Relevant bibliographies by topics / SQL-to-NoSQL

Contents

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

Academic literature on the topic 'SQL-to-NoSQL'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "SQL-to-NoSQL"

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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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Arif, Dashne Raouf, and Nzar Abdulqadir Ali. "Improving the performance of big data databases." Kurdistan Journal of Applied Research 4, no. 2 (2019): 206–20. http://dx.doi.org/10.24017/science.2019.2.20.

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Real-time monitoring systems utilize two types of database, they are relational databases such as MySQL and non-relational databases such as MongoDB. A relational database management system (RDBMS) stores data in a structured format using rows and columns. It is relational because the values of the tables are connected. A non-relational database is a database that does not adopt the relational structure given by traditional. In recent years, this class of databases has also been referred to as Not only SQL (NoSQL). This paper discusses many comparisons that have been conducted on the execution time performance of types of databases (SQL and NoSQL). In SQL (Structured Query Language) databases different algorithms are used for inserting and updating data, such as indexing, bulk insert and multiple updating. However, in NoSQL different algorithms are used for inserting and updating operations such as default-indexing, batch insert, multiple updating and pipeline aggregation. As a result, firstly compared with related papers, this paper shows that the performance of both SQL and NoSQL can be improved. Secondly, performance can be dramatically improved for inserting and updating operations in the NoSQL database compared to the SQL database. To demonstrate the performance of the different algorithms for entering and updating data in SQL and NoSQL, this paper focuses on a different number of data sets and different performance results. The SQL part of the paper is conducted on 50,000 records to 3,000,000 records, while the NoSQL part of the paper is conducted on 50,000 to 16,000,000 documents (2GB) for NoSQL. In SQL, three million records are inserted within 606.53 seconds, while in NoSQL this number of documents is inserted within 67.87 seconds. For updating data, in SQL 300,000 records are updated within 271.17 seconds, while for NoSQL this number of documents is updated within just 46.02 seconds.
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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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Sokolova, Marina V., Francisco J. Gómez, and Larisa N. Borisoglebskaya. "Migration from an SQL to a hybrid SQL/NoSQL data model." Journal of Management Analytics 7, no. 1 (2019): 1–11. http://dx.doi.org/10.1080/23270012.2019.1700401.

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Dai, Jiao. "SQL to NoSQL : What to do and How." IOP Conference Series: Earth and Environmental Science 234 (March 8, 2019): 012080. http://dx.doi.org/10.1088/1755-1315/234/1/012080.

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Schreiner, Geomar A., Denio Duarte, and Ronaldo dos S. Melo. "When Relational-Based Applications Go to NoSQL Databases: A Survey." Information 10, no. 7 (2019): 241. http://dx.doi.org/10.3390/info10070241.

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Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution.
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Wang, Peng, and Yan Qi. "Research of Load Balancing Based on NOSQL Database." Applied Mechanics and Materials 602-605 (August 2014): 3371–74. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3371.

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The NOSQL database to support data and high concurrent read and write,scalability and high availability features in a distributed storage system which has been applied widely. In this paper, through the research of load balancing in distributed storage system,and it proposes the consistent hashing algorithm and the virtual node strategy, in order to improve the load balancing of the system and increase the cache hit ratio. For the load balancing principle of NOSQL and SQL Server, analysis and comparison of the experimental data.The result shows that, with the increase of the number of virtual nodes, the cache hit ratio of NOSQL is higher than the cache hit ratio of SQL Server.
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Bogdan, George Tudorica. "Challenges for the NoSQL systems." International Journal of Sustainable Economies Management 2, no. 1 (2013): 55–64. http://dx.doi.org/10.4018/ijsem.2013010106.

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The concept described by the term NoSQL (Not Only SQL) is a database that is distributed, may not require fixed table schemas, usually avoids join operations and is typically horizontally scalable, it does not offer SQL query interface and is available in most cases as open source - some bibliographic sources use the term to refer to a completely unrelated system. This concept is also assimilated by sources in the academic world as a structured form of storage. The two terms seem not to be entirely equivalent; relational databases, for example, also meet the official definition of data storage structures, but they are somewhat opposite qualities to the concept of NoSQL. The aim of this paper is to discuss the challenges met by the NoSQL solutions and to propose solutions for these challenges.
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Rats, Juris, and Gints Ernestsons. "Clustering and Ranked Search for Enterprise Content Management." International Journal of E-Entrepreneurship and Innovation 4, no. 4 (2013): 20–31. http://dx.doi.org/10.4018/ijeei.2013100102.

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The aim of this work is to understand more closely where the border lies between relational and Not Only Structured Query Language (NoSQL) platform as concerns Enterprise Content Management (ECM) area. Another objective (closely related to the first one) is to specify the conceptual architecture of the distributed ECM system. The authors specify the model of the prototype ECM system and compare two platforms for this model – MS SQL based for the relational platform and Clusterpoint for the NoSQL platform. The results of performance measurements of SQL and NoSQL technologies for Enterprise Content Management specific tasks are presented and analyzed. The viability of NoSQL Document-oriented database solution based on clustering and ranked search is demonstrated. The ways to leverage the improved performance and scalability of the software to better serve the business needs of the Enterprise are discussed. The conceptual architecture of the prototype system is outlined.
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Khashan, Eman, Ali Eldesouky, and Sally Elghamrawy. "An adaptive spark-based framework for querying large-scale NoSQL and relational databases." PLOS ONE 16, no. 8 (2021): e0255562. http://dx.doi.org/10.1371/journal.pone.0255562.

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The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.
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Dissertations / Theses on the topic "SQL-to-NoSQL"

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Brown, Elin. "Datamigration av Content Management Systems (CMS) för Multi-siteapplikationer : En studie på SQL-till-NoSQL migration." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15549.

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Detta arbete undersöker om existerande Multi-siteapplikationer i CMS-systemet WordPress kan uppnå bättre prestanda genom att övergå från WordPress till det nya CMS-systemet Keystone JS genom en datamigration. Denna migrationsprocess utvärderas med ett vetenskapligt experiment, för att undersöka om migrationsprocessen i sig eventuellt kan medföra prestandaproblem, men också kring när en migration är relevant och i slutändan värd att genomföra. Experimentet mäter svarstider för olika databasoperationer av den originella WordPress-applikationen samt den migrerade Keystone JS-applikationen. Resultatet av mätningen visade att den migrerade applikationen kan uppnå upp till 59% förbättrade svarstider för subdomänrendering, vilket bekräftar att Multi-siteapplikationer kan gynnas av en migration till Keystone JS. Migrationsprocessen ansågs heller inte ha någon individuell negativ prestandapåverkan.
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Bodegård, Gustafsson Rebecca. "Consequences of converting a data warehouse based on a STAR-schema to a column-oriented-NoSQL-database." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15794.

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Data warehouses based on the relational model has been a popular technology for many years, because they are very reliable due to their ACID-properties (Atomicity, Consistency, Isolation, and Durability). However, the new demands on databases today due to increasing amounts of data and data structures changing do mean that the relational model might not always be the optimal choice. NoSQL is the name of a group of databases that are less bound by schemas and are therefore more scalable and easier to make changes in. They are also adapted for massive parallel processing and are therefore suited for handling large amounts of data. Out of all of the NoSQL databases column-databases are the most like the relational model since it also consists of tables. This study has therefore converted a relational data warehouse based on a STAR-schema to a column-oriented-NoSQL-database and evaluated the implementation by comparing query-times between the relational data warehouse and the column-oriented-NoSQL-database. Scrambled economical data from a business in Sweden has been used to do the conversion and test it by asking a few usual queries. The results show that the mapping works but the query-time in the NoSQL-database is simnifically longer.
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Monjaras, Alvaro, Enrique Bcndezu, and Carlos Raymundo. "Decision Tree Model to Support the Successful Selection of a Database Engine for Novice Database Administrators." Institute of Electrical and Electronics Engineers Inc, 2019. http://hdl.handle.net/10757/656346.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>There are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database category's purpose is to serve a specific or specifics use cases to perform fast to manage the information when needed, so in this paper, we study and analyze the SQL, NoSQL and In Memory databases to understand their fit uses cases and make performance tests to build a decision tree that can help to take the decision to choose what database category to use to maintain a good performance. The precision of the tests of relational databases was 96.26% in NoSQL databases was 91.83% and finally in IMDBS was 93.87%.
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"DATA MIGRATION FROM STANDARD SQL TO NoSQL." Thesis, 2013. http://hdl.handle.net/10388/ETD-2013-11-1342.

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Currently two major database management systems are in use for dealing with data, the Relational Database Management System (RDBMS) also knows as standard SQL databases and the NoSQL databases. The RDBMS databases deal with structured data and the NoSQL databases with unstructured or semi-structured data. The RDBMS databases have been popular for many years but the NoSQL type is gaining popularity with the introduction of the internet and social media. Data flow from SQL to NoSQL or vice versa is very much possible in the near future due to the growing popularity of the NoSQL databases. The goal of this thesis is to analyze the data structures of the RDBMS and the NoSQL databases and to suggest a Graphical User Interface (GUI) tool that migrates the data from SQL to NoSQL databases. The relational databases have been in use and have dominated the industry for many years. In contrast, the NoSQL databases were introduced with the increased usage of the internet, social media, and cloud computing. The traditional relational databases guarantee data integrity whereas high availability and scalability are the main advantages of the NoSQL databases. This thesis presents a comparison of these two technologies. It compares the data structure and data storing techniques of the two technologies. The SQL databases store data differently as compared to the NoSQL databases due to their specific demands. The data stored in the relational databases is highly structured and normalized in most environments whereas the data in the NoSQL databases are mostly unstructured. This difference of the data structure helps in meeting the specific demands of these two systems. The NoSQL DBs are scalable with high availability due to the simpler data model but does not guarantee data consistency at all times. On the other hand the RDBMS systems are not easily scalable and available at the same time due to the complex data model but guarantees data consistency. This thesis uses CouchDB and MySQL to represent the NoSQL and standard SQL databases respectively. The aim of the iii research in this document is to suggest a methodology for data migration from the RDBMS databases to the document-based NoSQL databases. Data migration between the RDBMS and the NoSQL systems is anticipated because both systems are currently in use by many industry leaders. This thesis presents a Graphical User Interface as a starting point that enables the data migration from the RDBMS to the NoSQL databases. MySQL and CouchDB are used as the test databases for the relational and NoSQL systems respectively. This thesis presents an architecture and methodology to achieve this objective.
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Hsu, Jen-Chun, and 徐仁淳. "Correlation Aware Technique for SQL to NoSQL Transformation." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/77732462514698260699.

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碩士<br>中華大學<br>資訊工程學系碩士班<br>102<br>For better efficiency of parallel and distributed computing, Apache Hadoop distributes the imported data randomly on data nodes. This mechanism provides some advantages for general data analysis, when the data have the relationship between the data sets (example: Database), it’s a popular issue that stores the data with relevance. With the data sets increasing, a lot of data to be stored in a database, if we still use traditional database that has been unable to capable of providing an efficient service to real-time system. Most people wanted to use Hadoop to improve database performance. At this time, Apache provided a tool named Sqoop that can import all databases to Hadoop environment by command line interface. Have the same concept with Hadoop, Apache Sqoop separates each table into four parts and randomly distributes them on data nodes. However, there is still a database performance concern with this data placement mechanism. This paper proposes a Correlation Aware method on Sqoop (CA_Sqoop) to improve the data placement. By gathering related data as close as it could be to reduce the data transformation cost of the network and improve the performance in terms of database usage. The CA_Sqoop also considers the table correlation and size for better data locality and query efficiency. Simulation results show the data locality of CA_Sqoop is two times better than that of original Apache Sqoop.
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Chen, Yi-Hsiung, and 陳義雄. "Data Modeling in Cloud with Cassandra: From SQL to NoSQL." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/37665566601988018100.

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Shih, Jhe Wei, and 石哲維. "Development of Entity-Relationship Model driven SQL-to-NoSQL Schema Transformation for Machine Learning Algorithms." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/evfg9s.

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碩士<br>國立臺北科技大學<br>電子工程系研究所<br>105<br>Machine learning algorithms can produce high-value analysis and prediction for decision making from a mass of data without the help of humanity. Thus, the machine learning technique becomes one of most popular data analytics. Regarding the trend of big data, cloud computing has the ability of parallel processing and high scalability. More and more companies try to migrate from the traditional SQL database into the new NoSQL database. However, most engineers may not be familiar with the NoSQL database like the SQL database. Thus, this paper presents an SQL-to-NoSQL schema transformation based on the entity-relationship model (ER model) for machine learning. First, the proposed approach analyzes the schema of one SQL database based on the corresponding ER model. Then, our approach can produce the schema of the NoSQL database by merging related tables according to the proposed rules. That is, any SQL engineers can transfer the SQL database into the NoSQL database even if they need not design the schema of the NoSQL database by themselves.
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Books on the topic "SQL-to-NoSQL"

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Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases. Elsevier Science & Technology Books, 2013.

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Book chapters on the topic "SQL-to-NoSQL"

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

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Nair, Mydhili K., Nihal Nayak, and Arjun Rao. "Data Migration Techniques from SQL to NoSQL." In NoSQL: Database for Storage and Retrieval of Data in Cloud. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315155579-4.

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Rathika, V. "Graph-Based Denormalization for Migrating Big Data from SQL Database to NoSQL Database." In Intelligent Communication Technologies and Virtual Mobile Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28364-3_56.

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Zulkafli, Abu Zarin, Shuib Basri, Rohiza Ahmad, and Abdullahi Abubakar Imam. "A Framework for Image Synchronization from Mobile NoSQL Database to Server-Side SQL Database." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57141-6_19.

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Tear, Adrian. "SQL or NoSQL? Contrasting Approaches to the Storage, Manipulation and Analysis of Spatio-temporal Online Social Network Data." In Computational Science and Its Applications – ICCSA 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09144-0_16.

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Adhikari, Mainak, and Sukhendu Kar. "NoSQL Databases." In Handbook of Research on Securing Cloud-Based Databases with Biometric Applications. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6559-0.ch006.

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NoSQL database provides a mechanism for storage and access of data across multiple storage clusters. NoSQL dabases are finding significant and growing industry to meet the huge data storage requirements of Big data, real time applications, and Cloud Computing. NoSQL databases have lots of advantages over the conventional RDBMS features. NoSQL systems are also referred to as “Not only SQL” to emphasize that they may in fact allow Structured language like SQL, and additionally, they allow Semi Structured as well as Unstructured language. A variety of NoSQL databases having different features to deal with exponentially growing data intensive applications are available with open source and proprietary option mostly prompted and used by social networking sites. This chapter discusses some features and challenges of NoSQL databases and some of the popular NoSQL databases with their features on the light of CAP theorem.
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Deka, Ganesh Chandra. "NoSQL Databases." In Advances in Data Mining and Database Management. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5864-6.ch008.

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NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.
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"Bridging Relational and NoSQL Worlds." In Bridging Relational and NoSQL Databases. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3385-6.ch005.

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The chapter discusses the fact that the development and use of NoSQL databases showed that neither everything was good in NoSQL nor everything was so bad in relational databases. Namely, when operating with data, NoSQL databases have identical requirements for entering, updating, deleting or searching data, or for the data manipulation that SQL already resolved long ago. Therefore, it is not surprising that further development of many NoSQL databases shifted towards supporting SQL, which is one of the topics of this chapter. Namely, database users are generally not concerned with details about how data is stored. Rather, they want to have the possibility to view and analyze data together, regardless of whether the data is stored in relational or NoSQL databases. Therefore, vendors of relational databases were forced to look for solutions that would allow them to work with data stored in NoSQL databases as well.
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"Bridging Relational and NoSQL Worlds." In Bridging Relational and NoSQL Databases. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3385-6.ch007.

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The chapter presents a real case study of the integration of relational and NoSQL databases. The example of a real project related to vehicle registration, particularly to testing vehicles for compliance with environmental standards, explains how those two worlds can be integrated. Oracle database is used as a relational database, while MongoDB is used as NoSQL database. The chapter sustains that the COMN notation can be successfully used in the process of modeling both relational and nonrelational data. All three ways of integration of relational and NoSQL databases are tested. The native solution was tested by using of native drivers for communication with Oracle and MongoDB databases. The hybrid solution used a Unity product. The reducing-to-one option, in this case, SQL, was tested on Oracle database. The capabilities of Oracle 12c database to work both with relational and nonrelational data by using SQL were tested.
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"Integration of Relational and NoSQL Databases." In Bridging Relational and NoSQL Databases. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3385-6.ch006.

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The chapter proposes three ways of integration of the two different worlds of relational and NoSQL databases: native, hybrid, and reducing to one option, either relational or NoSQL. The native solution includes using vendors' standard APIs and integration on the business layer. In a relational environment, APIs are based on SQL standards, while the NoSQL world has its own, unstandardized solutions. The native solution means using the APIs of the individual systems that need to be connected, leaving to the business-layer coding the task of linking and separating data in extraction and storage operations. A hybrid solution introduces an additional layer that provides SQL communication between the business layer and the data layer. The third integration solution includes vendors' effort to foresee functionalities of “opposite” side, thus convincing developers' community that their solution is sufficient.
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Conference papers on the topic "SQL-to-NoSQL"

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Parker, Zachary, Scott Poe, and Susan V. Vrbsky. "Comparing NoSQL MongoDB to an SQL DB." In the 51st ACM Southeast Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2498328.2500047.

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Zhao, Gansen, Qiaoying Lin, Libo Li, and Zijing Li. "Schema Conversion Model of SQL Database to NoSQL." In 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2014. http://dx.doi.org/10.1109/3pgcic.2014.137.

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Hsu, Jen-Chun, Ching-Hsien Hsu, Shih Chang Chen, and Yeh Ching Chung. "Correlation Aware Technique for SQL to NoSQL Transformation." In 2014 7th International Conference on Ubi-Media Computing and Workshops (UMEDIA). IEEE, 2014. http://dx.doi.org/10.1109/u-media.2014.27.

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Yassine, Fatima, and Mamoun Adel Awad. "Migrating from SQL to NOSQL Database: Practices and Analysis." In 2018 International Conference on Innovations in Information Technology (IIT). IEEE, 2018. http://dx.doi.org/10.1109/innovations.2018.8606019.

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Solanke, Ganesh B., and K. Rajeswari. "SQL to NoSQL transformation system using data adapter and analytics." In 2017 IEEE International Conference on Technological Innovations in Communication, Control and Automation (TICCA). IEEE, 2017. http://dx.doi.org/10.1109/ticca.2017.8344580.

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Ceresnak, Roman, Adam Dudas, Karol Matiasko, and Michal Kvet. "Mapping rules for schema transformation : SQL to NoSQL and back." In 2021 International Conference on Information and Digital Technologies (IDT). IEEE, 2021. http://dx.doi.org/10.1109/idt52577.2021.9497629.

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7

Gomes, Carlos, Eduardo Tavares, and Meuse Nogueira De O. Junior. "Energy Consumption Evaluation of NoSQL DBMSs." In XV Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2016.9729.

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Abstract:
Over the years, NoSQL Database Management Systems (DBMS) have been adopted as an alternative to the constraints of relational/SQL DBMSs. In order to demonstrate their feasibility, works have evaluated NoSQL DBMSs regarding some performance metrics, but energy consumption has not been assessed. Indeed, energy consumption is an issue that should not be neglected due to the rise of energy costs and environmental sustainability. This paper presents a peformance and energy consumption evaluation of NoSQL DBMSs, more specifically, Cassandra (column), MongoDB (document-oriented), Redis (key-value). Experiments are based on YCSB benchmark, and results demonstrate energy consumption can vary significantly among the assessed DBMSs for different commands (e.g., read) and workloads.
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Lee, Chao-Hsien, and Yu-Lin Zheng. "Automatic SQL-to-NoSQL schema transformation over the MySQL and HBase databases." In 2015 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2015. http://dx.doi.org/10.1109/icce-tw.2015.7216979.

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Lee, Chao-Hsien, and Yu-Lin Zheng. "SQL-to-NoSQL Schema Denormalization and Migration: A Study on Content Management Systems." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.353.

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Ghule, Sanket, and Ramkrishna Vadali. "Transformation of SQL system to NoSQL system and performing data analytics using SVM." In 2017 International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2017. http://dx.doi.org/10.1109/icoei.2017.8300833.

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