This paper delivers a comparative analysis of the performance of three of the NoSQL technologies in Web applications. These technologies are graph stores, key-value stores, and document stores. The study aims to assist developers and organizationsin picking the suitable NoSQL solution for their application. For this purpose, three identical e-book applications were developed. Each of these is connected to adatabase from the selected technologies to examine how they perform compared toeach other against various performance measures.

Deciding which database management system (DBMS) to use has perhaps never been harder. In recent years there has been an explosive growth of new types of database management systems that address different issues and performs well for different scenarios. This thesis is an improving case study of an Event Persistence Solution for the Eiffel Framework, which is a framework used for achieving traceability in very-large-scale systems development. The purpose of this thesis is to investigate whether it is possible to improve the performance of the Eiffel Event Persistence Solution by changing from MongoDB, to Elasticsearch or ArangoDB. Experiments were conducted to measure the request throughput for 4 types of requests. As a prerequisite to measuring the performance, support for the different DBMSs and the possibility to change between them was implemented. The results showed that Elasticsearch performed better than MongoDB in terms of nested-document-search as well as for graph-traversal operations. ArangoDB had even better performance for graph-traversal operations but had an inadequate performance for nested-document-search. / 决定使用哪个数据库管理系统（DBMS）可能从未如此困难过。近年来，新型数据库管理系统呈现爆炸式增长，它们解决了不同的问题，并在不同的情境中表现出优异性能。本论文是针对Eiffel框架的事件持久性解决方案的改进案例研究，该框架被用于实现超大规模系统开发中的可追溯性。本文的目的是研究是否可以通过摒弃MongoDB并改用Elasticsearch或ArangoDB来提高Eiffel事件持久性解决方案的性能。为测量4种类型的请求的请求吞吐量进行了实验。作为衡量性能的前提条件，实施了对不同数据库管理系统（可在这些系统之间进行更换）的支持。结果表明，Elasticsearch在嵌套文档搜索和图形遍历操作方面的性能均优于MongoDB。 ArangoDB在图形遍历操作方面具有比前者更好的性能，但在嵌套文档搜索方面的性能不佳。

Sociala media genererar stora mängder data men det finns fler saker som gör det och lagrar i NoSQL databassystem och smarta sensorer som registrerar elektrisk förbrukning är en av de. MongoDB är ett NoSQL databassystem som lagrar sin data i dataformatet JSONB. PostgreSQL som är ett SQL databassystem har i sina senare distributioner också börjat hantera JSONB. Det gör att PostgreSQL är en typ av hybrid då den hanterar operationer för både SQL och NoSQL. I denna studie gjordes ett experiment för att se hur dessa databassystem hanterar data för att läsa och skriva när det gäller JSON för smarta sensorer. Svarstider registrerades och försökte svara på hypotesen om PostgreSQL kan vara lämplig för att läsa och skriva JSON data som genereras av en smart sensor. Experimentet påvisade att PostgreSQL inte ökar svarstid markant när mängden data ökar för insert men för MongoDB gör det. Svaret på hypotesen om PostgreSQL kan vara lämplig för JSON data är att det är det möjligt att den kan vara det men svårt att svara på och ytterligare forskning behövs.

This thesis describes development of real-time reactive portal applications build on Meteor platform. The goal of this thesis is to describe different parts of the platform as a whole and to offer best practices to develop such applications. The theoretical part provides information covering Node.js server, MongoDB document database and their specific features. The beginning of the practical part focuses on the application design and its components implementation. The last chapter summarizes the outcome of this thesis and analyses advantages and disadvantages of Meteor platform over more traditional technology stack. The output and the main value is the Opticube application that is being used in retail. Another plus is the fact, that this thesis is the first to cover Meteor platform at the Czech University of Life Sciences in Prague.

Rapporten beskriver planering samt konstruktion av en ”modern webbapplikation” för serie- och omslagstecknaren Anders Ferm. Arbetet utfördes i syfte att erbjuda Anders en lättanvänd plattform för sin konst, där han själv kan publicera samt hantera media och information utan kunskap inom webbutveckling. Rapporten syftar till att ge inblick i hur några av de mer nya och populära webbutvecklingsramverken på marknaden kan arbetas med, samt besvara frågan om mindre traditionella ramverk och databaser faktiskt kan utföra de inom webben vanligaste typen av uppgifter och, om så är fallet, de faktiskt erbjuder fördelar över sina föregångare.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-03-14T23:51:19Z No. of bitstreams: 1 ArlindoRodriguesDaSilvaNeto_DISSERT.pdf: 3147140 bytes, checksum: 65ec83f6b7b7603769da720a2273e85b (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-03-16T23:01:46Z (GMT) No. of bitstreams: 1 ArlindoRodriguesDaSilvaNeto_DISSERT.pdf: 3147140 bytes, checksum: 65ec83f6b7b7603769da720a2273e85b (MD5) / Made available in DSpace on 2017-03-16T23:01:46Z (GMT). No. of bitstreams: 1 ArlindoRodriguesDaSilvaNeto_DISSERT.pdf: 3147140 bytes, checksum: 65ec83f6b7b7603769da720a2273e85b (MD5) Previous issue date: 2016-08-26 / Este trabalho consiste em criar uma ferramenta de BI (Business Intelligence) dispon?vel em nuvem (cloud computing) atrav?s de SaaS (Software as Service) utilizando t?cnicas de ETL (Extract, Transform, Load) e tecnologias de Big Data, com a inten??o de facilitar a extra??o descentralizada e o processamento de dados em grande quantidade. Atualmente, constata-se que ? praticamente invi?vel realizar uma an?lise consistente sem o aux?lio de um software para gera??o de relat?rios e estat?sticas. Para tais fins, a obten??o de resultados concretos com a tomada de decis?o exige estrat?gias de an?lise de dados e vari?veis consolidadas. Partindo dessa vis?o, enfatiza-se neste estudo o Business Intelligence (BI) com o objetivo de simplificar a an?lise de informa??es gerenciais e estat?sticas para propiciar indicadores atrav?s de gr?ficos ou listagens din?micas de dados gerenciais. Assim, ? poss?vel inferir que, com o crescimento exponencial dos dados torna-se cada vez mais dif?cil a obten??o de resultados de forma r?pida e consistente, tornando necess?rio atuar com novas t?cnicas e ferramentas para tratamentos de dados em larga escala. Este trabalho ? de natureza t?cnica de cria??o de um produto de Engenharia de Software, fundamentado a partir do estudo da arte da ?rea, e de um comparativo com as principais ferramentas existentes no mercado, evidenciando vantagens e desvantagens da solu??o criada. / This work is to create a BI tool (Business Intelligence) available in the cloud (cloud computing) through SaaS (Software as Service) using ETL techniques (extract, transform, load) and Big Data technologies, with the intention of facilitating decentralized extraction and data processing in large quantities. Currently, it appears that it is practically impossible conduct a consistent analysis without the aid of a software for reporting and statistics. For these purposes, the achievement of concrete results with decision making requires data analysis strategies and consolidated variable. From this view, it is emphasized in this study Business Intelligence (BI) in order to simplify the analysis of management information and statistics to provide indicators through graphs or dynamic lists of data management. Thus, it is possible to infer that with the exponential growth of data becomes increasingly difficult to obtain results quickly and consistently, making it necessary to work with new techniques and tools for large-scale data processing. This work is technical in nature to create a product of Software Engineering, based from the study of art in the area, and a comparison with the main existing tools on the market, showing advantages and disadvantages of the created solution. / 2020-12-31

For quite some time relational databases, such as MySQL, Oracle and Microsoft SQL Server, have been used to store data for most applications. While they are indeed ACID compliant (meaning interrupted database transactions won't result in lost data or similar nasty surprises) and good at avoiding redundancy, they are difficult to scale horizontally (across multiple servers) and can be slow for certain tasks. With the Web growing rapidly, spawning enourmous, user-generated content websites such as Facebook and Twitter, fast databases that can handle huge amounts of data are a must. For this purpose new databases management systems collectively called NoSQL are being developed. This thesis explains NoSQL further and compares the write and retrieval speeds, as well as the space efficiency, of two database management systems from the document oriented branch of NoSQL called MongoDB and CouchDB, which both use the JavaScript Object Notation (JSON) to store their data within. The benchmarkings performed show that MongoDB is quite a lot faster than CouchDB, both when inserting and querying, when used with their respective Python libraries and dynamic queries. MongoDB also is more space efficient than CouchDB.

The session-based SAMBO is an ontology alignment system involving MySQL to store matching results. Currently, SAMBO is able to align most ontologies within acceptable time. However, when it comes to large scale ontologies, SAMBO fails to reach the target. Thus, the main purpose of this thesis work is to improve the performance of SAMBO, especially in the case of matching large scale ontologies.  To reach the purpose, a comprehensive literature study and an investigation on two outstanding large scale ontology system are carried out with the aim of setting the improvement directions. A detailed investigation on the existing SAMBO is conducted to figure out in which aspects the system can be improved. Parallel matching process optimization and data management optimization are determined as the primary optimization goal of the thesis work. In the following, a few relevant techniques are studied and compared. Finally, an optimized design is proposed and implemented.  System testing results of the improved SAMBO show that both parallel matching process optimization and data management optimization contribute greatly to improve the performance of SAMBO. However the execution time of SAMBO to align large scale ontologies with database interaction is still unacceptable.

It can be difficult to choose the right NoSQL DBMS, and some systems lack sufficient research and evaluation. There are also tools for moving and transforming data between DBMS' in order to combine or use different systems for different use cases. We have described a use case, based on requirements related to the quality attributes Consistency, Scalability, and Performance. For the Performance attribute, focus is fast insertions and full-text search queries on a large dataset of forum posts. The evaluation was performed on two NoSQL DBMS' and two tools for transforming data between them. The DBMS' are MongoDB and Elasticsearch, and the transformation tools are NotaQL and Compose's Transporter. The purpose is to evaluate three different NoSQL systems, pure MongoDB, pure Elasticsearch and a combination of the two. The results show that MongoDB is faster when performing simple full-text search queries, but otherwise slower. This means that Elasticsearch is the primary choice regarding insertion and complex full-text search query performance. MongoDB is however regarded as a more stable and well-tested system. When it comes to scalability, MongoDB is better suited for a system where the dataset increases over time due to its simple addition of more shards. While Elasticsearch is better for a system which starts off with a large amount of data since it has faster insertion speeds and a more effective process for data distribution among existing shards. In general NotaQL is not as fast as Transporter, but can handle aggregations and nested fields which Transporter does not support. A combined system using MongoDB as primary data store and Elasticsearch as secondary data store could be used to achieve fast full-text search queries for all types of expressions, simple and complex.

The project is about the investigation on Document-Based databases, their evaluation criteria and use cases regarding requirements management, SW architecture and test management to set up an (ESLM) Embedded Systems Lifecycle Management tool. The current database used in the ESLM is a graph database called Neo4j, which meets the needs of the current system. The result of studying Document databases turned to the decision of not using a Document database for the system. Instead regarding the requirements, a combination of Graph database and Document database could be the practical solution in future.