Relevant bibliographies by topics / NosSQL database

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'NosSQL database'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "NosSQL database"

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Babić, Andrea, Danijela Jakšić, and Patrizia Poščić. "Queryng data in NoSQL databases." Zbornik Veleučilišta u Rijeci 7, no. 1 (2019): 257–70. http://dx.doi.org/10.31784/zvr.7.1.9.

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The goal of this paper is to give an overview of fundamental concepts and types of NoSQL databases, to show some examples of database queries, some related research, and the implementation of those queries in an original practical example. The introduction is a brief representation and description of the NoSQL database. There are also several comparisons of NoSQL database with the relational database. The next chapter contains a review of the basic NoSQL databases and their prototypes. In each of the following subchapters, the types of NoSQL databases are described in more detail and various queries which can be performed over them are presented. In the last chapter there is also a practical example of querying one of these databases.
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Trumboo, Owais Noor, and Jasra Nisar. "Traditional Databases vs NOSQL." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 68–70. http://dx.doi.org/10.31142/ijtsrd12961.

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Krstić, Lazar, and Marija Krstić. "Testing the performance of NoSQL databases via the database benchmark tool." Vojnotehnicki glasnik 66, no. 3 (2018): 614–39. http://dx.doi.org/10.5937/vojtehg66-15928.

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Nisa, Behjat U. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 845–48. http://dx.doi.org/10.31142/ijtsrd11214.

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Ahmad, Khaleel, Mohammad Shoaib Alam, and Nur Izura Udzir. "Security of NoSQL Database Against Intruders." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 5–12. http://dx.doi.org/10.2174/1872212112666180731114714.

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Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.
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Kunda, Douglas, and Hazael Phiri. "A Comparative Study of NoSQL and Relational Database." Zambia ICT Journal 1, no. 1 (December 11, 2017): 1–4. http://dx.doi.org/10.33260/zictjournal.v1i1.8.

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Relational Database and NoSQL are competing types of database models. The former has been in existence since 1979 and the latter since the year 2000. The demands of modern applications especially in web 2.0, 3.0 and big data have made NoSQL a popular database of choice. Choosing an appropriate database model to use is an important decision that developers must make based on the features of a given database model. This paper compares the features of Relational Databases and NoSQL to establish which database is better at supporting demands of modern applications. The paper also brings out the challenges of NoSQL. Finally, the paper concludes by determining whether Relational Databases would completely be replaced by NoSQL database models. The findings revealed that, Relational Databases are based on ACID model which emphasizes better consistency, security and offers a standard query language. However, Relational Databases have poor scalability, weak performance, cost more, face availability challenges when supporting large number of users and handle limited volume of data. NoSQL, on the other hand is based on the BASE model, which emphasizes greater scalability and provides a flexible schema, offers better performance, mostly open source, cheap but, lacks a standard query language and does not provide adequate security mechanisms. Both databases will continue to exist alongside each other with none being better than the other. The choice of the database to use will depend on the nature of the application being developed. Each database type has its own challenges and strengths, with relational database lacking of support for unstructured data while NoSQL lacks standardization and has poor security. Modern applications in web 2.0, 3.0 and big data are well suited to use NoSQL but, there are still many applications that rely on Relational Databases.
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Yasmeen, Mrs. "NOSQL Database Engines for Big Data Management." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 617–22. http://dx.doi.org/10.31142/ijtsrd18608.

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Lourenço, João Ricardo, Bruno Cabral, Jorge Bernardino, and Marco Vieira. "Comparing NoSQL Databases with a Relational Database: Performance and Space." Services Transactions on Big Data 2, no. 1 (September 2015): 1–14. http://dx.doi.org/10.29268/stbd.2015.2.1.1.

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Alotaibi, Obaid, and Eric Pardede. "Transformation of Schema from Relational Database (RDB) to NoSQL Databases." Data 4, no. 4 (November 27, 2019): 148. http://dx.doi.org/10.3390/data4040148.

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Relational database has been the de-facto database choice in most IT applications. In the last decade there has been increasing demand for applications that have to deal with massive and un-normalized data. To satisfy the demand, there is a big shift to use more relaxed databases in the form of NoSQL databases. Alongside with this shift, there is a need to have a structured methodology to transform existing data in relational database (RDB) to NoSQL database. The transformation from RDB to NoSQL database has become more challenging because there is no current standard on NoSQL database. The aim of this paper is to propose transformation rules of RDB Schema to various NoSQL database schema, namely document-based, column-based and graph-based databases. The rules are applied based on the type of relationships that can appear in data within a database. As a proof of concept, we apply the rules into a case study using three NoSQL databases, namely MongoDB, Cassandra, and Neo4j. A set of queries is run in these databases to demonstrate the correctness of the transformation results. In addition, the completeness of our transformation rules are compared against existing work.
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Pokorny, Jaroslav. "NoSQL databases: a step to database scalability in web environment." International Journal of Web Information Systems 9, no. 1 (March 29, 2013): 69–82. http://dx.doi.org/10.1108/17440081311316398.

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Dissertations / Theses on the topic "NosSQL database"

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Ravu, Venkata Sathya Sita J. S. "Compaction Strategies in Apache Cassandra : Analysis of Default Cassandra stress model." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12850.

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Context. The present trend in a large variety of applications are ranging from the web and social networking to telecommunications, is to gather and process very large and fast growing amounts of information leading to a common set of problems known collectively as “Big Data”. The ability to process large scale data analytics over large number of data sets in the last decade proved to be a competitive advantage in a wide range of industries like retail, telecom and defense etc. In response to this trend, the research community and the IT industry have proposed a number of platforms to facilitate large scale data analytics. Such platforms include a new class of databases, often refer to as NoSQL data stores. Apache Cassandra is a type of NoSQL data store. This research is focused on analyzing the performance of different compaction strategies in different use cases for default Cassandra stress model. Objectives. The performance of compaction strategies are observed in various scenarios on the basis of three use cases, Write heavy- 90/10, Read heavy- 10/90 and Balanced- 50/50. For a default Cassandra stress model, so as to finally provide the necessary events and specifications that suggest when to switch from one compaction strategy to another. Methods. Cassandra single node network is deployed on a web server and its behavior of read and write performance with different compaction strategies is studied with read heavy, write heavy and balanced workloads. Its performance metrics are collected and analyzed. Results. Performance metrics of different compaction strategies are evaluated and analyzed. Conclusions. With a detailed analysis and logical comparison, we finally conclude that Level Tiered Compaction Strategy performs better for a read heavy (10/90) workload while using default Cassandra stress model , as compared to size tiered compaction and date tiered compaction strategies. And for Balanced Date tiered compaction strategy performs better than size tiered compaction strategy and date tiered compaction strategy.
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Klapač, Milan. "Výhody a nevýhody relačních a nerelačních (noSQL) databází pro analytické úlohy." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193931.

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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.
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Moatassem, Nawal N. "A Study of Migrating Biological Data from Relational Databases to NoSQL Databases." Youngstown State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1442486094.

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Günzl, Richard. "Aplikace grafové databáze na analytické úlohy." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193342.

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This diploma thesis is about graph databases, which belong to the category of database systems known as NoSQL databases, but graph databases are beyond NoSQL databases. Graph databases are useful in many cases thanks to native storing of interconnections between data, which brings advantageous properties in comparison with traditional relational database system, especially in querying. The main goal of the thesis is: to describe principles, properties and advantages of graph database; to design own convenient graph database use case and to realize the template verifying designed use case. The theoretical part focuses on the description of properties and principles of the graph database which are then compared with relational database approach. Next part dedicates analysis and explanation of the most typical use cases of the graph database including the unsuitable use cases. The last part of thesis contains analysis of own graph database use case in which several principles are defined. The principles can be applied separately. There are crucial analytical operations in the use case. The analytical operations search the causes with their rate of influence on amount or change in the value of the indicator. This part also includes the realization of the template verifying the use case in the graph database. The template consists of the database structure design, the concrete database data and analytical operations. In the end the returned results from graph database are verified by the alternative calculations without using the graph database.
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Ekberg, Albin, and Jacob Holm. "Effektiv och underhållssäker lagring av medicinsk data." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-107331.

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Creating a database to manage medical data is not the easiest. We create a database to be used for a presentation tool that presents medical data about patients that is stored in the database. We examine which of the three databases, MySQL with relational design, MySQL with EAV design and MongoDB that are best suited for storing medical data. The analysis i performed in two steps. The first step handles the database that is most effective to retriev data. The second step examines how easy it is to change the structure of the various databases. The results show that depending on whether efficiency or maintenance is most important, different databases are the best choise. MySQL with relational design proves to be most effective while MongoDB is the easiest to maintain.
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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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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.
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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Mendoza, Jayo Rubén G., Carlos Raymundo, Francisco Domínguez Mateos, and Rodríguez José María Alvarez. "Convergence model for the migration of a relational database to a NoSQL database." International Institute of Informatics and Systemics, IIIS, 2017. http://hdl.handle.net/10757/656362.

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Pretorius, Dawid Johannes. "NoSQL database considerations and implications for businesses." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85727.

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Thesis (MComm)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: NoSQL databases, a new way of storing and retrieving data, can provide businesses with many benefits, although they also pose many risks for businesses. The lack of knowledge among decision-makers of businesses regarding NoSQL databases can lead to risks left unaddressed and missed opportunities. This study, by means of an extensive literature review, identifies the key drivers, characteristics and benefits of a NoSQL database, thereby providing a clear understanding of the subject. The business imperatives related to NoSQL databases are also identified and discussed. This can help businesses to determine whether a NoSQL database might be a viable solution, and to align business and information technology (IT) objectives. The key strategic and operational IT risks are also identified and discussed, based on the literature review. This can help business to ensure that the risks related to the use of NoSQL databases are appropriately addressed. Lastly, the identified risks were mapped to the processes of COBIT (Control Objectives for Information and Related Technology) to inform a business of the highest risk areas and the associated focus areas.
AFRIKAANSE OPSOMMING: NoSQL databasisse, 'n nuwe manier om data te stoor en herwin, het die potensiaal om baie voordele vir besighede in te hou, maar kan ook baie risiko's teweeg bring. Gebrekkige kennis onder besigheidsbesluitnemers oor NoSQL databasisse kan lei tot onaangespreekte risiko’s en verlore geleenthede. Hierdie studie, deur middel van 'n uitgebreide literatuuroorsig, identifiseer die sleutel eienskappe, kenmerke en voordele van 'n NoSQL databasis, om sodoende 'n duidelike begrip van die onderwerp te verkry. Die besigheidsimperatiewe wat verband hou met NoSQL databasisse is ook geïdentifiseer en bespreek. Dit kan besighede help om te bepaal of 'n NoSQL databasis 'n werkbare oplossing kan wees, asook sake- en inligtingstegnologie (IT) doelwitte in lyn met mekaar bring. Na aanleiding van die literatuurstudie is die sleutel-strategiese en operasionele IT-risiko's geïdentifiseer en bespreek. Dit kan help om aan besighede sekerheid te verskaf dat die risiko's wat verband hou met die gebruik van NoSQL databasisse toepaslik aangespreek word. Laastens is die geïdentifiseerde risiko's gekoppel aan die prosesse van COBIT om 'n besigheid van die hoë-risiko areas en die gepaardgaande fokusareas in te lig.
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Vizina, Petr. "NoSQL databáze pro data senzorů s podporou časových řad." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363797.

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This thesis deals with NoSQL databases, which can be used for effective storage of sensors data with character of time series. The aim is to design and implement own solution for database designed to store time series data, with usage of NoSQL.
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Books on the topic "NosSQL database"

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

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Edlich, Stefan. NoSQL: Einstieg in die Welt nichtrelationaler Web-2.0-Datenbanken. München: Hanser, 2010.

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1963-, Fowler Martin, ed. NoSQL distilled: A brief guide to the emerging world of polyglot persistence. Upper Saddle River, NJ: Addison-Wesley, 2013.

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

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Peter, Membrey, Hawkins Tim, and SpringerLink (Online service), eds. The Definitive Guide to MongoDB: The NoSQL Database for Cloud and Desktop Computing. Berkeley, CA: Eelco Plugge, Tim Hawkins, Peter Membrey, 2010.

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Mastering Apache Cassandra: Build, manage, and configure high-performing, reliable NoSQL database for your application with Cassandra. Birmingham, England: Packt Publishing, 2015.

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Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement. Pragmatic Bookshelf, 2018.

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Robinson, Ian. Graph Dabases. O'Reilly, 2013.

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Fowler, Adam. NoSQL For Dummies. For Dummies, 2015.

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Fowler, Adam. NoSQL for Dummies. Wiley & Sons, Incorporated, John, 2015.

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Book chapters on the topic "NosSQL database"

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Adriana, Jane, and Maristela Holanda. "NoSQL2: SQL to NoSQL Databases." In Advances in Intelligent Systems and Computing, 938–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_89.

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Meier, Andreas, and Michael Kaufmann. "NoSQL Databases." In SQL & NoSQL Databases, 201–18. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24549-8_7.

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Wiktorski, Tomasz. "NOSQL Databases." In Advanced Information and Knowledge Processing, 77–84. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04603-3_8.

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Cutler, Josh, and Matt Dickenson. "NoSQL Databases." In Textbooks on Political Analysis, 117–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36826-5_9.

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Lake, Peter, and Paul Crowther. "NoSQL Databases." In Undergraduate Topics in Computer Science, 97–134. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5601-7_5.

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Domdouzis, Konstantinos, Peter Lake, and Paul Crowther. "NoSQL Databases." In Undergraduate Topics in Computer Science, 101–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42224-0_5.

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Meier, Andreas, and Michael Kaufmann. "Database Languages." In SQL & NoSQL Databases, 85–121. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24549-8_3.

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Sakr, Sherif. "NoSQL Database Systems." In Encyclopedia of Big Data Technologies, 1193–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_50.

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Sakr, Sherif. "NoSQL Database Systems." In Encyclopedia of Big Data Technologies, 1–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_50-2.

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Sakr, Sherif. "NoSQL Database Systems." In Encyclopedia of Big Data Technologies, 1–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_50-1.

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Conference papers on the topic "NosSQL database"

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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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Frozza, Angelo Augusto, Eduardo Dias Defreyn, and Ronaldo Dos Santos Mello. "A Process for Inference of Columnar NoSQL Database Schemas." In XXXV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbbd.2020.13637.

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Although NoSQL Databases do not require a schema a priori, to be aware of the database schema is essential for activities like data integration, data validation or data interoperability. This paper presents a process for inference of columnar NoSQL DB schemas. We validate the proposed process through a prototype tool that is able to extract schemas from the HBase columnar NoSQL database system. HBase was chosen as a case study because it is one of the most popular columnar NoSQL solutions. When compared to related work, we novel by proposing a simple solution for the inference of column data types for columnar NoSQL databases that store only byte arrays as column values, as well as a generated schema that follows the JSON Schema format.
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Abramova, Veronika, and Jorge Bernardino. "NoSQL databases." In the International C* Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2494444.2494447.

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

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Jing Han, Haihong E, Guan Le, and Jian Du. "Survey on NoSQL database." In 2011 6th International Conference on Pervasive Computing and Applications (ICPCA). IEEE, 2011. http://dx.doi.org/10.1109/icpca.2011.6106531.

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Lopes, Ingrid Santana, and Maristela Holanda. "Biological database in NoSQL document-oriented database." In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2018. http://dx.doi.org/10.23919/cisti.2018.8399168.

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Xiao, Zhifeng, and Yimin Liu. "Remote sensing image database based on NOSQL database." In 2011 19th International Conference on Geoinformatics. IEEE, 2011. http://dx.doi.org/10.1109/geoinformatics.2011.5980724.

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Gu, Yunhua, Shu Shen, Jin Wang, and Jeong-Uk Kim. "Application of NoSQL database MongoDB." In 2015 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2015. http://dx.doi.org/10.1109/icce-tw.2015.7216831.

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Srivastava, Pragati Prakash, Saumya Goyal, and Anil Kumar. "Analysis of various NoSql database." In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2015. http://dx.doi.org/10.1109/icgciot.2015.7380523.

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Li, Junshan, and Jianjun Li. "Research on NoSQL Database Technology." In 2018 2nd International Conference on Management, Education and Social Science (ICMESS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icmess-18.2018.252.

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Reports on the topic "NosSQL database"

1

Klein, John, Ian Gorton, Neil Ernst, Patrick Donohoe, Kim Pham, and Chrisjan Matser. Quality Attribute-Guided Evaluation of NoSQL Databases: An Experience Report. Fort Belvoir, VA: Defense Technical Information Center, October 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. Fort Belvoir, VA: Defense Technical Information Center, January 2015. http://dx.doi.org/10.21236/ada614284.

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