The aim of my thesis is to provide a collection of examples about document oriented MongoDB database using Node.js platform, specifically using the Mongoose program, for object-document mapping (ODM). The aim is met by analysis of Mongoose and Async module, which provides functions for more comprehensive asynchronous querying, needed for working with input/output to the MongoDB database in Node.js. The main merit of this thesis is (in the general sense) a demonstration of how to create a administration part of web application (backend) in Node.js, applying document oriented MongoDB database. The thesis discusses, in the theoretical part, about characteristics and significance of document oriented MongoDB database, about characteristics and architecture of Node.js platform employing untyped and multiplatform JavaScript language and about object document mapping (ODM) programs for Node.js on MongoDB. The practical part contains a collection of examples, where in the first chapter introduces an instalation and execution manual of necessary programs. The next chapter is dedicated to simple examples of Mongoose module and in the last chapter there are stated the complex examples of Mongoose and Async modules, which are the main merits of this thesis.

De nos jours, des millions de sources de données différentes produisent une énorme quantité de données non structurées et semi-structurées qui changent constamment. Les systèmes d'information doivent gérer ces données tout en assurant la scalabilité et la performance. En conséquence, ils ont dû s'adapter pour supporter des bases de données hétérogènes, incluant des bases de données No-SQL. Ces bases de données proposent une structure de données sans schéma avec une grande flexibilité, mais sans séparation claire des couches logiques et physiques. Les données peuvent être dupliquées, fragmentées et/ou incomplètes, et ils peuvent aussi changer à mesure des besoins de métier.La flexibilité et l’absence de schéma dans les systèmes NoSQL orientés documents, telle que MongoDB, permettent d’explorer des nouvelles alternatives de structuration sans faire face aux contraintes. Le choix de la structuration reste important et critique parce qu’il y a plusieurs impacts à considérer et il faut choisir parmi des nombreuses d’options de structuration. Nous proposons donc de revenir sur une phase de conception dans laquelle des aspects de qualité et les impacts de la structure sont pris en compte afin de prendre une décision d’une manière plus avertie.Dans ce cadre, nous proposons SCORUS, un système pour l’analyse et l’évaluation des structures orientés document qui vise à faciliter l’étude des possibilités de semi-structurations orientées document, telles que MongoDB, et à fournir des métriques objectives pour mieux faire ressortir les avantages et les inconvénients de chaque solution par rapport aux besoins des utilisateurs. Pour cela, une séquence de trois phases peut composer un processus de conception. Chaque phase peut être aussi effectuée indépendamment à des fins d’analyse et de réglage. La stratégie générale de SCORUS est composée par :1. Génération d’un ensemble d’alternatives de structuration : dans cette phase nous proposons de partir d’une modélisation UML des données et de produire automatiquement un large ensemble de variantes de structuration possibles pour ces données.2. Evaluation d’alternatives en utilisant un ensemble de métriques structurelles : cette évaluation prend un ensemble de variantes de structuration et calcule les métriques au regard des données modélisées.3. Analyse des alternatives évaluées : utilisation des métriques afin d’analyser l’intérêt des alternatives considérées et de choisir la ou les plus appropriées. / Nowadays, millions of different data sources produce a huge quantity of unstructured and semi-structured data that change constantly. Information systems must manage these data but providing at the same time scalability and performance. As a result, they have had to adapt it to support heterogeneous databases, included NoSQL databases. These databases propose a schema-free with great flexibility but with a no clear separation of the logical and physical layers. Data can be duplicated, split and/or incomplete, and it can also change as the business needs.The flexibility and absence of schema in document-oriented NoSQL systems, such as MongoDB, allows new structuring alternatives to be explored without facing constraints. The choice of the structuring remains important and critical because there are several impacts to consider and it is necessary to choose among many of options of structuring. We therefore propose to return to a design phase in which aspects of quality and the impacts of the structure are considered in order to make a decision in a more informed manner.In this context, we propose SCORUS, a system for the analysis and evaluation of document-oriented structures that aims to facilitate the study of document-oriented semi-structuring possibilities, such as MongoDB, and to provide objective metrics for better highlight the advantages and disadvantages of each solution in relation to the needs of the users. For this, a sequence of three phases can compose a design process. Each phase can also be performed independently for analysis and adjustment purposes. The general strategy of SCORUS is composed by:1. Generation of a set of structuration alternatives: in this phase we propose to start from UML modeling of the data and to automatically produce a large set of possible structuring variants for this data.2. Evaluation of Alternatives Using a Set of Structural Metrics: This evaluation takes a set of structuring variants and calculates the metrics against the modeled data.3. Analysis of the evaluated alternatives: use of the metrics to analyze the interest of the considered alternatives and to choose the most appropriate one(s).

Les systèmes d’aide à la décision occupent une place prépondérante au sein des entreprises et des grandes organisations, pour permettre des analyses dédiées à la prise de décisions. Avec l’avènement du big data, le volume des données d’analyses atteint des tailles critiques, défiant les approches classiques d’entreposage de données, dont les solutions actuelles reposent principalement sur des bases de données R-OLAP. Avec l’apparition des grandes plateformes Web telles que Google, Facebook, Twitter, Amazon… des solutions pour gérer les mégadonnées (Big Data) ont été développées et appelées « Not Only SQL ». Ces nouvelles approches constituent une voie intéressante pour la construction des entrepôts de données multidimensionnelles capables de supporter des grandes masses de données. La remise en cause de l’approche R-OLAP nécessite de revisiter les principes de la modélisation des entrepôts de données multidimensionnelles. Dans ce manuscrit, nous avons proposé des processus d’implantation des entrepôts de données multidimensionnelles avec les modèles NoSQL. Nous avons défini quatre processus pour chacun des deux modèles NoSQL orienté colonnes et orienté documents. De plus, le contexte NoSQL rend également plus complexe le calcul efficace de pré-agrégats qui sont habituellement mis en place dans le contexte ROLAP (treillis). Nous avons élargis nos processus d’implantations pour prendre en compte la construction du treillis dans les deux modèles retenus.Comme il est difficile de choisir une seule implantation NoSQL supportant efficacement tous les traitements applicables, nous avons proposé deux processus de traductions, le premier concerne des processus intra-modèles, c’est-à-dire des règles de passage d’une implantation à une autre implantation du même modèle logique NoSQL, tandis que le second processus définit les règles de transformation d’une implantation d’un modèle logique vers une autre implantation d’un autre modèle logique. / Decision support systems occupy a large space in companies and large organizations in order to enable analyzes dedicated to decision making. With the advent of big data, the volume of analyzed data reaches critical sizes, challenging conventional approaches to data warehousing, for which current solutions are mainly based on R-OLAP databases. With the emergence of major Web platforms such as Google, Facebook, Twitter, Amazon...etc, many solutions to process big data are developed and called "Not Only SQL". These new approaches are an interesting attempt to build multidimensional data warehouse capable of handling large volumes of data. The questioning of the R-OLAP approach requires revisiting the principles of modeling multidimensional data warehouses.In this manuscript, we proposed implementation processes of multidimensional data warehouses with NoSQL models. We defined four processes for each model; an oriented NoSQL column model and an oriented documents model. Each of these processes fosters a specific treatment. Moreover, the NoSQL context adds complexity to the computation of effective pre-aggregates that are typically set up within the ROLAP context (lattice). We have enlarged our implementations processes to take into account the construction of the lattice in both detained models.As it is difficult to choose a single NoSQL implementation that supports effectively all the applicable treatments, we proposed two translation processes. While the first one concerns intra-models processes, i.e., pass rules from an implementation to another of the same NoSQL logic model, the second process defines the transformation rules of a logic model implementation to another implementation on another logic model.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-25T12:27:42Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_CarlosGomes_MPROF_CINUFPE_2016.pdf: 4079444 bytes, checksum: 308622549a641d5ab125dbbdbceb4d2d (MD5) / Made available in DSpace on 2017-04-25T12:27:42Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_CarlosGomes_MPROF_CINUFPE_2016.pdf: 4079444 bytes, checksum: 308622549a641d5ab125dbbdbceb4d2d (MD5) Previous issue date: 2016-08-08 / NoSQL é uma tecnologia de sistemas de gerenciamento de banco de dados (SGBD) emergente, tendo modelos flexíveis focados em desempenho e escalabilidade, proposta para a manipulação de grandes quantidades de dados. NoSQL não substitui as abordagens de sistemas de gerenciamento de banco de dados relacionais, mas sim atende às restrições relacionadas à manipulação de dados em massa. Tal tecnologia já é aplicada em sistemas bem conhecidos em todo o mundo, tais como serviços de e-commerce e middleware. A importância de tal tecnologia tem motivado muitos trabalhos, principalmente em relação ao desempenho. Poucos trabalhos caracterizam e comparam o consumo de energia no contexto de SGBDs NoSQL, apesar de sua importância. De fato, o consumo de energia não deve ser negligenciado devido ao aumento dos custos financeiros e ambientais. A fim de avaliar essa questão, este trabalho analisa o desempenho e consumo de energia em sistemas de gerenciamento de banco de dados NoSQL, selecionamos o Cassandra (coluna), MongoDB (orientado a documento) e Redis (chave-valor) por serem representativos exemplos desta tecnologia. A metodologia baseia-se em Design of Experiments, de tal forma que as cargas de trabalho são geradas por Yahoo! Cloud Serving Benchmark (YCSB) produzindo leitura, escrita e atualização, por ciclos de 1.000, 10.000 e 100.000 operações. Como resultado são avaliados 27 tratamentos. Para a medição do consumo de energia é aplicado um framework específico chamado Emeter. As métricas são tempo de execução e consumo de energia, assim como a evolução no incremento da carga de trabalho. Os resultados demonstram que o consumo de energia pode variar significativamente entre os SGBDs para comandos distintos e cargas de trabalho. Conclui-se ainda que mesmo havendo uma correlação positiva entre o consumo de energia e o tempo de execução, o SGBD mais rápido não é, necessariamente o que utiliza menos energia. / NoSQL is an emergent database management systems technology (DBMS), having flexible models focused on performance and scalability, proposed for manipulating massive amounts of data. NoSQL is not intending for replacing the relational database management systems approaches, but to overcome constraints related to massive data manipulation. Such a technology already is applied in well-known systems around the world, such as e-commerce and middleware services. The importance of such technology has motivated lots of works, mainly relating to performance. Few works can be enumerated regarding characterization of energy consumption on NoSQL DataBase Management Systems, despite its importance. In fact the energy consumption is a feature that cannot be neglected due its impact on financial cost and environmental questions. In order to deal with such an issue, this work evaluates not only performance but the energy consumption involved on NoSQL DataBase Management Systems, specifically for Cassandra (Column), MongoDB (Document Oriented) and Redis (Key-Value). The methodology is based on Design of Experiments, in such a way the workloads are generated by Yahoo! Cloud Serving Benchmark (YCSB) producing readings, writings and updatings by cycles of 1.000, 10.000 and 100.000. As result, it is evaluated twenty seven treatments. For measuring energy consumption is applied a specific framework named Emeter. The Emeter captures metrics such as execution time and energy consumption related to treatments under analyze. In addition to the individual evaluation, the performance and energy consumption are analyzed among relevant scenarios, as well as the trends due to increases in the workload. The results demonstrate that energy consumption can differs for each DBMS according to command and workload. Additionally, the results make it possible to infer that despite the well-known positive correlation between performance and energy consumption, the fastest DBMS is not necessarily the best on saving energy.

This work focuses on NoSQL databases, their use for analytical tasks and on comparison of NoSQL databases with relational and OLAP databases. The aim is to analyse the benefits of NoSQL databases and their use for analytical purposes. The first part presents the basic principles of Business Intelligence, Data Warehousing, and Big Data. The second part deals with the key features of relational and NoSQL databases. The last part of the thesis describes the properties of four basic types of NoSQL databases, analyses their advantages, disadvantages and areas of application. The end of this part in-cludes specific examples of the use of NoSQL databases, together with the reasons for the selection of those solutions.