A evolução de serviços requer estratégicas para lidar adequadamente com a gerência de versões resultantes das alterações ocorridas durante o ciclo de vida do serviço. Normalmente, uma versão de serviço é exposta como um documento que descreve a funcionalidade do serviço, orientando desenvolvedores clientes sobre os detalhes de acesso ao serviço. No entanto, não existe um padrão para o tratamento de versões dos documentos que descrevem o serviço. Isso implica na dificuldade de identificação e localização de alterações, bem como na medição do seu impacto, especialmente em uma perspectiva mais granular. A compatibilidade aborda um estilo mais elegante de evolução de serviços, considerando os efeitos provenientes das alterações nas aplicações cliente. Ela define um conjunto de alterações permissivas, as quais não afetem a integração externa com o serviço. Entretanto, provedores não conseguem garantir que as alterações necessárias ao serviço estarão no conjunto de alterações compatíveis. Além disso, o conceito de compatibilidade é muitas vezes aplicado sobre a descrição do serviço como um todo, o que pode não ser representativo do uso real do serviço por uma aplicação cliente em particular. Assim, é de responsabilidade dos desenvolvedores clientes avaliar a extensão das alterações no serviço a fim de medir o impacto no seu cenário em particular. Esse processo pode ser difícil e propenso a erros sem o uso de mecanismos de identificação de mudanças. Este trabalho aborda a evolução do serviço de maneira mais granular, o que chamamos de nível de feature. Desse modo, nós propomos um modelo de controle de versões e um algoritmo de compatibilidade a nível de feature, que permite a identificação e qualificação do impacto das alterações, assim como a avaliação da compatibilidade das mudanças neste nível de feature. Este trabalho também apresenta um experimento com base em um serviço real, que explora o modelo de controle de versões para avaliar a extensão das mudanças implícitas e explícitas e sua avaliação de compatibilidade. / Service evolution requires sound strategies to appropriately manage versions resulting from changes during service lifecycle. Typically, a service version is exposed as a description document that describes the service functionality, guiding client developers on the details for accessing the service. However, there is no standard for handling the versioning of service descriptions, which implies on difficulties on identifying and tracing changes as well as measuring their impact, particularly in a finer grain perspective. Compatibility addresses the graceful evolution of services by considering the effects of changes on client applications. It defines a set of permissible change cases that do not disrupt the service external integration. However, providers cannot always guarantee that the necessary changes yield compatible service descriptions. Moreover, the concept of compatibility is often applied to the entire service description, which can not be representative of the actual use of the service by a particular client application. So, it is the client’s developers responsibility to assess the extent of the change and their impact in their particular usage scenario, which can be hard and error-prone without proper change identification mechanisms. This work addresses service evolution in a finer grain manner, which we refer to as feature level. Hence, we propose a versioning model and a compatibility algorithm at feature level, which allows the identification and qualification of changes impact points, their ripple effect, as well as the assessment of changes’ compatibility in this finer grain of features. This work also reports an experiment based on a real service, which explores the versioning model to assess the scope of implicit and explicit changes and their compatibility assessment.

A evolução de serviços requer estratégicas para lidar adequadamente com a gerência de versões resultantes das alterações ocorridas durante o ciclo de vida do serviço. Normalmente, uma versão de serviço é exposta como um documento que descreve a funcionalidade do serviço, orientando desenvolvedores clientes sobre os detalhes de acesso ao serviço. No entanto, não existe um padrão para o tratamento de versões dos documentos que descrevem o serviço. Isso implica na dificuldade de identificação e localização de alterações, bem como na medição do seu impacto, especialmente em uma perspectiva mais granular. A compatibilidade aborda um estilo mais elegante de evolução de serviços, considerando os efeitos provenientes das alterações nas aplicações cliente. Ela define um conjunto de alterações permissivas, as quais não afetem a integração externa com o serviço. Entretanto, provedores não conseguem garantir que as alterações necessárias ao serviço estarão no conjunto de alterações compatíveis. Além disso, o conceito de compatibilidade é muitas vezes aplicado sobre a descrição do serviço como um todo, o que pode não ser representativo do uso real do serviço por uma aplicação cliente em particular. Assim, é de responsabilidade dos desenvolvedores clientes avaliar a extensão das alterações no serviço a fim de medir o impacto no seu cenário em particular. Esse processo pode ser difícil e propenso a erros sem o uso de mecanismos de identificação de mudanças. Este trabalho aborda a evolução do serviço de maneira mais granular, o que chamamos de nível de feature. Desse modo, nós propomos um modelo de controle de versões e um algoritmo de compatibilidade a nível de feature, que permite a identificação e qualificação do impacto das alterações, assim como a avaliação da compatibilidade das mudanças neste nível de feature. Este trabalho também apresenta um experimento com base em um serviço real, que explora o modelo de controle de versões para avaliar a extensão das mudanças implícitas e explícitas e sua avaliação de compatibilidade. / Service evolution requires sound strategies to appropriately manage versions resulting from changes during service lifecycle. Typically, a service version is exposed as a description document that describes the service functionality, guiding client developers on the details for accessing the service. However, there is no standard for handling the versioning of service descriptions, which implies on difficulties on identifying and tracing changes as well as measuring their impact, particularly in a finer grain perspective. Compatibility addresses the graceful evolution of services by considering the effects of changes on client applications. It defines a set of permissible change cases that do not disrupt the service external integration. However, providers cannot always guarantee that the necessary changes yield compatible service descriptions. Moreover, the concept of compatibility is often applied to the entire service description, which can not be representative of the actual use of the service by a particular client application. So, it is the client’s developers responsibility to assess the extent of the change and their impact in their particular usage scenario, which can be hard and error-prone without proper change identification mechanisms. This work addresses service evolution in a finer grain manner, which we refer to as feature level. Hence, we propose a versioning model and a compatibility algorithm at feature level, which allows the identification and qualification of changes impact points, their ripple effect, as well as the assessment of changes’ compatibility in this finer grain of features. This work also reports an experiment based on a real service, which explores the versioning model to assess the scope of implicit and explicit changes and their compatibility assessment.

This diploma thesis deals with the analysis of the current state of company. Subsequently, based on the obtained data, it designs and implements a software development management system. The diploma thesis describes the theoretical basis of the work, the requirements of the company. When choosing a system, it takes into account the individual needs of the selected company. Part of the diploma thesis is a description of the implementation and deployment of the system in the company together with the time analysis.

The thesis analyzes version control system Subversion and its available backends for storing data in a repository. It compares these backends and describes basic features of a new SQL database backend. Design and implementation of the new backend, based on MySQL database, is described and the new backend is then compared with existing backends BDB and FSFS.

Coherent and consistent tracking of provenance data and in particular update history information is a crucial building block for any serious information system architecture. Version Control Systems can be a part of such an architecture enabling users to query and manipulate versioning information as well as content revisions. In this paper, we introduce an RDF versioning approach as a foundation for a full featured RDF Version Control System. We argue that such a system needs support for all concepts of the RDF specification including support for RDF datasets and blank nodes. Furthermore, we placed special emphasis on the protection against unperceived history manipulation by hashing the resulting patches. In addition to the conceptual analysis and an RDF vocabulary for representing versioning information, we present a mature implementation which captures versioning information for changes to arbitrary RDF datasets.

Background: Versioning is a product differentiation strategy where a product or service are distinguished or diversified in the intention of making it more attractive to a specific target group. It is often used to price discriminate and a way to profit maximize. Previous studies have mostly concerned software products in general. But with cloud based software products, specific conditions when versioning might occur. E-learning system are one specific software product that shifts more and more to the cloud and a specific market to study. Purpose: Differentiating a software product can be made in several ways but which features to use when versioning is not studied in previous studies, the conditions and reasons are unclear. The purpose of this study is to study cloud based e-learning providers to discover characteristics of feasible features when versioning. Method: To answer the defined research problem qualitative research methods was used in terms of interviews, documentations and observing artefacts. The research data was gathered through four case studies of different cloud e-learning provider, conducting of eight interviewees and four observations of their respective e-learning system. Additionally, articles and other documents was also analysed, that was provided by the companies of the case studies. Delimitations: This research was restricted so it focused on cloud e-learning providers’ perspective as they are those who use the versioning strategy. The limitations therefore only concerns e-learning and cloud services and are not generalized to all software products. Results: The results from the different data collection methods shows that feature characteristics mainly concerns market reasons with specific user preferences and customers with buying behaviors. E-learning systems brings technical reasons to why features are feasible, and the cloud have different service layers, each with different possible features to differentiate in. Costs is also a major concern when choosing feasible features for versioning. Conclusion: E-learning providers see the versioning strategy as an iterative process. Which features that are feasible for one versions seem to change over time as the market changes and product develops. Features are mainly related to the specific market. When versioning, features are separated between standard features for the whole system or segment specific features for one version. Features are also considered if they will add more value than the cost to develop. E-learning providers also develop features if they are economic and technological possible to develop, and if they are within the company’s scope.

With data volumes increasing at a high rate and the emergence of highly scalable infrastructures (cloud computing, petascale computing), distributed management of data becomes a crucial issue that faces many challenges. This thesis brings several contributions in order to address such challenges. First, it proposes a set of principles for designing highly scalable distributed storage systems that are optimized for heavy data access concurrency. In particular, it highlights the potentially large benefits of using versioning in this context. Second, based on these principles, it introduces a series of distributed data and metadata management algorithms that enable a high throughput under concurrency. Third, it shows how to efficiently implement these algorithms in practice, dealing with key issues such as high-performance parallel transfers, efficient maintainance of distributed data structures, fault tolerance, etc. These results are used to build BlobSeer, an experimental prototype that is used to demonstrate both the theoretical benefits of the approach in synthetic benchmarks, as well as the practical benefits in real-life, applicative scenarios: as a storage backend for MapReduce applications, as a storage backend for deployment and snapshotting of virtual machine images in clouds, as a quality-of-service enabled data storage service for cloud applications. Extensive experimentations on the Grid'5000 testbed show that BlobSeer remains scalable and sustains a high throughput even under heavy access concurrency, outperforming by a large margin several state-of-art approaches.

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of "dynamic data citation". The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.

La problématique "Big Data" peut être caractérisée par trois "V": * "Big Volume" se rapporte à l'augmentation sans précédent du volume des données. * "Big Velocity" se réfère à la croissance de la vitesse à laquelle ces données sont déplacées entre les systèmes qui les gèrent. * "Big Variety" correspond à la diversification des formats de ces données. Ces caractéristiques imposent des changements fondamentaux dans l'architecture des systèmes de gestion de données. Les systèmes de stockage doivent être adaptés à la croissance des données, et se doivent de passer à l'échelle tout en maintenant un accès à hautes performances. Cette thèse se concentre sur la construction des systèmes de gestion de grandes masses de données passant à l'échelle. Les deux premières contributions ont pour objectif de fournir un support efficace des "Big Volumes" pour les applications data-intensives dans les environnements de calcul à hautes performances (HPC). Nous abordons en particulier les limitations des approches existantes dans leur gestion des opérations d'entrées/sorties (E/S) non-contiguës atomiques à large échelle. Un mécanisme basé sur les versions est alors proposé, et qui peut être utilisé pour l'isolation des E/S non-contiguës sans le fardeau de synchronisations coûteuses. Dans le contexte du traitement parallèle de tableaux multi-dimensionels en HPC, nous présentons Pyramid, un système de stockage large-échelle optimisé pour ce type de données. Pyramid revoit l'organisation physique des données dans les systèmes de stockage distribués en vue d'un passage à l'échelle des performances. Pyramid favorise un partitionnement multi-dimensionel de données correspondant le plus possible aux accès générés par les applications. Il se base également sur une gestion distribuée des métadonnées et un mécanisme de versioning pour la résolution des accès concurrents, ce afin d'éliminer tout besoin de synchronisation. Notre troisième contribution aborde le problème "Big Volume" à l'échelle d'un environnement géographiquement distribué. Nous considérons BlobSeer, un service distribué de gestion de données orienté "versioning", et nous proposons BlobSeer-WAN, une extension de BlobSeer optimisée pour un tel environnement. BlobSeer-WAN prend en compte la hiérarchie de latence et favorise les accès aux méta-données locales. BlobSeer-WAN inclut la réplication asynchrone des méta-données et une résolution des collisions basée sur des "vector-clock". Afin de traîter le caractère "Big Velocity" de la problématique "Big Data", notre dernière contribution consiste en DStore, un système de stockage en mémoire orienté "documents" qui passe à l'échelle verticalement en exploitant les capacités mémoires des machines multi-coeurs. Nous montrons l'efficacité de DStore dans le cadre du traitement de requêtes d'écritures atomiques complexes tout en maintenant un haut débit d'accès en lecture. DStore suit un modèle d'exécution mono-thread qui met à jour les transactions séquentiellement, tout en se basant sur une gestion de la concurrence basée sur le versioning afin de permettre un grand nombre d'accès simultanés en lecture.

Cette thèse se situe dans le champ de la gestion des connaissances à l’aide de modèles ontologiques. Pour représenter les connaissances de domaine, nous avons conçu une ontologie hybride à deux niveaux : au niveau local, chaque groupe d’experts (du même métier) a construit sa propre ontologie, au niveau global une ontologie consensuelle regroupant les connaissances partagées est créée de façon automatique. De plus, des liens sémantiques entre les éléments de différentes ontologies locales peuvent être ajoutés.Nous avons construit un système d’aide pour guider les experts dans le processus de création de liens sémantiques ou mises en correspondance. Ses particularités sont de proposer des mesures de similarité en fonction des caractéristiques des ontologies à aligner, de réutiliser des résultats déjà calculés et de vérifier la cohérence des mises en correspondances créées.Par ailleurs, les ontologies locales peuvent être mises à jour. Cela implique des changements au niveau de l’ontologie globale ainsi que des mises en correspondances créées. De ce fait, nous avons développé une approche, adaptée à notre domaine pour gérer l’évolution de l’ontologie hybride. En particulier, nous avons utilisé la notion de versions d’ontologies afin de garder trace de toutes les modifications apportées au niveau des ontologies et de pouvoir revenir à tout moment à une version précédente.Nous avons appliqué notre travail de recherche à la géotechnique qui est un domaine complexe impliquant des experts de différents métiers. Une plateforme logicielle est en cours de réalisation et permettra de tester la faisabilité de nos travaux. / This thesis concerns the scope of knowledge management using ontological models.To represent domain knowledge, we design a hybrid ontology on two levels: In a local level, each experts’ group has designed its own ontology. In a global level, a consensual ontology containing all the shared knowledge is automatically created.We design a computer-aided system to help experts in the process of mapping creation. It allows experts to choice similarity measures relatively to the ontology characteristics, to reuse the calculated similarities and to verify the consistency of the created mappings.In addition, local ontologies can be updated. This involves modifications in the global ontology and on the created mappings. A relevant approach of our domain was developed.In particular, ontology versioning is used in order to keep a record of all the occurred modifications in the ontologies; it allows to return at any time a previous version of the hybrid ontology.The exploited domain is geotechnics which gathers various business experts. A prototype is in progress and currently does not still captures ontology evolution.