Ray tracing is used in computer graphics to generate images. The process of rendering images using ray tracing includes testing large counts of rays for intersection against geometry. Testing for ray-geometry intersection is more formally known as the Ray Shooting Problem (RSP) and has broad applications across multiple communities. Hierarchical acceleration structures are frequently employed to index geometry and increase processing speed. Such hierarchical structures make it almost impossible for central processing units to predict memory access and branching patterns. This project focuses on the Bounding Volume Hierarchy (BVH) structure and improving its performance when querying large batches of first hit ray-geometry intersections. The core contribution is an Interleaved Prefetching Ray Traversal (IPRT) algorithm that addresses memory and branching issues. Five standardized test scenarios with varying geometric complexity provide evaluation data. The experimental evaluation suggests that for incoherent rays, IPRT achieves 6:1 - 41:8% faster performance compared to Stackless Traversal. However, for fully coherent rays, performance is 68:8 - 149:1% slower. These results suggest that for select ray tracing workloads that elicit low coherence, the IPRT algorithm is likely to outperform Stackless Traversal. A microarchitectural analysis affirms previous research; memory accesses and branching behavior are critical for performance. Surprisingly, addressing each component in isolation yields no significant performance improvement. It is paramount to address the two simultaneously, as the IPRT algorithm does successfully. / Ray tracing används inom datorgrafik för att generera bilder. Att rendera bilder med ray tracing kräver att datorer simulerar stora mängder ljusstrålar i en virtuell miljö. Problemet att beräkna om en stråle träffar geometri är mer formellt känt som Ray Shooting Problem (RSP) och har breda applikationer inom flera områden. Hierarkiska accelerationsstrukturer används ofta för att indexera geometri och öka beräkningshastighet. Sådana hierarkiska strukturer gör det nästan omöjligt för centrala processorenheter att förutsäga minnesåtkomst och förgreningsmönster. Detta projekt fokuserar på Bounding Volume Hierarchy (BVH) strukturen och dess prestanda. En ny algoritm tas fram för att behandla dessa identifierade problem: Interleaved Prefetching Ray Traversal (IPRT). Fem standardiserade testscener med varierande geometrisk komplexitet används för experimentell utvärdering. Den experimentella utvärderingen antyder i jämförelse med Stackless Traversal så uppnår IPRT-algoritmen 6:1 - 41:8% bättre prestanda för osammanhängande strålar. När det gäller helt sammanhängande strålar är prestandan dock 68:8 - 149:1% långsammare. En mikroarkitektisk analys bekräftar tidigare forskning; minnesåtkomst och förgreningsbeteende är mycket viktigt för prestanda. Isolerad optimering av faktorerna ger dessvärre ingen signifikant prestandaförbättring. Det är därför ytterst viktigt att optimera båda komponenter samtidigt, vilket IPRT-algoritmen lyckas med.

Over the years, data management has diversified and moved into multiple directions, mainly caused by a significant growth in the application space with different usage patterns, a massive change in the underlying hardware characteristics, and-last but not least-growing data volumes to be processed. A solution matching these constraints has to cope with a multidimensional problem space including techniques dealing with a large number of domain-specific data types, data and consistency models, deployment scenarios, and processing, storage, and communication infrastructures on a hardware level. Specialized database engines are available and are positioned in the market optimizing a particular dimension on the one hand while relaxing other aspects (e.g. web-scale deployment with relaxed consistency). Today it is common sense, that there is no single engine which can handle all the different dimensions equally well and therefore we have very good reasons to tackle this problem and optimize the dimensions with specialized approaches in a first step. However, we argue for a second step (reflecting in our opinion on the even harder problem) of a deep integration of individual engines into a single coherent and consistent data management ecosystem providing not only shared components but also a common understanding of the overall business semantics. More specifically, a data management ecosystem provides common “infrastructure” for software and data life cycle management, backup/recovery, replication and high availability, accounting and monitoring, and many other operational topics, where administrators and users expect a harmonized experience. More importantly from an application perspective however, customer experience teaches us to provide a consistent business view across all different components and the ability to seamlessly combine different capabilities. For example, within recent customer-based Internet of Things scenarios, a huge potential exists in combining graph-processing functionality with temporal and geospatial information and keywords extracted from high-throughput twitter streams. Using SAP HANA as the running example, we want to demonstrate what moving a set of individual engines and infra-structural components towards a holistic but also flexible data management ecosystem could look like. Although there are some solutions for some problems already visible on the horizon, we encourage the database research community in general to focus more on the Big Picture providing a holistic/integrated approach to efficiently deal with different types of data, with different access methods, and different consistency requirements-research in this field would push the envelope far beyond the traditional notion of data management.

Relational database management systems build on the closed world assumption requiring upfront modeling of a usually stable schema. However, a growing number of today's database applications are characterized by self-descriptive data. The schema of self-descriptive data is very dynamic and prone to frequent changes; a situation which is always troublesome to handle in relational systems. This demo presents the relational database management system FRDM. With flexible relational tables FRDM greatly simplifies the management of self-descriptive data in a relational database system. Self-descriptive data can reside directly next to traditionally modeled data and both can be queried together using SQL. This demo presents the various features of FRDM and provides first-hand experience of the newly gained freedom in relational database systems.

The large volume of freely available graph data sets impedes the users in analyzing them. For this purpose, they usually pose plenty of pattern matching queries and study their answers. Without deep knowledge about the data graph, users can create ‘failing’ queries, which deliver empty answers. Analyzing the causes of these empty answers is a time-consuming and complicated task especially for graph queries. To help users in debugging these ‘failing’ queries, there are two common approaches: one is focusing on discovering missing subgraphs of a data graph, the other one tries to rewrite the queries such that they deliver some results. In this demonstration, we will combine both approaches and give the users an opportunity to discover why empty results were delivered by the requested queries. Therefore, we propose DebEAQ, a debugging tool for pattern matching queries, which allows to compare both approaches and also provides functionality to debug queries manually.

Yes / Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness.

La réduction de la mortalité maternelle et infantile est universellement acceptée comme un objectif du millénaire pour le développement. L'aide étrangère est un des moyens utilisés pour l'atteindre. Cependant, malgré les succès, à l'échelle microéconomique, de certains programmes de santé financés par les aides étrangères, l'efficacité globale de ces aides demeure inconnue. Plusieurs travaux ont traité de l'efficacité de l'aides sur la croissance économique, mais peu d'entre eux concernent le secteur de la santé. Le but de cette thèse, est précisément d'évaluer l'efficacité des aides étrangères sur des indicateurs de santé à l'échelle macroéconomique. On va essayer d'explorer l'impact des aides étrangères octroyées par des bailleurs privés et publics sur l'état de santé telle que la mortalité infantile, maternelle et des adultes dans les pays en développement. La thèse examine l'affectation des aides étrangères au secteur de la santé, à travers trois documents de travail à soumettre à publication. / The reduction of child and maternal mortality is universally accepted as a millennium development goal (MDG). Foreign aid for health is one of the means implemented to reach it. However, even if many successes of health aid activities have been underlined at the microeconomic level, the effectiveness of health aid in general remains unknown. In spite of many macroeconomic works on aid effectiveness on economic growth, only little deals with its effectiveness in health. The purpose of this thesis is precisely to assess the effectiveness of foreign aid in improving health measurements, at the macroeconomic level. I tried to explore the impact of health aid disbursed by the donors through the government and private sector on health outcomes like child, maternal and adult mortality rates in developing economies. The thesis examines the issue of foreign aid earmarked for health sector using a three-paper format. The three chapters of this thesis can be read independently.

Les systèmes d'information mobiles et ambiants liés à la localisation et à la navigation évoluent progressivement vers des environnements à petite échelle. La nouvelle frontière scientifique et technologique concerne les applications qui assistent les utilisateurs dans leurs déplacements et activités au sein d’espaces bâtis dits «indoor» (e.g., aéroports, musées, bâtiments). La recherche présentée par cette thèse développe une représentation de données spatiales d'un environnement «indoor» qui tient compte des dimensions contextuelles centrées sur l'utilisateur et aborde les enjeux de gestion de données mobiles. Un modèle de données «indoor» hiérarchique et sensible au contexte est proposé. Ce modèle intègre différentes dimensions du contexte en plus de la localisation des entités concernées, telles que le temps et les profils des utilisateurs. Ce modèle est basé sur une structure arborescente dans laquelle l'information spatiale est représentée à différents niveaux d'abstraction. Cette conception hiérarchique favorise un traitement adaptatif et efficace des Requêtes Dépendantes de la Localisation (RDL) qui sont considérées comme des éléments clés pour le développement des différentes catégories de services de géolocalisation sensibles au contexte. Un langage de requêtes continues est développé et illustré par des exemples de requêtes RDL. Ce langage exploite le concept des granules spatiales, et permet de représenter les requêtes continues et dépendantes de la localisation en prenant compte des préférences de l'utilisateur au moment de l'exécution.Cette approche de modélisation est complétée par le développement d'une architecture générique pour le traitement continu des requêtes RDL et par la conception et la mise en oeuvre de plusieurs algorithmes qui permettent un traitement efficace des requêtes dépendantes de la localisation sur des objets mobiles en «indoor». Plusieurs algorithmes de traitement continu des requêtes de recherche de chemin hiérarchique et des requêtes de zone appliquées à des objets statiques et/ou en mouvement sont présentés. Ces algorithmes utilisent une approche hiérarchique et incrémentale afin d'exécuter efficacement les requêtes RDL continues. Un prototype encapsulant le modèle de données hiérarchique, les opérateurs et les contraintes introduits dans le langage de requête ainsi que les différents algorithmes et méthodes pour la manipulation de requêtes RDL a été développé comme une extension du SGBD Open Source PostgreSQL. Une étude expérimentale des solutions développées a été menée pour étudier la performance et le passage à l'échelle à l'égard des propriétés intrinsèques des solutions proposées. / The range of applications in ambient information systems progressively evolves from large to small scale environments. This is particularly the case for applications that assist humans in navigation-related activities in indoor spaces (e.g., airports, museums, office buildings). The research presented by this Ph.D. dissertation develops a data and knowledge representation of an indoor environment that takes into account user-centred contextual dimensions and mobile data management issues. We introduce a hierarchical, context-dependent, and feature-based indoor spatial data model. This model takes into account additional contextual dimensions such as time, user profiles, and real-time events. The model is based on a tree structure in which location information is represented at different levels of abstraction. The hierarchical design favours performance and scalability of location-dependent query processing. A query grammar is developed and implemented on top of that model. This query language supports continuous location-dependent queries and takes into account user preferences at execution time. The concept of location granules is introduced at the query execution and presentation levels.This modelling approach is complemented by the development of a generic architecture for continuous query processing. Several algorithms for location dependent query processing over indoor moving objects have been designed and implemented. These algorithms include path searches and range queries applied to both static and moving objects. They are based on an incremental approach in order to execute continuous location-dependent queries.The operators and constraints introduced in the query language and the algorithms for location-dependent query processing have been implemented as a database extension of the open source DBMS PostgreSQL, and where the hierarchical network-based indoor data model has been developed at the logical level. Several experiments have been conducted to evaluate the scalability and performance of the whole framework.

This thesis uses innovative approaches to analyse energy policy interventions aimed at enhancing the environmental sustainability of energy use as well as its consequential welfare implications. First, we examine the relationship between energy efficiency improvement and CO2 emissions at the macro level. We use the Index Decomposition Analysis to derive energy efficiency by separating out the impact of shifts in economic activity on energy intensity. We then employ econometric models to relate energy efficiency and CO2 emissions accounting for non-economic factors such as consumers lifestyle and attitudes. The applications for 13 OPEC and 30 OECD countries show that at the country-group and individual country level, increase in energy intensity for OPEC is associated with both deteriorations in energy efficiency and shifts towards energy-intensive activities. The model results suggest that the reduction in energy efficiency in general go in tandem with substantial increases in CO2 emissions. The decline in energy intensity for OECD can be attributed mainly to improvements in energy efficiency which is found to compensate for the impact on CO2 emissions of income changes. The results confirm the empirical relevance of energy efficiency improvements for the mitigation of CO2 emissions. The method developed in this chapter further enables the separate assessment of non-economic behavioural factors which according to the results exert a non-trivial influence on CO2 emissions. Secondly, having empirically confirmed the relationship between energy efficiency improvements and CO2 emission at the macro level in Chapter 2, we investigate potential underlying drivers of energy efficiency improvements taking into account potential asymmetric effects of energy price change in Chapter 3. This is crucial for designing effective and efficient policy measures that can promote energy efficiency. In addition to the Index Decomposition Analysis used to estimate the economy-wide energy efficiency in Chapter 2, we also use Stochastic Frontier Analysis and Data Envelop Analysis as alternative methods. The driving factors are examined using static and dynamic panel model methods that account for both observed and unobserved country heterogeneity. The application for 32 OECD countries shows that none of the three methods leads to correspondence in term of ranking between energy efficiency estimates and energy intensity at the country level corroborating the criticism that energy intensity is a poor proxy for energy efficiency. The panel-data regression results using the results of the three methods show similarities in the impacts of the determinants on the energy efficiency levels. Also, we find insignificant evidence of asymmetric effects of total energy price but there is proof of asymmetry using energy specific prices. Thirdly, in Chapter 4 we offer an improved understanding of the impacts to expect of abolishing fuel price subsidy on fuel consumption, and also of the welfare and distributional impacts at the household level. We develop a two-step approach for this purpose. Key aspect of the first step is a two-stage budgeting model to estimate various fuel types elasticities using micro-data. Relying on these estimates and the information on households expenditure shares for different commodities, the second step estimates the welfare (direct and indirect) and distributional impacts. The application for Nigeria emphasises the relevance of this approach. We find heterogeneous elasticities of fuel demand among household groups. The distributional impact of abolishing the kerosene subsidy shows a regressive welfare loss. Although we find a progressive loss for petrol, the loss gap between the low- and high-income groups is small relative to the loss gap from stopping kerosene subsidy, making the low-income groups to suffer a higher total welfare loss. Finally, from the highlighted results, we draw the following concluding remarks in chapter 5. Energy efficiency appears a key option to mitigate CO2 emissions but there is also a need for additional policies aiming for behavioural change; energy specific prices and allowing for asymmetry in analysing the changes in energy efficiency is more appropriate and informative in formulating reliable energy policies; the hypothesis that only the rich would be worse-off from fuel subsidy removal is rejected and the results further suggest that timing of the fuel subsidy removal would be crucial as a higher international oil price will lead to higher deregulated fuel price and consequently, larger welfare loss.

RDF et SPARQL se sont imposés comme modèle de données et langage de requêtes standard pour décrire et interroger les données sur la Toile. D’importantes quantités de données RDF sont désormais disponibles, sous forme de jeux de données ou de méta-données pour des documents semi-structurés, en particulier XML. La coexistence et l’interdépendance grandissantes entre RDF et XML rendent de plus en plus pressant le besoin de représenter et interroger ces données conjointement. Bien que de nombreux travaux couvrent la production et la publication, manuelles ou automatiques, d’annotations pour données semi-structurées, peu de recherches ont été consacrées à l’exploitation de telles données. Cette thèse pose les bases de la gestion de données hybrides XML-RDF. Nous présentons XR, un modèle de données accommodant l’aspect structurel d’XML et la sémantique de RDF. Le modèle est suffisamment général pour représenter des données indépendantes ou interconnectées, pour lesquelles chaque nœud XML est potentiellement une ressource RDF. Nous introduisons le langage XRQ, qui combine les principales caractéristiques des langages XQuery et SPARQL. Le langage permet d’interroger la structure des documents ainsi que la sémantique de leurs annotations, mais aussi de produire des données semi-structurées annotées. Nous introduisons le problème de composition de requêtes dans le langage XRQ et étudions de manière exhaustive les techniques d’évaluation de requêtes possibles. Nous avons développé la plateforme XRP, implantant les algorithmes d’évaluation de requêtes dont nous comparons les performances expérimentalement. Nous présentons une application reposant sur cette plateforme pour l’annotation automatique et manuelle de pages trouvées sur la Toile. Enfin, nous présentons une technique pour l’inférence RDFS dans les systèmes de gestion de données RDF (et par extension XR). / Since the beginning of the Semantic Web, RDF and SPARQL have become the standard data model and query language to describe resources on the Web. Large amounts of RDF data are now available either as stand-alone datasets or as metadata over semi-structured documents, typically XML. The ability to apply RDF annotations over XML data emphasizes the need to represent and query data and metadata simultaneously. While significant efforts have been invested into producing and publishing annotations manually or automatically, little attention has been devoted to exploiting such data. This thesis aims at setting database foundations for the management of hybrid XML-RDF data. We present a data model capturing the structural aspects of XML data and the semantics of RDF. Our model is general enough to describe pure XML or RDF datasets, as well as RDF-annotated XML data, where any XML node can act as a resource. We also introduce the XRQ query language that combines features of both XQuery and SPARQL. XRQ not only allows querying the structure of documents and the semantics of their annotations, but also producing annotated semi-structured data on-the-fly. We introduce the problem of query composition in XRQ, and exhaustively study query evaluation techniques for XR data to demonstrate the feasibility of this data management setting. We have developed an XR platform on top of well-known data management systems for XML and RDF. The platform features several query processing algorithms, whose performance is experimentally compared. We present an application built on top of the XR platform. The application provides manual and automatic annotation tools, and an interface to query annotated Web page and publicly available XML and RDF datasets concurrently. As a generalization of RDF and SPARQL, XR and XRQ enables RDFS-type of query answering. In this respect, we present a technique to support RDFS-entailments in RDF (and by extension XR) data management systems.

近年來，全球氣候變遷與環境惡化顯示目前人類活動與永續發展的衝突，如何減少能源消耗以降低溫室氣體排放成為當務之急。在都市規劃方面，以緊密都市理念探討都市發展與交通能源消耗最受到關注，然而都市發展與產業活動、家戶行為密切相關，因此若僅從交通方面分析能源消耗，恐忽略其他影響能源消耗的重要因素。故本研究著重於探討都市發展特性與能源消耗的關聯性，將產業活動與家戶行為納入模型分析，可更全面性了解都市與能源消耗的關聯性。此外，基於「全球思考、地方行動」的考量，如何落實節能減碳的目標，必須就各縣市的都市發展特性著手，透過追蹤資料模型，進一步了解各縣市的固定效果對能源消耗之影響以及能源消耗之時間趨勢。 透過對都市耗能部門進行分類，並以台灣改制後19個縣市為實證範圍，可以確認都市發展特性的幾個面向：土地使用密度、土地混合使用、交通屬性、產業屬性、家戶屬性、環境屬性與交通運輸、產業發展、家戶活動的耗能關係。實證結果發現，緊密都市有助於節能目標達成；道路增加可及性，但亦助長汽車使用，增加交通耗能；為提升大眾運輸使用率，需加強轉乘便利性以及改變私人交通運輸偏好；產業耗能與出口貿易關聯性高，不利能源減量；家戶居住行為與生活型態對能源消耗有正向影響；公園綠地能調節都市氣溫，減少耗能。 綜上所述，欲形塑一個節能減碳的都市必須透過多元途徑，在土地使用方面須維持適當的發展密度與混合程度；交通方面須加強大眾運輸便利性以提高民眾使用意願並抑制汽車使用；產業方面須透過政府獎勵節能措施及促進產業轉型以提升能源效率；家戶方面應透過教育及政策宣導以培養節能生活習慣，方能達成節能減碳的目標。 / In recent years, global climate change and environmental deterioration show a conflict between human activities and sustainable development. How to reduce energy consumption in order to mitigate greenhouse gas emission has become a top priority. According to the concept of compact city, urban planning is seen as the effective way to reduce transportation energy consumption. However, urban development is associated with industry development and household activities, so it would be improper to focus only on transportation sector. Thus the main motivation for this study is trying to illustrate urban development characteristics by combing transportation sector, industry sector and household sector, in order to understand the influence the urban development characteristics on energy consumption more comprehensively. In addition, on the concept of “global thinking, local action”, how to successfully implement energy saving policies should first understand the urban development characteristics of all counties. The purpose of this study is to empirically explore the influences of urban development characteristics on energy consumption by using panel data models, which uses the reorganization of nineteen counties areas in Taiwan as samples.In order to find out the fixed effect of all counties on energy consumption and trend of energy consumption. The empirical results show that the concepts of compact city still contributes the energy-saving goal；construction of roads increase accessibility, but also encourage car use, which increase energy consumption；to encourage the use of public transportation need to improve the convenience of transfer and change the preference of people；energy consumption in industry is highly associated to international trade, so it would be difficult for energy reduction；the trend of energy consumption has increased due to household lifestyle change；the green resources provides by park, which can adjust the temperature of city and reduce energy consumption. In sum, achieving the energy-saving city need diversified approaches, it can’t just keep increasing the density or land mixed-use. Traffic should be strengthened by improving transfer system. In order to increase the willingness to use public transport system and decrease car dependency；industry must trying to improve energy efficiency；households should cultivate the habit of saving energy by education in order to be a true energy-saving city.