Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present numerous programming challenges, including scalability, load balancing, and efficient memory utilization. In this age of Big Data we face additional challenges since the data is often streaming at a high velocity and we wish to make near real-time decisions for real-world events. For instance, we may wish to track Twitter for the pandemic spread of a virus. Analyzing such data sets requires combing algorithmic optimizations and utilization of massively multithreaded architectures, accelerator such as GPUs, and distributed systems. My research focuses upon designing new analytics and algorithms for the continuous monitoring of dynamic social networks. Achieving high performance computing for irregular algorithms such as Social Network Analysis (SNA) is challenging as the instruction flow is highly data dependent and requires domain expertise. The rapid changes in the underlying network necessitates understanding real-world graph properties such as the small world property, shrinking network diameter, power law distribution of edges, and the rate at which updates occur. These properties, with respect to a given analytic, can help design load-balancing techniques, avoid wasteful (redundant) computations, and create streaming algorithms. In the course of my research I have considered several parallel programming paradigms for a wide range systems of multithreaded platforms: x86, NVIDIA's CUDA, Cray XMT2, SSE-SIMD, and Plurality's HyperCore. These unique programming models require examination of the parallel programming at multiple levels: algorithmic design, cache efficiency, fine-grain parallelism, memory bandwidths, data management, load balancing, scheduling, control flow models and more. This thesis deals with these issues and more.

Database Management Systems (DBMS) are used by software applications, to store, manipulate, and retrieve large sets of data. However, the requirements of current software systems pose various challenges to established DBMS. First, most software systems organize their data by means of objects rather than relations leading to increased maintenance, redundancy, and transformation overhead when persisting objects to relational databases. Second, complex objects are separated into several objects resulting in Object Schizophrenia and hard to persist Distributed State. Last but not least, current software systems have to cope with increased complexity and changes. These challenges have lead to a general paradigm shift in the development of software systems. Unfortunately, classical DBMS will become intractable, if they are not adapted to the new requirements imposed by these software systems. As a result, we propose an extension of DBMS with roles to represent complex objects within a relational database and support the exibility required by current software systems. To achieve this goal, we introduces RSQL, an extension to SQL with the concept of objects playing roles when interacting with other objects. Additionally, we present a formal model for the logical representation of roles in the extended DBMS.

Transportsektorn är en av världens största producenter av växthusgas därav är dess avkarbonisering essentiell för att uppnå Parisavtalets mål för CO2-emissioner. Ett viktigt steg för att uppnå dessa mål utförs genom elektrifiering. Litium-jon-batterier (eng. litium-ion batteries, ’LIB’) har blivit väldigt populära energilagringssystem för batteridrivna elektriska fordon (eng. battery electric vehicles, ’BEV’) men tenderar att åldras, precis som alla andra batterier. Därav krävs forskning kring batteriföråldring på grund av nedbrytningsprocessernas inverkan på prissättningen, prestationerna och miljöpåverkan av BEV. Olika modeller används för att beskriva batteriernas åldrande. Datadrivna modeller som förutspår batteriers livstid ökar i popularitet vars noggrannhet och prestationer till stor del beror på indatats kvalitet. Formatet för tidsinhämtade data kräver enorma mängder lagringsutrymme, hög processkapacitet och längre processer; något ’reducerad’ eller ’aggregerad’ data delvis åtgärdar. Denna avhandling fokuserar på att utveckla en metodik för användning av dynamiska fordonsdata i ’aggregerad’ form. Tidsloggade data inhämtade från kallklimatstesting av Scanias BEV-prototyp användes varav interaktionseffekterna mellan diverse fordonsparametrar samt deras effekt på batteriåldring utifrån en batteriåldringsmodell analyserades. Olika tillvägagångssätt för strukturering av dynamiska fordonsdata i modellen undersöktes också. Tolv aggregeringsscenarion designades och testades. Dessutom valdes tre scenarion för uppskattningar och jämförelser av återstående användbar livslängd (eng. remaining-useful-life, ’RUL’) tillsammans med resultat från tidsinhämtade data. Slutligen drogs slutsatser om: parameterinteraktioner, struktur av dynamiska fordonsdata och RUL. Flera framtida utvecklingsområden har också föreslagits bland annat: tester av andra aggregeringstekniker, utöka modellen till tjänstefordon samt kategorisera användningsbeteenden av fordon för att förbättra RUL-uppskattningar. / The transport sector is one of the world’s largest greenhouse gas producing sector and it’s decarbonisation is imperative to achieve the CO2 emission targets set by the Paris Agreement. One important step towards achieving these targets is through electrification of the sector. Lithium-ion batteries (LIBs) have become very popular energy storage systems for battery electric vehicles (BEVs). However, LIBs like all other batteries, tend to age. Hence, the study of the battery ageing phenomena is very essential since the degradation in battery characteristics hugely determines the cost, performance and the environmental impact of BEVs. Different modelling approaches are used to represent battery ageing behaviour. Data-driven models for predicting the lifetime of batteries are becoming popular. However, the accuracy and performance of data-driven models largely depends upon the quality of data being used as the input. Time-sampled format of logging data results in huge data files requiring enormous amounts of storage space, high processing power requirements and longer processing times. Instead, using data in a ’reduced’ or ‘aggregated’ form can help in addressing these issues. This thesis work focuses on developing a methodology for using dynamic vehicle data in an ‘aggregated’ form. Time-sampled data from a Scania prototype BEV truck, recorded during cold climate test, was used. The interaction effects between various vehicle parameters and their effect on battery ageing in a battery ageing model were analyzed. Different approaches to structuring dynamic vehicle data for use in the model were also studied. Twelve aggregation scenarios were designed and tested. Furthermore, three scenarios were selected for making remaining-useful-life (RUL) estimations and compared alongside time-sampled data results. Finally, conclusions about parameter interactions, structuring of dynamic vehicle data and RUL estimations were drawn. Several next steps for future work have also been suggested such as testing other aggregation techniques, extending the model to vehicle fleets and categorizing vehicle usage behaviours to make better RUL estimations.

Durant les 20 dernières années, la recherche en visualisation d’informations (InfoVis) a permis l’émergence de nouvelles techniques et méthodes qui permettent d’assister l’analyse de données intensives pour la science, l’industrie, et les gouvernements. Cependant, la plupart de ces travaux de recherches furent orientés sur des données statiques pour des utilisateurs experts.Dernièrement, des évolutions technologique et sociétales ont eu pour effet de rendre les données de plus en plus dynamiques et accessibles pour une population plus diverse. Par exemple des flux de données tels que les emails, les mises à jours de statuts sur les réseaux sociaux, les flux RSS, les systèmes de
gestion de versions, et bien d’autres. Ces nouveaux types de données sont utilisés par une population qui n’est pas forcément entraînée ou éduquée à utiliser des visualisations de données. La plupart de ces personnes sont des utilisateurs occasionnels, d’autres utilisent très souvent ces données dans leurs travaux. Dans les deux cas, il est probable que ces personnes n’aient pas reçu de formation formelle en visualisation de données.Ces changements technologiques et sociétaux ont généré une multitude de nouveaux défis, car la plupart des techniques de visualisations sont conçues pour des experts et des bases de données statiques. Peu d’études ont été conduites pour explorer ces défis. Dans ce rapport de thèse, j’adresse la question suivante : « Peut-­on permettre à des utilisateurs non­-experts de créer leur propre visualisation et de contribuer à l’analyse de flux de données ? »La première étape pour répondre à cette question est d’évaluer si des personnes non formées à la visualisation d’informations ou aux « data sciences » peuvent effectuer des tâches d’analyse de données dynamiques utiles, en utilisant un système de visualisation adapté pour supporter cette tâche. Dans la première partie de cette dissertation, je présente différents scénarios et systèmes, qui permettent à des utilisateurs non­-experts (de 20 à 300 ou 2000 à 700 000 personnes) d’utiliser la visualisation d’informations pour analyser des données dynamiques.Un autre problème important est le manque de principes génériques de design pour l’encodage visuel de visualisations d’informations dynamiques. Dans cette dissertation, je conçois, définis, et explore un espace de design pour représenter des donnés dynamiques pour des utilisateurs non­-experts. Cette espace de design est structuré par des jetons graphiques représentant des éléments de données qui permettent de construire dans le temps différentes visualisations, tant classiques que nouvelles.Dans cette thèse, je propose un nouveau paradigme de conception (design) pour faciliter la réalisation de visualisation d’informations par les utilisateurs non­-experts. Ce paradigme est inspiré par des théories établies en psychologie du développement, tout autant que par des pratiques passées et présentes de création de visualisation à partir d’objets tangibles. Je décris tout d’abord les composants et processus de bases qui structurent ce paradigme. Ensuite, j’utiliserai cette description pour étudier *si et comment* des utilisateur non­-experts sont capables de créer, discuter, et mettre à jour leurs propres visualisations. Cette étude nous permettra de réviser notre modèle précédent et de fournir une première exploration des phénomènes relatifs à la création d’encodages visuels par des utilisateurs non­-experts sans logiciel. En résumé, cette thèse contribue à la compréhension des visualisations dynamiques pour des utilisateurs non­-experts. / During the past two decades, information visualisation (InfoVis) research has created new techniques and methods to support data- intensive analyses in science, industry and government. These have enabled a wide range of analyses tasks to be executed, with tasks varying in terms of the type and volume of data involved. However, the majority of this research has focused on static datasets, and the analysis and visualisation tasks tend to be carried out by trained expert users. In more recent years, social changes and technological advances have meant that data have become more and more dynamic, and are consumed by a wider audience. Examples of such dynamic data streams include e-mails, status updates, RSS 1 feeds, versioning systems, social networks and others. These new types of data are used by populations that are not specifically trained in information visualization. Some of these people might consist of casual users, while others might consist of people deeply involved with the data, but in both cases, they would not have received formal training in information visualization. For simplicity, throughout this dissertation, I refer to the people (casual users, novices, data experts) who have not been trained in information visualisation as non-experts.These social and technological changes have given rise to multiple challenges because most existing visualisation models and techniques are intended for experts, and assume static datasets. Few studies have been conducted that explore these challenges. In this dissertation, with my collaborators, I address the question: Can we empower non-experts in their use of visualisation by enabling them to contribute to data stream analysis as well as to create their own visualizations?The first step to answering this question is to determine whether people who are not trained in information visualisation and the data sciences can conduct useful dynamic analysis tasks using a visualisation system that is adapted to support their tasks. In the first part of this dissertation I focus on several scenarios and systems where different sized crowds of InfoVis non-experts users (20 to 300 and 2 000 to 700 000 people) use dynamic information visualisation to analyse dynamic data.Another important issue is the lack of generic design principles for the visual encoding of dynamic visualization. In this dissertation I design, define and explore a design space to represent dynamic data for non-experts. This design space is structured by visual tokens representing data items that provide the constructive material for the assembly over time of different visualizations, from classic represen- tations to new ones. To date, research on visual encoding has been focused on static datasets for specific tasks, leaving generic dynamic approaches unexplored and unexploited.In this thesis, I propose construction as a design paradigm for non-experts to author simple and dynamic visualizations. This paradigm is inspired by well-established developmental psychological theory as well as past and existing practices of visualisation authoring with tangible elements. I describe the simple conceptual components and processes underlying this paradigm, making it easier for the human computer interaction community to study and support this process for a wide range of visualizations. Finally, I use this paradigm and tangible tokens to study if and how non-experts are able to create, discuss and update their own visualizations. This study allows us to refine our previous model and provide a first exploration into how non-experts perform a visual mapping without software. In summary, this thesis contributes to the understanding of dynamic visualisation for non-expert users.

The main objective of this work is to design and implement an application that can be used as an aid for the education of programming essentials. Particularly, the attention focuses on the domain of dynamic data structures. The target application will be implemented with the use of web technologies so that it can be run in an ordinary WWW browser. First of all, a brief introduction recapitulates the data structures to be covered. Then the work summarizes the usable technologies available within the web browsers with the focus on the particular technology (which is DHTML) that will become the target platform. The most significant part of this work then discusses the design of the final application. This rather theoretical part is then followed by the description of the practical implementation. A short user manual is also included.

A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimilation is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, pruning redundant data, and identifying the most beneficial additional observations. These questions originate in operational data assimilation practice, and have started to attract considerable interest in the recent past. This dissertation advances the state of knowledge in four dimensional variational (4D-Var) - data assimilation by developing, implementing, and validating a novel computational framework for estimating observation impact and for optimizing sensor networks. The framework builds on the powerful methodologies of second-order adjoint modeling and the 4D-Var sensitivity equations. Efficient computational approaches for quantifying the observation impact include matrix free linear algebra algorithms and low-rank approximations of the sensitivities to observations. The sensor network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this "optimization-constrained" optimization problem are provided. The results of this work can be directly applied to the deployment of intelligent sensors and adaptive observations, as well as to reducing the operating costs of measuring networks, while preserving their ability to capture the essential features of the system under consideration. / Ph. D.

The present research paper deals with the comparison of static and dynamic data structures: static array, dynamic array, pointers array - class TList (Delphi) and dynamic doubly linked list, created with the help of recursive record.To compare the above mentioned structures, sorting (Bubble) and convex hull creation algorithms (Graham, Endrew) are realized, with the time of their implementation analysed. The algorithm of sorting (Bubble) is realized by four ways: static array, dynamic array, pointers array (class TList) and a dynamic doubly linked list, created with the help of recursive record.The algorithms of convex hull creation (Graham, Endrew) is realized by three ways: static array, dynamic array and pointers array (TList).The research paper also describes the pointers array class TList (Delphi), its properties and methods. The sorting method Sort of this class is compared with the sorting method of a “Bubble”. Using class templates, a universal class MList (C++) was created for work with dynamic linear linked lists.

Cieľom tejto práce je návrh analýzy tvaru haldy vhodnej pre potreby analyzátora 2LS. 2LS je nástroj pre analýzu C programov založený na automatickom odvodzovaní invariantov s použitím SMT solvera. Navrhované riešenie obsahuje spôsob reprezentácie tvaru programovej haldy pomocou logických formulí nad teóriou bitových vektorov. Tie sú následne využité v SMT solveri pre predikátovú logiku prvého rádu na odvodenie invariantov cyklov a súhrnov jednotlivých funkcií analyzovaného programu. Náš prístup je založený na ukazateľových prístupových cestách, ktoré vyjadrujú dosiahnuteľnosť objektov na halde z ukazateľových premenných. Informácie získané z analýzy môžu byť využité na dokázanie rôznych vlastností programu súvisiacich s prácou s dynamickýcmi dátovými štruktúrami. Riešenie bolo implementované v rámci nástroja 2LS. S jeho použitím došlo k výraznému zlepšeniu schopnosti 2LS analyzovať programy pracujúce s ukazateľmi a dynamickými dátovými štruktúrami. Toto je demonštrované na sade experimentov prevzatých zo známej medzinárodnej súťaže vo verifikácii programov SV-COMP a iných experimentoch.