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Relevant bibliographies by topics / NoSQL

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Academic literature on the topic 'NoSQL'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "NoSQL"

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Krapans, Jānis, and Sergejs Kodors. "NOSQL DATABASES." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 25 (April 23, 2021): 55–58. http://dx.doi.org/10.17770/het2021.25.6780.

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Darbā ir aprakstīts trīs NoSQL datubāžu salīdzinājums: Mango, CouchBase un Cassandra. Datubāžu salīdzinājuma dati tika iegūti izmantojot YCSB- programmu, kura noslogo datubāzi ar dažādām darba slodzēm, kuras atbilst mūsdienu moderno aplikāciju prasībām. Testu izpildes beigās tiks iegūti katras datubāzes veiktspējas dati, kā arī tās latentums

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Lalwani, Raj. "NOSQL Databases." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (2018): 877–82. http://dx.doi.org/10.22214/ijraset.2018.6133.

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Tsai, Chia-Ping, Che-Wei Chang, Hung-Chang Hsiao, and Haiying Shen. "The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase." Future Internet 14, no. 3 (2022): 92. http://dx.doi.org/10.3390/fi14030092.

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Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.

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Ashwaq A. Alotaibi, Reem M. Alotaibi and Nermin Hamza, Ashwaq A. Alotaibi, Reem M. Alotaibi and Nermin Hamza. "Access Control Models in NoSQL Databases: An Overview." journal of king abdulaziz university computing and information technology sciences 8, no. 1 (2019): 1–9. http://dx.doi.org/10.4197/comp.8-1.1.

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Recently non-relational databases known as NoSQL have become most popular for handling a huge amount of data. Many organizations move from relational databases towards NoSQL databases due to the growing popularity of cloud computing and big data. NoSQL database is designed to handle unstructured data like documents, e-mails, and social media efficiently. It uses distributed and cooperating devices to store and retrieve data. As a large number of people storing sensitive data in NoSQL databases, security issues become critical concerns. NoSQL has many advantages like scalability and availability, but it suffers from some security issues like weak authorization mechanisms. This paper reviews the different models of NoSQL databases and the security issues concerning these databases. In addition, we present the existing access control models in different NoSQL databases.

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Aftab, Zain, Waheed Iqbal, Khaled Mohamad Almustafa, Faisal Bukhari, and Muhammad Abdullah. "Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping." Scientific Programming 2020 (July 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8813350.

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Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

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Chaudhary, Renu, and Gagangeet Singh. "A NOVEL TECHNIQUE IN NoSQL DATA EXTRACTION." International Journal of Research -GRANTHAALAYAH 1, no. 1 (2014): 51–58. http://dx.doi.org/10.29121/granthaalayah.v1.i1.2014.3086.

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NoSQL databases (commonly interpreted by developers as „not only SQL databases‟ and not „no SQL‟) is an emerging alternative to the most widely used relational databases. As the name suggests, it does not completely replace SQL but compliments it in such a way that they can co-exist. In this paper we will be discussing the NoSQL data model, types of NoSQL data stores, characteristics and features of each data store, query languages used in NoSQL, advantages and disadvantages of NoSQL over RDBMS and the future prospects of NoSQL. Motivation/Background:NoSQL systems exhibit the ability to store and index arbitrarily big data sets while enabling a large amount of concurrent user requests. Method:Many people think NoSQL is a derogatory term created to poke at SQL. In reality, the term means Not Only SQL. The idea is that both technologies can coexist and each has its place. Results:Large-scale data processing (parallel processing over distributed systems); Embedded IR (basic machine-to-machine information look-up & retrieval); Exploratory analytics on semi-structured data (expert level); Large volume data storage (unstructured, semi-structured, small-packet structured). Conclusions:This study report motivation to provide an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data; as well as to provide a global overview of this non-relational NoSQL databases.

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Laksmita, Nadea Cipta, Erwin Apriliyanto, I. Wayan Pandu, and Kusrini Rini. "Comparison of NoSQL Database Performance with SQL Server Database on Online Airplane Ticket Booking." Indonesian Journal of Applied Informatics 4, no. 2 (2020): 64. http://dx.doi.org/10.20961/ijai.v4i2.38956.

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<em>Flight ticket booking services have become more advanced, where bookings can be made through the android / iOS application and through a web browser, ticket reservations, no longer have to come to travel agents or come to the airport to book plane tickets. In this study using an online ticket booking database where one database uses the NoSQL database and another database uses SQL Server. The purpose of this research is to test the performance of NoSQL speed with SQL Server with the Insert, Delete and Select commands. The testing method uses 100 records, 500 records, 1000 records, and 5000 records, with each record being tested four times and then taken on average. The results of this study are that the NoSQL database Insert command has a speed 4 times faster than the SQL Server database for under 500 records, whereas above 500 NoSQL database records 5 times slower, the Delete NoSQL database command has a speed 3 times faster than the SQL database Server, and the command Select 1 NoSQL database table 55 times faster than SQL Server databases, while 2 NoSQL database tables are 18 times slower than SQL Server databases, while 3 NoSQL database tables are 10 times slower than SQL Server databases, whereas 4 database tables NoSQL is 16 times slower than SQL Server databases.</em>

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Koutroumanis, Nikolaos, Nikolaos Kousathanas, Christos Doulkeridis, and Akrivi Vlachou. "A demonstration of NoDA." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2851–54. http://dx.doi.org/10.14778/3476311.3476361.

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In this demo paper, we present a system prototype, called NoDA, that unifies access to NoSQL stores, by exposing a single interface to big data developers. This hides the heterogeneity of NoSQL stores, in terms of different query languages, non-standardized access, and different data models. NoDA comprises a layer positioned on top of NoSQL stores that defines a set of basic data access operators (filter, project, aggregate, etc.), implemented for different NoSQL engines. The provision of generic data access operators enables a declarative interface using SQL as query language. Furthermore, NoDA is extended to provide more complex operators, such as geospatial operators, which are only partially supported by NoSQL stores. We demonstrate NoDA by showcasing that the exact same query can be processed by different NoSQL stores, without any modification or transformation whatsoever.

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Shah, Monika, Amit Kothari, and Samir Patel. "A Comprehensive Survey on Energy Consumption Analysis for NoSQL." Scalable Computing: Practice and Experience 23, no. 1 (2022): 35–50. http://dx.doi.org/10.12694/scpe.v23i1.1971.

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During the last few years, we are witnessing increasing development in the Internet of Things (IoT) and big data. To address increasing workload complexity with better performance and to handle scalability issues of such applications, non-relational (NoSQL) has started taking the place of relational databases. With increasing load, it is challenging to maintain NoSQL’s performance, scalability, and availability without expanding the capacity of hosts and power budget of computing resources. Future scaling of data center capabilities depends on the improvement of server power efficiency. Considering the rise of energy costs and environmental sustainability, we can not ignore this high energy consumption caused by NoSQL. Despite the increasing popularity and share of NoSQL in the software market, little is still known about its energy footprint. To the best of our knowledge, there are no comprehensive studies that analyze the energy consumption by various modules of NoSQL. This article, therefore, conducts a comprehensive survey on the energy consumption analysis of NoSQL. There are limited proposals to reduce the energy consumption of NoSQL. This paper also provides a brief description of these little efforts on reducing the energy consumption of NoSQL. Based on the review, this paper discusses the research scope and opportunities for researchers to improve the energy conservation of NoSQL systems.

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BenAli-Sougui, Ines, Minyar Sassi Hidri, and Amel Grissa-Touzi. "No-FSQL." International Journal of Fuzzy System Applications 5, no. 2 (2016): 54–63. http://dx.doi.org/10.4018/ijfsa.2016040104.

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NoSQL (Not only SQL) is an efficient database model for storing and manipulating huge quantities of precise data. However, most NoSQL databases scale well as data grows and often are flexible enough to accommodate imprecise and ambiguous data. This comprehensive hands-on guide presents fundamental concepts and practical solutions for using fuzziness with NoSQL to deals with fuzzy databases (FDB). In this paper, the authors present a graph-based fuzzy NoSQL model to deal with large fuzzy databases while extending the NoSQL one. The authors consider the cypher declarative query language proposed for Neo4j which is the current leader on this market to querying fuzzy databases.

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Dissertations / Theses on the topic "NoSQL"

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Souza, Jane Adriana. "NoSQL2 : administrando banco de dados NoSQL com a linguagem SQL." reponame:Repositório Institucional da UnB, 2016. http://repositorio.unb.br/handle/10482/22948.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2016.<br>Submitted by Albânia Cézar de Melo (albania@bce.unb.br) on 2017-02-15T12:23:44Z No. of bitstreams: 1 2016_JaneAdrianaSouza.pdf: 1805634 bytes, checksum: be4f8c819c44b36ff04c685db1485101 (MD5)<br>Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2017-03-17T17:00:26Z (GMT) No. of bitstreams: 1 2016_JaneAdrianaSouza.pdf: 1805634 bytes, checksum: be4f8c819c44b36ff04c685db1485101 (MD5)<br>Made available in DSpace on 2017-03-17T17:00:26Z (GMT). No. of bitstreams: 1 2016_JaneAdrianaSouza.pdf: 1805634 bytes, checksum: be4f8c819c44b36ff04c685db1485101 (MD5)<br>Nos últimos anos, novos modelos de banco de dados, chamados NoSQL (Not Only SQL) estão sendo considerados alternativas para a gestão de grandes volumes de dados - Big Data, pois gerenciam e armazenam os dados de forma eficiente, possuem alta escalabilidade, disponibilidade e desempenho satisfatório. A administração de bancos de dados implica na execução de tarefas, tais como criação de bases e objetos, atribuição de privilégios, realização de backups, dentre outras atividades. A execução dessas tarefas de administração em bancos de dados NoSQL exige um maior nível de conhecimento por parte dos administradores de bancos de dados (DBA), e expõe questões relacionadas à falta de familiaridade desses profissionais nos ambientes NoSQL. De forma a contribuir nesse campo de estudo, a presente dissertação apresenta a solução NoSQL2 para execução de tarefas de administração, usando a linguagem SQL (Structured Query Language), que funciona em diferentes Sistemas Gerenciadores de Bancos de Dados (SGBD) NoSQL. O NoSQL2 permite aos DBAs se desvincularem das particularidades de formas de acesso de cada NoSQL, pois disponibiliza recursos para conversão de comandos da sintaxe SQL para a sintaxe proprietária do banco de dados NoSQL.<br>New database models, called NoSQL (Not Only SQL) are considered appropriate alternatives for managing and storing Big Data due to their efficiency, high scalability, availability and performance. Database administration effects tasks such as creating databases and objects, attributing priorities and performing backups. The execution of these tasks, in NoSQL databases, require that DBA (database administrators) have a high level of knowledge, and often exposes problems with the DBA unfamiliarity of the NoSQL environments. In order to contribute to the scholarship in this field, this paper presents the middleware NoSQL2 to perform management tasks using the SQL language, which runs on different NoSQL databases. The NoSQL2 allows DBA to disassociate themselves from the particularities of access forms of each NoSQL, since provides resources for converting SQL commands to the proprietary NoSQL database syntax.

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Erven, Gustavo Cordeiro Galvão van. "MDG-NoSQL : modelo de dados para bancos NoSQL baseados em grafos." reponame:Repositório Institucional da UnB, 2015. http://dx.doi.org/10.26512/2015.03.D.18992.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.<br>Submitted by Raquel Viana (raquelviana@bce.unb.br) on 2015-11-03T18:43:12Z No. of bitstreams: 1 2015_GustavoCordeiroGalvãoVanErven.pdf: 2937303 bytes, checksum: afdc1b126b59eacc4b9a3b62bc412d1b (MD5)<br>Approved for entry into archive by Marília Freitas(marilia@bce.unb.br) on 2015-12-20T16:23:49Z (GMT) No. of bitstreams: 1 2015_GustavoCordeiroGalvãoVanErven.pdf: 2937303 bytes, checksum: afdc1b126b59eacc4b9a3b62bc412d1b (MD5)<br>Made available in DSpace on 2015-12-20T16:23:49Z (GMT). No. of bitstreams: 1 2015_GustavoCordeiroGalvãoVanErven.pdf: 2937303 bytes, checksum: afdc1b126b59eacc4b9a3b62bc412d1b (MD5)<br>Os bancos de dados em grafo vêm se tornando populares juntamente com as demais iniciativas NoSQL. Porém, os bancos de dados em grafos não possuem uma notação padrão. Sendo assim, o presente trabalho agrupa diversas notações e propostas de modelagem em grafos, construindo um novo modelo, chamado de Modelo de Dados para Bancos NoSQL Baseados em Grafos (MDG-NoSQL), com recursos para agrupar em uma notação as características dos bancos de dados em grafos. Esse modelo foi validado utilizando a implementação de um banco de dados de vínculos societários de empresas, combinados com os relacionamentos dessas pessoas jurídicas com os processos de compras, também chamadas de licitações, junto ao Governo Federal (Banco de Vínculos Simpli_cado para Licitações e Sociedades). Esse estudo de caso foi direcionado para auxiliar na detecção de fraudes em processos licitatórios que, além de ser diretamente aplicável a vários órgãos de controle, perícia e inteligência em âmbito nacional, permite extrair fundamentos extens íveis a outros problemas com modelagens de vínculos.<br>Currently, Graph Databases are becoming popular along with other NoSQL initiatives. Thus, researchers and companies have been developing data models to manipulate information as graphs However, there is no standard notation involves several features and structures of the graph databases. This model introduces several notations and concepts for building a new graph data model, called Data Model for NoSQL Graph Databases (GDM-NoSQL). The GDM-NoSQL was veri_ed through a database for investigate relationships between companies and people as well as information about the procurements that these companies participated in with the Federal Government. This database was designed to facilitate the process of searching for frauds and to be used on multiple contexts, i.e. several di_erent national agencies. Finally, the designed model was implemented in both a relational and a graph database in order to validate the hypothesis that writing relationships is simpler and more e_cient in graph models than relational databases.

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Santos, Nuno Filipe Vieira dos. "Context storage using NoSQL." Master's thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7586.

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Mestrado em Engenharia de Computadores e Telemática<br>This thesis proposes the usage of an upcoming new breed of databases, called NoSQL, for the storage and management of context information, in a context management architecture based on XCoA (XMPP-based Context Architecture). In this context management architecture all context information is published on a central component called Context Broker, and stored for further requisition, processing and analysis. Due to the high number of published context data, traditional databases quickly develop serious performance limitations, which in turn may jeopardize the XMPP protocol’s efficiency; alternatively, NoSQL databases show serious performance improvements in the storage and management of large-scale data sets. This thesis presents a study of the several available NoSQL solutions, and presents the advantages and disadvantages of the usage of a specific solution in this type of problem.<br>O presente trabalho propõe o uso de uma nova geração de bases de dados, denominadas NoSQL, para o armazenamento e gestão de informação de contexto, assente numa arquitectura de gestão contexto baseada em XCoA (XMPP-based Context Architecture). Nesta arquitectura de gestão de contexto toda a informação de contexto é publicada num componente central, denominado Context Broker, e armazenada para posterior requisição, processamento e análise. Dado o elevado volume de informação de contexto publicada, as bases de dados tradicionais rapidamente apresentam limitações de desempenho, que por sua vez poem em causa a eficiência do funcionamento do protocolo XMPP; pelo contrário, as bases de dados NoSQL apresentam serias melhorias de desempenho na gestão de bases de dados de grande dimensão. Este trabalho estuda várias soluções NoSQL existentes, e apresenta as vantagens e desvantagens do uso de uma solução específica neste tipo de problema.

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Bergquist, Jan-Halvard, Fredrik Lindgren, and Linda Nguyen. "NoSQL! : Med rätt att finnas?" Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-42574.

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Заворотна, М. Г. "Высокопроизводительные нереляционные базы данных NOSQL". Thesis, Kharkiv National University of Radio Electronics, 2018. http://openarchive.nure.ua/handle/document/7846.

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The current relational database management systems are debugged mechanisms created for storing data and building queries to them. Since the creation of the data, the systems have become more sophisticated in terms of security and operability. As progress does not stand still, the amount of useful information in the world is steadily increasing, remaining, or becoming impossible. To solve these tasks and process the flow, structured and unstructured data of huge data, NoSQL approaches were created for the implementation of database stores.

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Persson, Ragnvald. "NoSQL-databaser i socialt nätverk." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20692.

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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.<br>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.

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Hesselryd, Jonas. "En utredning av NoSQL för iipax." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70154.

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NoSQL är ett omtalat ämne just nu. Det finns mycket som talar för att det ska lösa de problem relationsdatabaser lider av. Exempelvis onödigt resurskräavande system eller svårt att konvertera mellan olika format på data. Att lösa dessa problem är något Ida Infront är intresserade av för lagringen i deras ärendehanteringsplattform iipax. Uppgiften är att ta reda på vad NoSQL-begreppet faktiskt innebär och utvärdera utvalda databaser mot Ida Infront och iipax krav. Problemet har angripits genom en litteraturstudie av NoSQL för att sedan undersöka tre databaser: Neo4J, CouchDB och Cassandra. Implementationerna har undersökts för att ge en bättre bild av vad NoSQL innebär i praktiken. Resultatet av arbetet är att NoSQL är ett väldigt diffust begrepp där många är oense om vad som gäller. Det är några olika typer av databaser som räknas till NoSQL men de i sig är ingen definition av begreppet. Olika typer som ofta nämns är dokument, graf och kolumndatabaser. När det kommer till de specifika databaserna ser de ut att ha spännande egenskaper som kan passa iipax, till exempel bra datamodell eller stöd för fulltextindexering. Slutligen kan det sägas att Neo4J i dagsläget ser ut som den bästakandidaten för lagringen i iipax.

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Björklund, Anna. "NoSQL Database for Software Project Data." Thesis, Umeå universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-43838.

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The field of databases have exploded in the last couple of years. New architectures try to meet the need to store more and more data and new kinds of data. The old relational model is no longer the only way and the NoSQL movement is not a trend but a new way of making the database fit the data, not the other way around.This master thesis report aims to find an efficient and well designed solution for storing and retrieving huge amounts of software project data at Tieto. It starts by looking at different architectures and trying three to see if any of them can solve the problem. The three databases selected are the relational database PostgreSQL, the graph database Neo4j and the key value store Berkeley DB. These are all implemented as a Web service and time is measured to find out which, if any, can handle the data at Tieto. In the end it is clear that the best database for Tieto is Berkeley DB. Even if Neo4j is almost as fast, it is still new and not as mature as Berkeley DB.

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Панферова, И. Ю. "Модели построения мобильной NOSQL базы данных". Thesis, НТМТ, 2015. http://openarchive.nure.ua/handle/document/8318.

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Mendoza, Jayo Rubén Gilmar, and Castro José Gabriel Medina. "NoSQL: futuro de almacenamiento de datos." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2015. http://hdl.handle.net/10757/552364.

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The current existence of a behavior or prejudice that indicates that the non-relational model-oriented databases are not competitive compared to the relational model, associated with the lack of interest or lack of education for it professionals on the benefits of NoSQL solutions, and, because, considering that the non-relational model-oriented databases systems just are not in a phase of initiation, they have generated the dilemma that produces is research. The research project seeks to generate a comparative analysis between the relational data model and relational enabling to contribute to a point of view concerning the potential of the NoSQL. This triggered the generation of the proposed migration procedure relational data model to the non-relational and from that reveal the management involved in the development of prototype migration tool based on an experimental architecture outlined in the proposal (RoboSQL) and Finally, benchmarking resulting response time and scalability. The contribution becomes the comparative analysis of data models, which allows a useful reference for translation into relational terminology to non-relational by determining equivalences and differences. This was from the standard properties in data models: structure, constraints and operations. Comparing indicators response time, through a case by case scenarios, reveal that a NoSQL, in most contexts, it is more optimal for their inherent characteristics and performance that has on a given environment. On the side of scalability, evidenced by the variances of data loading and hardware, turned out to be outstanding product NoSQL to their relational counterparts.<br>La actual existencia de un comportamiento o prejuicio que indica que las bases de datos orientadas al modelo no relacional no son competitivas frente al modelo relacional, así como el desinterés o la carencia de educación de los profesionales de tecnologías de la información (TI) en cuanto a los beneficios de las soluciones NoSQL han generado el dilema que produce está investigación. A esto se le suma el considerar que los sistemas de bases de datos orientados al modelo no relacional recién se encuentran en una fase de iniciación. El proyecto de investigación busca generar un análisis comparativo entre el modelo de datos relacional y no relacional que permita contribuir a engrandecer un punto de vista relacionado a las potencialidades de las NoSQL. Se hizo la propuesta de procedimiento de migración del modelo de datos relacional hacia el no relacional y a partir de este revelar la gestión involucrada en el desarrollo de la herramienta prototipo de migración (RoboSQL) basada en una arquitectura experimental expuesta en la propuesta y, finalmente, el benchmarking resultante de tiempo de respuesta y escalabilidad. El aporte se constituye en el análisis comparativo de modelos de datos, el cual permite tener una referencia útil para la traslación de terminología relacional hacia la no relacional a través de la determinación de equivalencias y diferencias. Ello fue a partir de las propiedades estándares en los modelos de datos: estructura, restricciones y operaciones. Los indicadores de comparación de tiempo de respuesta, a través de escenarios en una casuística, revelan que una NoSQL, en la mayoría de contextos, es más óptima por sus características inherentes y el desempeño que tiene sobre un determinado ambiente. Por el lado de la escalabilidad, evidenciada a través de las varianzas de carga de datos y hardware, resultó ser sobresaliente el producto NoSQL ante su contraparte relacional.<br>Tesis

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Books on the topic "NoSQL"

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.

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Pivert, Olivier, ed. NoSQL Data Models. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119528227.

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Meier, Andreas, and Michael Kaufmann. SQL- & NoSQL-Datenbanken. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47664-2.

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Meier, Andreas, and Michael Kaufmann. SQL & NoSQL Databases. Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24549-8.

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Kaufmann, Michael, and Andreas Meier. SQL and NoSQL Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27908-9.

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Kaufmann, Michael, and Andreas Meier. SQL- & NoSQL-Datenbanken. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67092-7.

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Hollosi, Arno. Von Geodaten bis NoSQL: Leistungsstarke PHP-Anwendungen. Carl Hanser Verlag GmbH & Co. KG, 2012. http://dx.doi.org/10.3139/9783446431225.

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jun, Pi xiong. NoSQL shu ju ku ji shu shi zhan. Qing hua ta xue chu ban she, 2015.

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Carter, Jacqueline, ed. Seven Databases in Seven Weeks: A guide to modern databases and the NoSQL movement. Pragmatic Bookshelf, 2012.

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Meier, Andreas. Werkzeuge der digitalen Wirtschaft: Big Data, NoSQL & Co. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20337-5.

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Book chapters on the topic "NoSQL"

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Einführung." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.001.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "NoSQL – Theoretische Grundlagen." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.002.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Wide Column Stores." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.003.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Document Stores." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.004.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Key/Value-Datenbanken." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.005.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Graphdatenbanken." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.006.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "OrientDB." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.007.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Weitere NoSQL-Datenbanken." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.008.

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Edlich, Stefan, Achim Friedland, Jens Hampe, Benjamin Brauer, and Markus Brückner. "Orientierung im Datenbankraum." In NoSQL. Carl Hanser Verlag GmbH & Co. KG, 2011. http://dx.doi.org/10.3139/9783446428553.009.

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Edward, Shakuntala Gupta, and Navin Sabharwal. "NoSQL." In Practical MongoDB. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0647-8_2.

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Conference papers on the topic "NoSQL"

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Pokorny, Jaroslav. "NoSQL databases." In the 13th International Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2095536.2095583.

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Abramova, Veronika, and Jorge Bernardino. "NoSQL databases." In the International C* Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2494444.2494447.

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Matos, David, and Miguel Correia. "NoSQL Undo: Recovering NoSQL databases by undoing operations." In 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA). IEEE, 2016. http://dx.doi.org/10.1109/nca.2016.7778616.

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Thomas Mason, Robert. "NoSQL Databases and Data Modeling for a Document-oriented NoSQL Database." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2245.

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NoSQL databases are an important component of Big Data for storing and retrieving large volumes of data. Traditional Relational Database Management Systems (RDBMS) use the ACID theorem for data consistency, whereas NoSQL Databases use a non-transactional approach called BASE. RDBMS scale vertically and NoSQL Databases can scale both horizontally (sharding) and vertically. Four types of NoSQL databases are Document-oriented, Key-Value Pairs, Column-oriented and Graph. Data modeling for Document-oriented databases is similar to data modeling for traditional RDBMS during the conceptual and logical modeling phases. However, for a physical data model, entities can be combined (denormalized) by using embedding. What was once called a foreign key in a traditional RDBMS is now called a reference in a Document-oriented NoSQL database.

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Yassien, Amal W., and Amr F. Desouky. "RDBMS, NoSQL, Hadoop." In the 2nd Africa and Middle East Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2944165.2944174.

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Tsai, Chia-Ping, and Hung-Chang Hsiao. "Streaming in NoSQL." In 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2014. http://dx.doi.org/10.1109/padsw.2014.7097898.

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Schram, Aaron, and Kenneth M. Anderson. "MySQL to NoSQL." In the 3rd annual conference. ACM Press, 2012. http://dx.doi.org/10.1145/2384716.2384773.

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Dehdouh, Khaled, Fadila Bentayeb, Omar Boussaid, and Nadia Kabachi. "Columnar NoSQL CUBE: Agregation operator for columnar NoSQL data warehouse." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974527.

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Ferrari, Dario, Michele Carminati, Mario Polino, and Stefano Zanero. "NoSQL Breakdown: A Large-scale Analysis of Misconfigured NoSQL Services." In ACSAC '20: Annual Computer Security Applications Conference. ACM, 2020. http://dx.doi.org/10.1145/3427228.3427260.

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Tsai, Chia-Ping, Kuo-Yu Yang, Jui-Yu Hsieh, Hung-Chang Hsiao, and Ching-Hsien Hsu. "Publish/Subscribe in NoSQL." In 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE). IEEE, 2014. http://dx.doi.org/10.1109/cse.2014.227.

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Reports on the topic "NoSQL"

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Klein, John, Patrick Donohoe, Neil Ernst, Ian Gorton, Kim Pham, and Chrisjan Matser. NoSQL Data Store Technologies. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada611676.

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Hu, Vincent C. Access Control for NoSQL Databases. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ir.8504.ipd.

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Hu, Vincent. Access Control on NoSQL Databases. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ir.8504.

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Klein, John, Ian Gorton, Neil Ernst, Patrick Donohoe, Kim Pham, and Chrisjan Matser. Quality Attribute-Guided Evaluation of NoSQL Databases: An Experience Report. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613928.

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Klein, John, Ian Gorton, Neil Ernst, Patrick Donohoe, Kim Pham, and Chrisjan Matser. Quality Attribute-Guided Evaluation of NoSQL Databases: A Case Study. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada614284.

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