Current real-world applications are generating a large volume of datasets that are often continuously updated over time. Detecting outliers on such evolving datasets requires us to continuously update the result. Furthermore, the response time is very important for these time critical applications. This is challenging. First, the algorithm is complex; even mining outliers from a static dataset once is already very expensive. Second, users need to specify input parameters to approach the true outliers. While the number of parameters is large, using a trial and error approach online would be not only impractical and expensive but also tedious for the analysts. Worst yet, since the dataset is changing, the best parameter will need to be updated to respond to user exploration requests. Overall, the large number of parameter settings and evolving datasets make the problem of efficiently mining outliers from dynamic datasets very challenging. Thus, in this thesis, we design an exploration framework for detecting outliers in data streams, called EFO, which enables analysts to continuously explore anomalies in dynamic datasets. EFO is a continuous lightweight preprocessing framework. EFO embraces two optimization principles namely "best life expectancy" and "minimal trial," to compress evolving datasets into a knowledge-rich abstraction of important interrelationships among data. An incremental sorting technique is also used to leverage the almost ordered lists in this framework. Thereafter, the knowledge abstraction generated by EFO not only supports traditional outlier detection requests but also novel outlier exploration operations on evolving datasets. Our experimental study conducted on two real datasets demonstrates that EFO outperforms state-of-the-art technique in terms of CPU processing costs when varying stream volume, velocity and outlier rate.

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The main goal of this thesis work was to develop, implement and evaluate an algorithm that enables mining association rules from datasets that contain quantified distance information among the items. This was accomplished by extending and enhancing the Apriori Algorithm, which is the standard algorithm to mine association rules. The Apriori algorithm is not able to mine association rules that contain distance information among the items that construct the rules. This thesis enhances the main Apriori property by requiring itemsets forming rules to“deviate properly" in addition to satisfying the minimal support threshold. We say that an itemset deviates properly if all combinations of pair-wise distances among the items are highly conserved in the dataset instances where these items occur. This thesis introduces the notion of proper deviation and provides the precise procedure and measures that characterize it. Integrating the notion of distance preserving frequent itemset and proper deviation into the standard Apriori algorithm leads to the construction of our Distance-Based Association Rule Mining (DARM) algorithm. DARM can be applied in data mining and knowledge discovery from genetic, financial, retail, time sequence data, or any domain where the distance information between items is of importance. This thesis chose the area of gene expression and regulation in eukaryotic organisms as the application domain. The data from the domain was used to produce DARM rules. Sets of those rules were used for building predictive models. The accuracy of those models was tested. In addition, predictive accuracies of the models built with and without distance information were compared.

Distance-based partnerships are being increasingly used in health care and have previously been described to facilitate the training of nurses, researchers, and occupational therapists.1–6 In 2013, the Society of Hospital Medicine’s newly published guidelines for pediatric hospital medicine (PHM) programs indicated that strong leadership is critically important to a program’s success. Many smaller children’s hospitals have very few dedicated pediatric hospitalists, and these hospitalists might not have formal leadership or quality improvement (QI) training, resources, or dedicated time for QI work because of their clinical responsibilities. Similarly, pediatric residency programs at smaller institutions might lack robust inpatient QI experiences for their trainees. Leaders at Cincinnati Children’s Hospital Medical Center (Cincinnati) were approached by leaders at Niswonger Children’s Hospital (Niswonger) to complete a needs assessment of Niswonger’s inpatient program. Niswonger is a 69-bed children’s hospital colocated with Johnson City Medical Center, an adult hospital. These hospitals are located in a suburban area with a large rural catchment area. Both the adult and children’s hospitals are part of a larger health system, Mountain States Health Alliance. Niswonger is affiliated with East Tennessee State University (ETSU) Department of Pediatrics, which provided the majority of physician staffing. The needs assessment, completed in 2012, consisted of several site visits, observation of inpatient rounds, interviews with Niswonger faculty and staff, evaluation of available historical data, and collection of new data. Two main gaps in clinical care and training at Niswonger were identified. The first was the need for a dedicated hospitalist program with providers who did not have competing clinical responsibilities. The general pediatric inpatient unit was historically staffed by several ETSU faculty members, all of whom had primary responsibilities in other areas such as intensive care, outpatient primary care, and infectious disease and none of whom were dedicated pediatric hospitalists. These physicians would typically conduct inpatient teaching rounds in the morning and then resume other clinical responsibilities. The second was the need for QI training for the 19 residents in the ETSU pediatric residency program, an Accreditation Council for Graduate Medical Education requirement.

Several factors have influenced the recent development in charging of heavy goods vehicles in Europe. European legislation is changed and the political will is strong in many countries. The public awareness and acceptance is large for the need to put a price and a cost on the use of infrastructure. Environmental concerns are also actuating change in this direction. Systems are being introduced to support and enhance principles of marginal cost, fair pricing, inclusion of emission factors and to support modal shift. This has laid the foundation for a payment will for good technical solutions in the field of road user charging. This licentiate thesis aims at analysing and identifying the most important factors and areas to focus on for reaching a successful implementation of a system for charging heavy goods vehicles based on distance and marginal cost. These are: - Public acceptance and use of opportunities and public opinion - The political process and most important decision points - The legal framework before and during introduction as well as for operation of a system - The institutional framework - Responsibility assignment - System procurement and implementation, including procurement strategy, technology choices, testing and demonstration and interoperability Another important contribution is the implementation process model for an introduction of a distance based system for heavy goods vehicles in a country. The model comprises of the most important aspects to consider which has been made through the study and analysis of both implemented and cancelled systems as well as planned introductions, where the Swedish work in this direction has formed the major part of the analysis. The implementation process model with its main actors, decision points and information flow between actor groups are cornerstones of the model, which can serve as a tool for future implementers to follow.

<p>Several factors have influenced the recent development in charging of heavy goods vehicles in Europe. European legislation is changed and the political will is strong in many countries. The public awareness and acceptance is large for the need to put a price and a cost on the use of infrastructure. Environmental concerns are also actuating change in this direction. Systems are being introduced to support and enhance principles of marginal cost, fair pricing, inclusion of emission factors and to support modal shift. This has laid the foundation for a payment will for good technical solutions in the field of road user charging.</p><p>This licentiate thesis aims at analysing and identifying the most important factors and areas to focus on for reaching a successful implementation of a system for charging heavy goods vehicles based on distance and marginal cost. These are:</p><p>- Public acceptance and use of opportunities and public opinion</p><p>- The political process and most important decision points</p><p>- The legal framework before and during introduction as well as for operation of a system</p><p>- The institutional framework</p><p>- Responsibility assignment</p><p>- System procurement and implementation, including procurement strategy, technology choices, testing and demonstration and interoperability</p><p>Another important contribution is the implementation process model for an introduction of a distance based system for heavy goods vehicles in a country. The model comprises of the most important aspects to consider which has been made through the study and analysis of both implemented and cancelled systems as well as planned introductions, where the Swedish work in this direction has formed the major part of the analysis. The implementation process model with its main actors, decision points and information flow between actor groups are cornerstones of the model, which can serve as a tool for future implementers to follow.</p>

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Thesis advisor: Richard Sweeney / Public transportation is found across almost all major cities and differ widely in structure. Notably, transportation agencies adopt different fare structures to suit the idiosyncratic needs of cities. In the United States, the two most common fare structures are: distance based fares, and flat fares. This study evaluates the fairness of these two structures through the lens of consumer surplus and how it varies across different levels of poverty under each structure. Using ridership and demographic data from Washington D.C.'s "Metro" network, price elasticities of demand across demographic groups are determined and then applied to estimate the results of a hypothetical switch in fare structure. The resulting changes in consumer surplus are then compared between stations with different levels of poverty to determine whether one structure is more regressive than the other. The results of this analysis suggest that flexible fares such as distance based fares are more equitable as they charge higher prices for high-income individuals, who are also more price inelastic. / Thesis (BA) — Boston College, 2020. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

As técnicas de projeção multidimensional tornaram-se uma ferramenta de análise importante. Elas buscam mapear dados de um espaço multidimensional para um espaço visual, de menor dimensão, preservando as estruturas de distância ou de vizinhança no mapa visual produzido. Apesar dos recentes avanços, as técnicas existentes ainda apresentam deficiências que prejudicam a sua utilização como ferramentas exploratórias em certos domínios. Um exemplo está nos cenários streaming, nos quais os dados são produzidos e/ou coletados de forma contínua. Como a maioria das técnicas de projeção necessitam percorrer os dados mais de uma vez para produzir um layout final, e fluxos normalmente não podem ser carregados por completo em memória principal, a aplicação direta ou mesmo a adaptação das técnicas existentes em tais cenários é inviável. Nessa tese de doutorado é apresentado um novo modelo de projeção, chamado de Xtreaming, no qual as instâncias de dados são visitadas apenas uma vez durante o processo de projeção. Esse modelo é capaz de se adaptar a mudanças nos dados conforme eles são recebidos, atualizando o mapa visual para refletir as novas estruturas que surgem ao longo do tempo. Os resultados dos testes mostram que o Xtreaming é muito competitivo em termos de preservação de distâncias e tempo de execução se comparado com técnicas do estado-da-arte. Também é apresentada uma nova técnica de projeção multidimensional, chamada de User-assisted Projection Technique for Distance Information (UPDis), que foi projetada para permitir a intervenção do usuário exigindo apenas informações de distância entre as instâncias, e que é utilizada como parte do Xtreaming. Os resultados também mostram que a UPDis é tão rápida, precisa e flexível quanto as técnicas do estado-da-arte. / Multidimensional Projection techniques have become an important analytics tool. They map data from a multidimensional space into a visual space preserving the distance or neighborhood structures on the produced layout. Despite the recent advances, existing techniques still present drawbacks that impair their use as exploratory tools on certain domains. An example is the streaming scenario, in which data are captured or produced continuously. Since most projection techniques need to traverse the data more than once to produce a final layout, and streaming data typically cannot be completely loaded into the main memory, the direct use or even adaptation of the existing techniques in such scenarios is infeasible. In this dissertation, we present a novel projection model, called Xtreaming, wherein the data instances are visited only once during the projection process. This model is able to adapt itself to the changes in data as data is received, updating the visual layout to reflect the new structures that emerge over time. The tests show that Xtreaming is very competitive regarding distance preservation and running time when compared with state-of-the-art projection techniques. We also present a new multidimensional projection technique, called User-assisted Projection Technique for Distance Information (UPDis), that was designed to allow user intervention requiring only distance information between data instances. UPDis is used as part of the Xtreaming model. The results show that UPDis is as fast, accurate and flexible as state-of-the-art techniques.

<p>The moose (Alces alces) is very important both economically and ecologically, therefore all knowledge of moose is vital for future management of the moose population. Little is known about moose habitat selection in Sweden. In coastal southwestern Sweden growing human population and new infrastructure projects continuously threaten to fragment and isolate local moose populations. The habitat selection of 22 moose, 8 males and 14 females, in southwestern Sweden was studied from February 2002 until December 2005. The moose were captured and fitted with GPS-collars and positions were collected at 2-hour intervals. The number of moose positions totaled 71103 during the study period of 46 months. Data for individual animals were divided into four seasons: spring, summer, fall and winter based on climate and moose biology. A total of 125 moose seasonal home ranges were generated and habitat use within each of the generated home ranges was studied using Euclidean distance-based analysis. A reclassified digital landcover map was divided into the land use classes agriculture, clear-cut, coniferous forest, deciduous forest, mire and mountain. The results showed that there was a difference in habitat selection between males and females. Males were significantly closer to forest and clear-cuts compared to females. Both males and females selected clear-cuts and avoided agriculture within their home ranges.</p> / <p>Älgen är en viktig art, både ekonomiskt och ekologiskt, och all kunskap är viktig för att även i framtiden kunna sköta en sund älgstam. Trots flertalet studier finns det många frågetecken om älgens habitatval i Sverige. En ökad exploateringstakt och nya infrastrukturprojekt hotar att fragmentera och isolera populationer av älg. Habitatvalet hos 22 älgar, 8 tjurar och 14 kor, i sydvästra Sverige studerades mellan februari 2002 och december 2005. Älgarna sövdes och utrustades med GPS-sändare, deras positioner registrerades varannan timma och det totala antalet positioner under den 46 månader långa studietiden var 71103 stycken. Data från varje älg delades in i 4 säsonger; vår, sommar, höst och vinter, baserat på klimat och älgens biologi. Totalt genererades 125 hemområden baserade på säsong, och valet av habitat inom varje hemområde studerades med hjälp av Euclidean distance-based analysis. En omklassificerad digital marktäckedata användes som var indelad i 6 olika klasser; odlad mark, hygge, barrskog, lövskog, myrmark och berg i dagen. Resultaten visade att det var skillnad mellan könen i hur de väljer habitat. Tjurarna var signifikant närmare barrskog och hyggen än korna, men både tjurar och kor selekterade för hyggen och undvek odlad mark inom deras hemområden.</p>