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- Steinert, Rebecca
(författare)
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Probabilistic Fault Management in Networked Systems
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Technical advances in network communication systems (e.g. radio access networks) combined with evolving concepts based on virtualization (e.g. clouds), require new management algorithms in order to handle the increasing complexity in the network behavior and variability in the network environment. Current network management operations are primarily centralized and deterministic, and are carried out via automated scripts and manual interventions, which work for mid-sized and fairly static networks. The next generation of communication networks and systems will be of significantly larger size and complexity, and will require scalable and autonomous management algorithms in order to meet operational requirements on reliability, failure resilience, and resource-efficiency.A promising approach to address these challenges includes the development of probabilistic management algorithms, following three main design goals. The first goal relates to all aspects of scalability, ranging from efficient usage of network resources to computational efficiency. The second goal relates to adaptability in maintaining the models up-to-date for the purpose of accurately reflecting the network state. The third goal relates to reliability in the algorithm performance in the sense of improved performance predictability and simplified algorithm control.This thesis is about probabilistic approaches to fault management that follow the concepts of probabilistic network management (PNM). An overview of existing network management algorithms and methods in relation to PNM is provided. The concepts of PNM and the implications of employing PNM-algorithms are presented and discussed. Moreover, some of the practical differences of using a probabilistic fault detection algorithm compared to a deterministic method are investigated. Further, six probabilistic fault management algorithms that implement different aspects of PNM are presented.The algorithms are highly decentralized, adaptive and autonomous, and cover several problem areas, such as probabilistic fault detection and controllable detection performance; distributed and decentralized change detection in modeled link metrics; root-cause analysis in virtual overlays; event-correlation and pattern mining in data logs; and, probabilistic failure diagnosis. The probabilistic models (for a large part based on Bayesian parameter estimation) are memory-efficient and can be used and re-used for multiple purposes, such as performance monitoring, detection, and self-adjustment of the algorithm behavior.
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| 2. |
- Abghari, Shahrooz
(författare)
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Data Modeling for Outlier Detection
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.
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| 3. |
- Manell, Hannes, 1987-
(författare)
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Impaired Glucose Tolerance in Childhood Obesity : Contribution of Glucagon, GLP-1 and Inflammation
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the wake of increased obesity prevalence, impaired glucose tolerance (IGT) and type 2 diabetes (T2D) in childhood and adolescence is increasingly common. Given the negative impacts these conditions have on health over time, understanding the pathophysiology in those affected early in life is important. Both the proglucagon-derived peptides and low-grade inflammation have been implicated in the development of obesity-related complications. The aim of this thesis was to study across the glucose tolerance spectrum in children and adolescents with obesity 1) proglucagon-derived peptides glucagon, GLP-1 and glicentin, 2) dipeptidyl peptidase-4 (DPP-4) and its degradation of GLP-1 and 3) novel inflammatory markers. To this end, children and adolescents of the Uppsala Longitudinal Study of Childhood Obesity were studied. Children and adolescents with obesity had higher fasting plasma glucagon concentrations than lean controls. In particular visceral adiposity, hyperinsulinemia, triglycerides and free fatty acids (FFAs) were associated with high plasma glucagon concentrations. In isolated islets elevated FFAs caused hypersecretion of glucagon. In children and adolescents with IGT or T2D, fasting plasma glucagon was further elevated and the GLP-1 and glicentin response to an oral glucose tolerance test (OGTT) was decreased. In T2D plasma glucagon increased during the first 15 minutes of OGTT. Plasma DPP-4 concentrations were elevated in obesity and associated with lower proportion of intact GLP-1 but not with IGT. Several pro-inflammatory markers were elevated in children and adolescents with obesity but not further elevated in IGT or T2D with the exception of low plasma Tumor necrosis factor-related weak inducer of apoptosis (TWEAK) levels, which were associated with IGT, hyperinsulinemia and hyperglucagonemia. High plasma hepatocyte growth factor (HGF) concentration was associated with increased risk of further weight gain in children and adolescents with obesity.In conclusion, elevated glucagon concentration at fasting, a hyperglucagonemic response to OGTT and reduced GLP-1 and glicentin are characteristics of IGT and T2D development in childhood obesity reflecting altered usage of the proglucagon gene. DPP-4 concentrations are elevated in childhood obesity but not associated with IGT. Reduced circulating TWEAK was identified as a novel marker of IGT early in life. Children with obesity and high HGF are less likely to respond well to lifestyle intervention.
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| 4. |
- Rennemark, Mikael, 1951-, et al.
(författare)
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Factors related to frequent usage of the primary healthcare services in old age: findings from the Swedish National study on Aging and Care
- 2009
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Ingår i: Health & Social Care in the Community. - United Kingdom : Blackwell Publishing Ltd. - 0966-0410 .- 1365-2524. ; 17:3, s. 304-311
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Tidskriftsartikel (refereegranskat)abstract
- People aged 60 or more are the most frequent users of healthcare services.In this age range, however, both frequent and infrequent users can be found.Frequent users have high rates of illnesses. Previous research has found thatthe frequency may be influenced also by psychological and social factors.The aim of this study was to investigate to what degree such factors add tothe explanation of differences in number of visits to a physician. A crosssectionalstudy was conducted with a random sample consisting of 1017individuals, aged 60 to 78 years, from the Blekinge part of the SwedishNational Study on Aging and Care database. The data were collected during2001 to 2003. Hierarchical logistic regression analyses were used withfrequent (three visits or more during a year) and infrequent use as adichotomous dependent variable. The final statistical analyses included643 individuals (63% of the sample). Independent variables were sense ofcoherence (SOC), internal locus of control, education level and socialanchorage. Control variables were age, gender, functional ability andcomorbidity. The results showed that comorbidity was most strongly relatedto frequent use [adjusted odds ratio (OR) = 8.17, 95% confidence interval (CI)5.54–12.04]. In addition, SOC and internal locus of control had small, butsignificant effects on the odds of being a frequent user (adjusted OR = 1.03,95% CI 1.00–1.06 and adjusted OR = 1.14, 95% CI 1.02–1.27, respectively).The lower the SOC and the internal locus of control were, the higher were theodds of frequent use. Education level and social anchorage were unrelatedto frequency of use. The results indicate that frequent healthcare servicesusers are more ill than infrequent users. Psychological factors influence theuse only marginally, and social factors as well as age and gender are not bythemselves reason for frequent healthcare services use.
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