Journal articles: '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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Bouamama, Samah. "Migration from a Relational Database to NoSQL." International Journal of Knowledge-Based Organizations 8, no. 3 (July 2018): 63–80. http://dx.doi.org/10.4018/ijkbo.2018070104.

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This article describes how due to the monstrous evolution of the technology and the enormous increase in data, it becomes difficult to work with traditional database management tools; relational databases quickly reach their limits and adding servers does not increase performance. As a result of this problem, new technologies have emerged, such as NoSQL databases, which radically change the architecture of the databases that the authors are used to seeing, thus increasing the performance and availability of services. As these technologies are relatively new, standard or formal migration processes do not yet exist, the authors thought it useful to propose a migration approach from a relational database to a database-oriented columns type HBase and Cassandra.

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Bolesta, Wojciech. "Analysis of query execution speed in the selected NoSQL databases." Journal of Computer Sciences Institute 7 (September 30, 2018): 138–41. http://dx.doi.org/10.35784/jcsi.662.

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The scientific article deals with a comparison of the query speed of two NoSQL databases. Described databases will be MongoDB and CouchDB. The work presents speed comparisons of such queries as adding data to the database, editing database data, deleting data from the database, and searching data in the database. Also a general comparison of bases will be presented, with the answer to the question of which of the tested NoSQL databases is faster.

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Osemwegie, Omoruyi, Kennedy Okokpujie, Nsikan Nkordeh, Charles Ndujiuba, Samuel John, and Uzairue Stanley. "Performance Benchmarking of Key-Value Store NoSQL Databases." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 5333. http://dx.doi.org/10.11591/ijece.v8i6.pp5333-5341.

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Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.

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Pokorný, Jaroslav. "Integration of Relational and NoSQL Databases." Vietnam Journal of Computer Science 06, no. 04 (November 2019): 389–405. http://dx.doi.org/10.1142/s2196888819500210.

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The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.

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Acharya, Biswaranjan, Ajaya Kumar Jena, Jyotir Moy Chatterjee, Raghvendra Kumar, and Dac-Nhuong Le. "NoSQL Database Classification." International Journal of Knowledge-Based Organizations 9, no. 1 (January 2019): 50–65. http://dx.doi.org/10.4018/ijkbo.2019010105.

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The rapid growth in the digital world in form of exponentiation to accommodate huge amount of structured, semi-structured, unstructured and hybrid data received from different sources. By using the conventional data management tools, it is quite impossible to manage this semi-structured and unstructured data for which a non-relational database management system such as NoSQL and NewSQL are used to handle such types of data. These types of semi-structured and structured data are generally considered ‘Big Data.' This article describes the basic characteristics, background and the models of NoSQL used for big data applications. In this work, the authors surveyed different NoSQL characteristics used by the researchers and try to compare the strength and weakness of different NoSQL databases.

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Rakhmawati, Nur Aini, Muhammad Zuhri, Radityo Prasetianto Wibowo, Anwar Romadhon, Herdy Ardiansyah, and Olive Khoirul. "Benchmarking MySQL and NoSQL Databases on Egovbench Application." Journal of Information Technology and Its Utilization 2, no. 1 (August 21, 2019): 18. http://dx.doi.org/10.30818/jitu.2.1.2080.

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E-Government is a result of technological advances in the government field. E-government assessment is needed to encourage the development of e-government in a better direction. The e-Government assessment can be measured using the Egovbench application. Egovbench performs crawling to obtain information from related websites or social media. The process of crawling done by Egovbench produces extensive data, which reduced performance in data processing. Therefore, there is a need for a database solution that has the best performance such as high processing speed and small database size. This study examined the comparison between relational databases and non-relational databases based on selected metrics to obtain the most suitable database solution for Egovbench. The results show that the MySQL database has the advantage of complex query processing and the use of the database with the smallest storage space. MongoDB database has the advantage of low data transfer volumes. Couchbase database has the advantage of short and straightforward query processing with a high number. The evaluation results show that MySQL is more suitable for Egovbench needs, which is the best response time and query per second. MySQL outperformed the other two databases on backup and storage file sizes testing.

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Lalwani, Raj. "NOSQL Databases." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 877–82. http://dx.doi.org/10.22214/ijraset.2018.6133.

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Sharma, Yashraj, and Yashasvi Sharma. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research -GRANTHAALAYAH 7, no. 7 (July 31, 2019): 351–59. http://dx.doi.org/10.29121/granthaalayah.v7.i7.2019.777.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications. This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.

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Dykstra, Dave. "Comparison of the Frontier Distributed Database Caching System to NoSQL Databases." Journal of Physics: Conference Series 396, no. 5 (December 13, 2012): 052031. http://dx.doi.org/10.1088/1742-6596/396/5/052031.

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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 (August 9, 2020): 64. http://dx.doi.org/10.20961/ijai.v4i2.38956.

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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.

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Kriestanto, Danny, and Alif Benden Arnado. "IMPLEMENTASI WEBSITE PENCARIAN KOS DENGAN NoSQL." JIKO (Jurnal Informatika dan Komputer) 2, no. 2 (October 12, 2017): 103. http://dx.doi.org/10.26798/jiko.2017.v2i2.66.

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The new technology of database has moved forward the relational databases. Now, the massive and unstructured data encourage experts to create a new type of database without using query. One of this technology is called NoSQL (Not Only SQL). One of the developing RDBMS that using this technique is MongoDB, which already supporting data storage technology that is no longer need for structured tables and rigid-typed of data. The schema was made flexible to handle the changes of data. The MongoDB data collecting characteristics in the form of arrays is considered suitable for the implementation of boarding house searching where each of the boarding houses have their own scenario structures. MongoDB also supports several programming language, including PHP with Bootstrap material as interface. The results of the research showed that there are alot of difference in implementing a NoSQL database with the regular relational one. NoSQL databases considered alot more complicated in structure, data type, even the CRUD system. The results also showed that in order to view an array inside another array will need two processes.

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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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Rodriguez, Jonathan, Anthony Malgapo, Jacob Quick, and Chung-yu Huang. "Distributed Architecture of Mobile GIS Application Using NoSQL Database." International Journal of Information and Electronics Engineering 7, no. 6 (November 2017): 156–60. http://dx.doi.org/10.18178/ijiee.2017.7.6.681.

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Diogo, Miguel, Bruno Cabral, and Jorge Bernardino. "Consistency Models of NoSQL Databases." Future Internet 11, no. 2 (February 14, 2019): 43. http://dx.doi.org/10.3390/fi11020043.

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Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.

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Mallek, Hana, Faiza Ghozzi, Olivier Teste, and Faiez Gargouri. "BigDimETL with NoSQL Database." Procedia Computer Science 126 (2018): 798–807. http://dx.doi.org/10.1016/j.procs.2018.08.014.

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Stonebraker, Michael. "SQL databases v. NoSQL databases." Communications of the ACM 53, no. 4 (April 2010): 10–11. http://dx.doi.org/10.1145/1721654.1721659.

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Matallah, Houcine, Ghalem Belalem, and Karim Bouamrane. "Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database." International Journal of Software Science and Computational Intelligence 13, no. 3 (July 2021): 38–63. http://dx.doi.org/10.4018/ijssci.2021070104.

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NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

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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 (March 10, 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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Chen, Jeang-Kuo, and Wei-Zhe Lee. "An Introduction of NoSQL Databases Based on Their Categories and Application Industries." Algorithms 12, no. 5 (May 16, 2019): 106. http://dx.doi.org/10.3390/a12050106.

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The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise’s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.

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Berndt, Don, Ricardo Lasa, and James McCart. "SiteWit Corporation: SQL or NoSQL? That is the Question!" Journal of Information Technology Education: Discussion Cases 6 (2017): 04. http://dx.doi.org/10.28945/3920.

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Ricardo Lasa, CEO and co-founder of SiteWit Corporation, and his team faced a critical technology challenge in scaling the core database systems to meet rapidly escalating data volumes. Should he stick with well-known relational database technologies? Or should he re-implement core components in newer, highly distributed NoSQL databases in search of competitive advantages?

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Jeon, Joohyoung, Minjeong An, and Hongchul Lee. "NoSQL Database Modeling for End-of-Life Vehicle Monitoring System." Journal of Software 10, no. 10 (October 2015): 1160–69. http://dx.doi.org/10.17706//jsw.10.10.1160-1169.

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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 (December 31, 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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Mahmood, Alza A. "Automated Algorithm for Data Migration from Relational to NoSQL Databases." Al-Nahrain Journal for Engineering Sciences 21, no. 1 (February 10, 2018): 60. http://dx.doi.org/10.29194/njes21010060.

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One of the barriers that the developer community face once turning to the newly, highly distributable, schema agnostic and non-relational database, called NoSQL, which is how to migrate their legacy relational database (which is already filled with a large amount of data) into this new class of database management systems. This paper presents a new approach for converting the already filled relational database of any database management system to any type of NoSQL databases in the most optimized data structure form without bothering of specifying the schema of tables and relations between them. In addition, a simplified software as a prototype based on this algorithm is built to show the results of the output for testing the validity of the algorithm.

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Kharade, Jyoti, Anil Rama Kale, and Dhanaji S. Kharade. "NOSQL Database Opportunities and Applications." International Journal of Computer Sciences and Engineering 6, no. 5 (May 31, 2018): 804–7. http://dx.doi.org/10.26438/ijcse/v6i5.804807.

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Ganesh Chandra, Deka. "BASE analysis of NoSQL database." Future Generation Computer Systems 52 (November 2015): 13–21. http://dx.doi.org/10.1016/j.future.2015.05.003.

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Chang, Bao Rong, Hsiu-Fen Tsai, Chia-Yen Chen, Chien-Feng Huang, and Hung-Ta Hsu. "Implementation of Secondary Index on Cloud Computing NoSQL Database in Big Data Environment." Scientific Programming 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/560714.

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This paper introduces the combination of NoSQL database HBase and enterprise search platform Solr so as to tackle the problem of the secondary index function with fast query. In order to verify the effectiveness and efficiency of the proposed approach, the assessment using Cost-Performance ratio has been done for several competitive benchmark databases and the proposed one. As a result, our proposed approach outperforms the other databases and fulfills secondary index function with fast query in NoSQL database. Moreover, according to the cross-sectional analysis, the proposed combination of HBase and Solr database is capable of performing an excellent query/response in a big data environment.

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Lima, Cláudio, and Ronaldo Santos Mello. "On proposing and evaluating a NoSQL document database logical approach." International Journal of Web Information Systems 12, no. 4 (November 7, 2016): 398–417. http://dx.doi.org/10.1108/ijwis-04-2016-0018.

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Purpose NoSQL databases do not require a default schema associated with the data. Even that, they are categorized by data models. A model associated with the data can promote better strategies for persistence and manipulation of data in the target database. Based on this motivation, the purpose of this paper is to present an approach for logical design of NoSQL document databases that consists a process that converts a conceptual modeling into efficient logical representations for a NoSQL document database. The authors also evaluate their approach and demonstrate that the generated NoSQL logical structures reduce the amount of data items accessed by queries. Design/methodology/approach This paper presents an approach for logical design of NoSQL document database schemas based on a conceptual schema. The authors generate compact and redundancy-free schemas and define appropriate representations in a NoSQL document logical model. The estimated volume of data and workload information can be considered to generate optimized NoSQL document structures. Findings This approach was evaluated through a case study with an experimental evaluation in the e-commerce application domain. The results demonstrate that the authors’ workload-based conversion process improves query performance on NoSQL documents by reducing the number of database accesses. Originality/value Unlike related work, the reported approach covers all typical conceptual constructs, details a conversion process between conceptual schemas and logical representations for NoSQL document database category and, additionally, considers the estimated database workload to perform optimizations in the logical structure. An experimental evaluation shows that the proposed approach is promising.

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Matallah, Houcine, Ghalem Belalem, and Karim Bouamrane. "Evaluation of NoSQL Databases." International Journal of Software Science and Computational Intelligence 12, no. 4 (October 2020): 71–91. http://dx.doi.org/10.4018/ijssci.2020100105.

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The explosion of the data quantities, which reflects the scaling of volumes, numbers, and types, has resulted in the development of new locations techniques and access to data. The final steps in this evolution have emerged new technologies: cloud computing and big data. The new requirements and the difficulties encountered in the management of data classified “big data” have emerged NoSQL and NewSQL systems. This paper develops a comparative study about the performance of six solutions NoSQL, employed by the important companies in the IT sector: MongoDB, Cassandra, HBase, Redis, Couchbase, and OrientDB. To compare the performance of these NoSQL systems, the authors will use a very powerful tool called YCSB: Yahoo! Cloud Serving Benchmark. The contribution is to provide some answers to choose the appropriate NoSQL system for the type of data used and the type of processing performed on that data.

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Jain, Vidushi, and Aviral Upadhyay. "MongoDB and NoSQL Databases." International Journal of Computer Applications 167, no. 10 (June 15, 2017): 16–20. http://dx.doi.org/10.5120/ijca2017914385.

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Mukhin, A. M., M. A. Genaev, D. A. Rasskazov, S. A. Lashin, and D. A. Afonnikov. "RDBMS and NOSQL Based Hybrid Technology for Transcriptome Data Structuring and Processing." Mathematical Biology and Bioinformatics 15, no. 2 (December 28, 2020): 455–70. http://dx.doi.org/10.17537/2020.15.455.

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The transcriptome sequencing experiment (RNA-seq) has become almost a routine procedure for studying both model organisms and crops. As a result of bioinformatics processing of such experimental output, huge heterogeneous data are obtained, representing nucleotide sequences of transcripts, amino acid sequences, and their structural and functional annotation. It is important to present the data obtained to a wide range of researchers in the form of databases. This article proposes a hybrid approach to creating molecular genetic databases that contain information about transcript sequences and their structural and functional annotation. The essence of the approach consists in the simultaneous storing both structured and weakly structured data in the database. The technology was used to implement a database of transcriptomes of agricultural plants. This paper discusses the features of implementing this approach and examples of generating both simple and complex queries to such a database in the SQL language. The OORT database is freely available at https://oort.cytogen.ru/.

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Blank, Sebastian, Florian Wilhelm, Hans-Peter Zorn, and Achim Rettinger. "Querying NoSQL with Deep Learning to Answer Natural Language Questions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9416–21. http://dx.doi.org/10.1609/aaai.v33i01.33019416.

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Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trainable question answering (QA) system that allows a user to query an external NoSQL database by using natural language. A major challenge of such a system is the non-differentiability of database operations which we overcome by applying policy-based reinforcement learning. We evaluate our approach on Facebook’s bAbI Movie Dialog dataset and achieve a competitive score of 84.2% compared to several benchmark models. We conclude that our approach excels with regard to real-world scenarios where knowledge resides in external databases and intermediate labels are too costly to gather for non-end-to-end trainable QA systems.

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Тереник, Дмитро, and Георгій Кучук Анатолійович. "ПОРІВНЯННЯ SQL І NOSQL БАЗ ДАНИХ НА ПРИКЛАДІ ПРОЕКТУВАННЯ АФФІЛЕЙТ РЕПОРТ СИСТЕМ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (January 28, 2020): 83–89. http://dx.doi.org/10.32620/reks.2020.1.08.

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Nowadays, due to the rapid development of social networks and the blogger culture, there is a tendency to use affiliate systems to promote their product. The Affiliate Reporting Service is a service offered to customers who want to analyze the affiliate systems' performance data. These systems are used by business executives and business owners to analyze ecommerce data and convert it into profit/expense data to adjust their business path further. This type of service includes data storage for all affiliates, data archive management, conversion of advertising campaigns, trend tracking, and more. These systems are based on large data sets that need to be stored correctly and safely stored and processed using database management systems. There are two major direction: SQL and NoSQL, relational and non-relational databases. The differences between them are how they are designed, what types of data they support, how they store information, how they support information security. A rigid relational database schema helps maintain the security and integrity of data when stored and modified. The lack of a rigid database schema and the need to change the entire structure of the table with a minimal change in the storage concept, make it easier to work with non-relational databases and subsequently support them, but it also has its disadvantages. It is important to understand that the tasks are different and the methods for solving them are also different; Choosing a database and database management system is a complex multi-parameter task and is one of the most important steps in developing such applications. Properly selected database will reduce the monetary and time costs associated with the development of the software, as well as facilitate system support in the future. The purpose of the article is to compare relational and non-relational databases by different metrics used in Affiliate Reporting Systems Design. In particular, a performance analysis was conducted on the performance of various operations, on the basis of which conclusions were drawn about the use of a particular database.

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Shehata, NaglaaSaeed, and Amira Hassan Abed. "Big Data With Column Oriented NOSQL Database To Overcome The Drawbacks Of Relational Databases." International Journal of Advanced Networking and Applications 11, no. 05 (2020): 4423–28. http://dx.doi.org/10.35444/ijana.2020.11057.

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Ha, Van Muon, Yulia A. Shichkina, and Sergey V. Kostichev. "Determining the Composition of Collections for Key-Document Databases Based on a Given Set of Object Properties and Database Querie." Computer tools in education, no. 3 (September 30, 2019): 15–28. http://dx.doi.org/10.32603/2071-2340-2019-3-15-28.

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The work of transforming a database from one format periodically appears in different organizations for various reasons. Today, the mechanism for changing the format of relational databases is well developed. However, with the advent of new types of databases, such as NoSQL, this problem is prevalent due to the radically different ways of data organization at the various databases. This article discusses a formalized method based on set theory, at the choice of the number and composition of collections for a key-value type database. The initial data are the properties of objects, about which information is stored in the database, and the set of queries that are most frequently executed. The considered method can be applied not only when creating a new keyvalue database, but also when transforming an existing one, when moving from relational databases to NoSQL, when consolidating databases.

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Suma, Sugimiyanto, and Fahad Alqurashi. "A comparison study of NoSQL document-oriented database system." International Journal of Applied Mathematical Research 8, no. 1 (July 6, 2019): 27. http://dx.doi.org/10.14419/ijamr.v8i1.29434.

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By increasing data generation at these day, requirement for a sufficient storage system are strongly needed by stakeholders to store and access huge number of data in efficient way for fast analysis and decision. While RDBMS cannot deal with this challenge, NoSQL has emerged as a solution to address this challenge. There have been plenty of NoSQL database engine with their categories and characteristics, especially for document-oriented database. However, it makes a confusion for the system developer to choose the appropriate NoSQL database for their system. This paper is our preliminary report to provide a comparison of NoSQL databases. The comparison is based on performance of execution time which is measured by building a simple program. This experiment was done in our local cluster by exploiting around 1 million datasets. The result shows that RDB has better performance than CDB in terms of execution time.

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Bogdan, George Tudorica. "Challenges for the NoSQL systems." International Journal of Sustainable Economies Management 2, no. 1 (January 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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Ait El Mouden, Zakariyaa, and Abdeslam Jakimi. "A New Algorithm for Storing and Migrating Data Modelled by Graphs." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 11 (October 5, 2020): 137. http://dx.doi.org/10.3991/ijoe.v16i11.15545.

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NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. NoSQL solutions surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the machine that runs the database and also the operating system. Also, in term of velocity, many researches compared the execution time of different transactions and proved that NoSQL databases are the perfect solution for real-time applications. This paper presents an algorithm to store data modeled by graphs as NoSQL documents, the purpose of this study is to exploit the high amount of data stored in SQL databases and to make such data usable by recent clustering algorithms and other data science tools. This study links relational data to document datastores by defining an effective algorithm for reading relational data, modelling those data as graphs and storing those data as NoSQL documents.

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Chaudhary, Renu, and Gagangeet Singh. "A NOVEL TECHNIQUE IN NoSQL DATA EXTRACTION." International Journal of Research -GRANTHAALAYAH 1, no. 1 (August 31, 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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Pereira, Diogo Augusto, Wagner Ourique de Morais, and Edison Pignaton de Freitas. "NoSQL real-time database performance comparison." International Journal of Parallel, Emergent and Distributed Systems 33, no. 2 (March 30, 2017): 144–56. http://dx.doi.org/10.1080/17445760.2017.1307367.

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Sattar, Abdul, Torben Lorenzen, and Keerthi Nallamaddi. "Incorporating NoSQL into a database course." ACM Inroads 4, no. 2 (June 2013): 50–53. http://dx.doi.org/10.1145/2465085.2465100.

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Journal articles on the topic 'NosSQL database'

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