Syftet med denna uppsats var att jämföra två olika databashanteringssystem, MongoDB samt MariaDB, utifrån specifika krav från en uppdragsgivare gällande prestanda samt komplexitet. Då MariaDB är ett SQL-databashanteringssystem och MongoDB ett NoSQLdatabashanteringssystem som bygger på olika databasmodeller behandlas data på olika sätt, vilket ligger som grund till jämförelsen mellan de olika databashanteringssystemen. Uppsatsen fokuserar på att utifrån fyra olika tester, två prestandatestet och två tester som jämför komplexiteten, kunna jämföra databashanteringssystemen MariaDB och MongoDB. Dessa databashanteringssystem ställdes emot de angivna kraven från uppdragsgivaren för att se vilket av dem som är bäst lämpat. Två olika applikationer utvecklades med hjälp av C# och användes under testerna för att utföra testerna. Efter att testerna utförts rekommenderades MongoDB till uppdragsgivaren på grund av den prestandafördel som testerna visade på i långsiktig användning av systemet. Även komplexiteten för MongoDB visade sig vara mindre vilket stärker rekommendationen ytterligare.

This study conducted an experiment on NoSQL and NewSQL database management systems where the average throughput of Cassandra, CockroachDB, MongoDB, and VoltDB was compared using five workloads composed of different proportions of read and update queries. How much these different workload compositions affect throughput for each individual database management system was also investigated. The results showed that VoltDB had the highest throughput overall, and its throughput was affected the least by the workloads’ composition. MongoDB had similar high throughput consistency across workloads but at a much lower throughput level, and its throughput was affected much more by the workload compositions than VoltDB. Cassandra had extremely high throughput for 100 percent update workloads,even beating VoltDB in certain cases, but showed underwhelming results for all other workloads. CockroachDB’s throughput was by far the worst at workloads that had any update queries, but was comparable and sometimes even better than Cassandra and MongoDB with 100 percent read workloads. CockroachDB’s throughput proved to be the most affected by the query composition of workloads.

Ericsson’s Region IPR & Licensing (RIPL) receives about 3000 thousands Invention Disclosures (IvDs) every year submitted by researchers as a result of their R&D activities. To decide whether an IvD has a good business value and a patent application should be filed; a rigorous evaluation process is carried out by a selected Patent Attorney (PA). One of most important elements of the evaluation process is to find prior art similar, including similar IvDs that have been evaluated before. These documents are not public and therefore can’t be searched using available search tools. For now the process of finding prior art is done manually (without the help of any search tools) and takes up significant amount of time. The aim of this Master’s thesis is to develop and test an information retrieval search engine as a proof of concept to find similar Invention Disclosure documents and related patent applications. For this purpose, a SOLR database server is setup with up to seven thousand five hundred (7500) IvDs indexed. A similarity algorithm is implemented which is customized to weight different fields. LUCENE is then used to query the server and display the relevant documents in a web application.

Denna studie jämför relationsdatabasen MySQL med icke-relationsdatabasen MongoDB när komplexa sökfrågor ställs mot ett dataset som består av över 67 000 rader data. Problemet som uppstår blir att veta vilken av MySQL och MongoDB som presterar bäst och med kortast svarstider. För att ta reda på vilken av databaserna som har kortast svarstider har experiment utförts i en kontrollerad miljö, för att få ett så korrekt resultat som möjligt. Resultatet av experimentet blev att MySQL fick överlag kortast svarstider när komplexa sökfrågor ställdes. När en enkel sökfråga ställdes, fick MySQL och MongoDB nästintill identiska svarstider. Slutsatsen som drogs från denna studie var att MySQL fick snabbare svarstider än MongoDB när komplexa sökfrågor ställdes mot ett dataset som bestod av en stor mängd data.

Syftet med studien är att göra en fördjupning inom NoSQL-databaser och undersöka vilka uppgifter som de olika NoSQL-grupperna passar bäst till i ett socialt nätverk, som t.ex. Facebook och Twitter. Det finns fyra olika typer av NoSQL-databaser: kolumndatabaser, grafdatabaser, nyckelvärdedatabaser och dokumentdatabaser. Frågan är vilken NoSQL-databas ska man välja till en viss uppgift i ett givet socialt nätverk. När man ska utveckla ett socialt nätverk, som kräver lagring av data, är det viktigt att känna till vilken typ av databas som bör användas till en vis typ av uppgift. För att få svar på frågorna har det gjorts en undersökning över vad tidigare forskning har kommit fram till. Det har även gjorts en praktisk studie med alla fyra NoSQL-grupper i ett experiment med lagring av användaruppgifter, meddelanden och vänner. / The purpose of the study is to deepen within NoSQL databases and investigate what tasks the different NoSQL groups fit best in a social network, such as Facebook and Twitter. The data is, for example, about the storage of personal data or social networking. There are four different types of NoSQL databases: column databases, graph databases, key value databases and document databases. The question is which NoSQL database should be chosen for a particular task in a given social network. When developing a social network that requires data storage, it is important to know what kind of database should be used for a certain type of task.In order to answer the questions, an investigation has been made of what previous research has reached. There has also been a practical study of all four NoSQL groups in an experiment with storing user information, messages and friends.

This study proposes a performance comparison between the different structures of NoSQL databases; document, key-value, column and graph. A second study is also conducted, when looking at performance comparison between three different NoSQL databases, all of the same structure; document based, the databases that are tested here are; MongoDB, OrientDB and Couchbase. Performance tests are conducted using a benchmarking tool YCSB (Yahoo! Cloud Serving Benchmark), and by looking at time to execute and throughput (operations/ second). Beside benchmarking literature reviews are conducted to be able to understand the different NoSQL structures, and to elaborate our benchmarking results. Every NoSQL structure and database in our benchmark is tested in the same way, a loading phase of 1k, 10k and 100k entries, and a running phase with a workload of approximately 50% reads and 50% updates with 1k, 10k and 100k operations. The finding of this study is that there are differences in performance, both between different structures and between same structured NoSQL databases. Document based OrientDB was the highest performing database at high volumes of data, and key-value store database Redis performed best at low volumes of data. Reasons for performance differences are both linked to specific trademarks of the structural orientation, the usage of the specific attributes of CAP theorem, storage type and development language.

The need to store large amounts of data is always increasing and this requires better solutions for storing and managing the data. This is often done using a Database Management System (DBMS) which helps manage all data. There are a lot of different options to use today, where all serve a different purpose. This means that it is important to choose the right DBMS for the data that you have. Furthermore, it is not enough to just choose the best DBMS, the database then needs to be designed so that the data can be stored in a structured way. This can be done in many ways. Ericsson wants to implement a database solution in one of their systems to make the workflow more efficient. The system is used to store data for configuring 5G nodes in testing environments. To do this, an investigation about which DBMS fits this data best is to be done. For this purpose, the PostgreSQL database is chosen to represent SQL databases and MongoDB is chosen to represent NoSQL databases. Additionally, proposed designs for each DBMS are produced. These designs are compared with regards to their response time for common queries, as well as in a load test with an expected load on the database. The results show that the two DBMS are good in different aspects. For example, PostgreSQL is faster when relationships between different tables are used, but MongoDB is faster when querying only one document. In conclusion, both implementations serve their purpose and do have their benefits, but MongoDB is chosen to be the better one given the knowledge of how the system is to be used.

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Denna studie har benchmarkat NoSQL systemen MongoDB och CassandraDB via en webbapplikation. NoSQL system är databassystem som har en partitionstolerans förmåga, vilket betyder att NoSQL system har en förmåga att fördela ett data-set över flera hårddiskar. Vad som har benchmarkats är tiden det tar för systemen att behandla olika slumpgenererade serverfrågor. Utförandet av studien har skett via en webbapplikation vilket erbjuds av en Windows maskin. Studie har bedrivits helt lokalt så denna Windows maskin agerar som värd för databassystemen samt som maskinen som utför mätserierna. Hypotesen förespår att NoSQL systemet MongoDB ska ha bättre förmåga att hantera det valda E-sport data-set. Resultatet från studien tyder på att MongoDB kan utföra testfallen under kortare period, men studie upplyser samt om att det finns användningsområden för CassandraDB systemet. Detta arbete kan vara användbart för framtida studier vars forskning ska utvärdera NoSQL system.

Made available in DSpace on 2017-07-21T14:19:26Z (GMT). No. of bitstreams: 1 Giuvane Conti.pdf: 4640595 bytes, checksum: 144683112870e74bdbcd0335c9aac15a (MD5) Previous issue date: 2015-10-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The growth of the smartphone market, combined with the technologies used in these devices, motivates the use of this equipment in field applications because of its georeferencing potential and internet access. In cloud computing, a prominent concept is the Internet of Things (IoT) due to the exponential growth of data where are generated in day-to-day. The idea of centralization of this data, acquired from various areas, such as agriculture, urban mobility, urbanization, government, health, business, etc., are current challenges that the IoT find out to solve. It still in contrast, NoSQL databases, which have some differentiating features from relational models and they have the potential to solve some of these challenges, such as horizontal growth, high data availability and easy storage of complex structures. Another promising technology to solve some of these challenges is the RESTful, a web service that provides faster and works with fewer bytes than the communication with Simple Object Access Protocol (SOAP). In this context, this research was developed with the goal of creating a GIS Mobile application, to collect geospatial data (points, lines and polygons) and store them in an application server, in Software as a Service (SaaS) architecture, using RESTful and NoSQL. The structure follows the concept of IoT, with high potential for integration data from different areas, such as in this study. In this research were performed tests in field at the Fazenda Escola Capão-da-onça (FESCON) on 11 and 13 August 2015. A planning missions, has been pre - set to get the best results from Global Positioning System (GPS). On those dates were collected geo data on crop plots, lines and points of interest, such as erosion areas, failures in the soil and low productivity areas. The samples taken were successful and had no problems or failures, that is, the application provided facility of use and synchronization of the data with the application server. The results of the field surveys, have been exported to files in KML format and presented on thematic maps with the help of Google Earth tool. In conclusion, the project has a high potential for registration of agricultural properties and spatial geo data and, your architecture is relevant for the development of geospatial, agricultural projects in GIS Mobile area, where need to handle complex data and provide high data availability, such as Rural environmental registry (CAR, in brazil); Precision agriculture; Georeferenced control of agricultural, natural resources and urbanization data; and Business logistics applications. / O crescimento do mercado de smartphones, combinado com as tecnologias empregadas nestes aparelhos, motiva seu uso em aplicações a campo devido ao seu potencial de georreferenciamento e acesso a internet. Em computação na nuvem, um conceito proeminente é a Internet of Things (IoT) ou Internet das Coisas devido ao crescimento exponencial de dados que são gerados no dia-a-dia. A ideia da centralização destes dados, provenientes de várias áreas, como: agricultura, mobilidade urbana, urbanização, governamentais, saúde, empresariais, etc., são desafios atuais que a Internet das Coisas procura solucionar. Destaca-se ainda, os bancos de dados NoSQL, os quais possuem potencial para solução de alguns destes desafios. Outra tecnologia promissora para solucionar alguns destes desafios é o padrão RESTful, um web service que apresenta rapidez e trabalha com quantidades reduzidas de bytes em sua comunicação. Neste contexto, esta pesquisa foi desenvolvida com o objetivo de criar um Sistema de Informação Geográfica (SIG) Móvel, que realize a coleta de dados geoespaciais (pontos, linhas e polígonos), se comunique com um servidor de aplicação, no modelo Software as a Service (SaaS) que irá armazenar os dados em um banco de dados MongoDB e sua arquitetura segue o conceito de Internet das Coisas. Foram realizados testes a campo na Fazenda Escola Capão-da-onça (FESCON) nos dias 11 e 13 de agosto de 2015. Um planejamento de missões, foi pré - estabelecido, para obter os melhores resultados do Sistema de Posicionamento Global (GPS). Nas referidas datas foram coletados polígonos em talhões de culturas, linhas e pontos de interesse, tais como áreas de erosão, falhas no solo e áreas de baixa produtividade. As coletas realizadas foram bem sucedidas, o aplicativo proporcionou facilidade de uso e de sincronismo dos dados com o servidor de aplicação. Os resultados, dos levantamentos à campo, foram exportados para arquivos no formato KML e apresentados em mapas, com auxílio da ferramenta Google Earth. Conclui-se que, o projeto apresenta alto potencial de uso para cadastro de propriedades agrícolas e dados espacializados e, sua arquitetura é pertinente para o desenvolvimento de projetos geoespaciais, agrícolas em SIG Móvel, que necessitem tratar dados complexos e apresentem alta disponibilidade de dados, tais como: Cadastro Ambiental Rural (CAR); Agricultura de precisão; Controle georreferenciado de dados agrícolas, recursos naturais e urbanização; e Aplicações empresariais de logística.