate their performance in the market competitors. Diagnoses were also obtained to support the decision making of entrepreneurs in improving the management of organizational resources and promote guidelines the regulators. Finally, the average index of sustainable performance was 69.42%, representing the efforts of the environmental suitability of the Gas station. This results point out a significant awareness of this segment, but it still needs further action to enhance sustainability in the long term

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.