Journal articles: 'NoSQL MongoDB'

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Renaldi, Renaldi, Billy Cahyo Santoso, Youzy Natasya, Steven Willian, and Fladianand Alfando. "Tinjauan Pustaka Sistematis terhadap Basis Data MongoDB." Jurnal Inovasi Informatika 5, no. 2 (September 30, 2020): 132–42. http://dx.doi.org/10.51170/jii.v5i2.79.

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Istilah NoSQL di dunia IT sudah mulai terkenal di bidang basis data. Basis data SQL dan NoSQL memilki perbedaan yang cukup signifikan.Model data yang digunakan basis data SQL yaitu berupa tabel, yang tersusun atas baris dan kolom. Basis data SQL manapun pasti model data yang sama dalam menyimpan arsip datanya, yaitu melalui tabel. Hal ini berbeda dengan basis data NoSQL. No SQL memilki arti non SQL atau not only SQL yang artinya ditujukan pada penggunaan model data alternatif selain tabular (relasi antar tabel). Tergantung basis datanya, basis data NoSQL bisa berupa dokumen, grafik ataupun nilai kunci. Salah basis data NoSQL yang sudah banyak dikenal orang yaitu MongoDB. MongoDB adalah basis data NoSQL yang bersifat document based, artinya hanya tersusun atas koleksi dan dokumen. Paper ini tertulis tentang karakteristik MongoDB, Kelebihan dan kekurangan MongoDB, Aplikasi yang sudah menggunakan MongoDB, serta beberapa contoh query yang di gunakan di MongoDB. Dengan paper ini, penulis berharap pembaca bisa mengerti dengan benar apa itu MongoDB serta implementasinya.

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Wahane, Deepa Suresh, and Prof Mayuri Dhondiba Dendge. "Analysis on NoSQL: MongoDB Tool." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1077–81. http://dx.doi.org/10.31142/ijtsrd13089.

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Xiang, Longgang, Xiaotian Shao, and Dehao Wang. "PROVIDING R-TREE SUPPORT FOR MONGODB." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 545–49. http://dx.doi.org/10.5194/isprsarchives-xli-b4-545-2016.

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Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

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Xiang, Longgang, Xiaotian Shao, and Dehao Wang. "PROVIDING R-TREE SUPPORT FOR MONGODB." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 545–49. http://dx.doi.org/10.5194/isprs-archives-xli-b4-545-2016.

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Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.

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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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Andor, C. F. "Performance Benchmarking for NoSQL Database Management Systems." Studia Universitatis Babeș-Bolyai Informatica 66, no. 1 (July 1, 2021): 23. http://dx.doi.org/10.24193/subbi.2021.1.02.

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NoSQL database management systems are very diverse and are known to evolve very fast. With so many NoSQL database options available nowadays, it is getting harder to make the right choice for certain use cases. Also, even for a given NoSQL database management system, performance may vary significantly between versions. Database performance benchmarking shows the actual performance for different scenarios on different hardware configurations in a straightforward and precise manner. This paper presents a NoSQL database performance study in which two of the most popular NoSQL database management systems (MongoDB and Apache Cassandra) are compared, and the analyzed metric is throughput. Results show that Apache Cassandra outperformes MongoDB in an update heavy scenario only when the number of operations is high. Also, for a read intensive scenario, Apache Cassandra outperformes MongoDB only when both number of operations and degree of parallelism are high.

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Zhao, Yan. "Research on MongoDB Design and Query Optimization in Vehicle Management Information System." Applied Mechanics and Materials 246-247 (December 2012): 418–22. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.418.

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NoSQL database is the broad definition of non-relational database. MongoDB is one of the most popular NoSQL database nowadays, and it is a schema-free and document oriented database, which has great query performance with huge amount of data and great scalability. More and more companies have adopted MongoDB, as increasingly large scale data is being stored in MongoDB, design philosophy and query optimization issues become growing concerns. This paper attempts to give the data storage structures of MongoDB on vehicle management information system. Based on this system, this paper gives two design philosophy and five different query optimization methods for MongoDB and gives the key code and diagrams of MongoDB optimization methods.

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Tunardi, Yovita, and Rita Layona. "Nosql Technology In Android Based Mobile Chat Application Using Mongodb." ComTech: Computer, Mathematics and Engineering Applications 5, no. 2 (December 1, 2014): 553. http://dx.doi.org/10.21512/comtech.v5i2.2180.

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Along with the development of data storage technology which previously using relational concept began to change to the non-relational concept or sometimes referred to as the term NoSQL technology. Data storage in NoSQL is no longer based on the relations between tables but using another methods, one of them is document-oriented. This method was applied specifically in MongoDB. This concept brings new hope because oftheir superiority that can handle very large data with promising performance and is perfect for agile system development. The purpose of this research was to measure the performance NoSQL, especially MongoDB with implemented it in a Android based mobile chat application. This research uses three methods, analysis method including literature study, analysis of similar application, questionnaires, design method using Agile SofwareDevelopment, and evaluation method including eight golden rules, analysis of similar application, questionnaires, and interview. The results of this research is a mobile chat application that uses MongoDB as the data storage technology. Through this research can be drawn the conclusion that NoSQL technology implementation, MongoDB, give special advantages like lighter data storage and faster data access.

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Gupta, Sangeeta. "Performance Evaluation of Unstructured PBRA for Bigdata with Cassandra and MongoDB in Cloud." International Journal of Cloud Applications and Computing 8, no. 3 (July 2018): 48–59. http://dx.doi.org/10.4018/ijcac.2018070104.

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In this article, performance evaluation of web collection data in data stores, such as NoSQL-Cassandra and MongoDB is presented, yielding scalability of applications. In addition to scalability, security of NoSQL databases remains highly unproved. It is noteworthy that existing works in the area of cloud with NoSQL focus on either scalability or security but not both aspects. Also, security, if provided, is at minor interface levels. In this article, the PBRA system is designed to deal with highly unstructured big data emerging from the twitter social networking service, which is new of its kind to strengthen the bigdata security. PBRA is Passphrase Based REST API model where the REST API methods are integrated with the user generated passphrase in addition to the private key for a set of records of user desirable number before storing into the Cassandra and MongoDB databases. Results are presented to illustrate the same for nearly 1 million records and the efficiency of Cassandra over MongoDB is observed. It is observed from the results that though the time taken to load and retrieve bulk data records is higher than dealing with cipher text, Cassandra performs better than MongoDB with the proposed security model.

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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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Boehm, J., and K. Liu. "NOSQL FOR STORAGE AND RETRIEVAL OF LARGE LIDAR DATA COLLECTIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 577–82. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-577-2015.

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Developments in LiDAR technology over the past decades have made LiDAR to become a mature and widely accepted source of geospatial information. This in turn has led to an enormous growth in data volume. The central idea for a file-centric storage of LiDAR point clouds is the observation that large collections of LiDAR data are typically delivered as large collections of files, rather than single files of terabyte size. This split of the dataset, commonly referred to as tiling, was usually done to accommodate a specific processing pipeline. It makes therefore sense to preserve this split. A document oriented NoSQL database can easily emulate this data partitioning, by representing each tile (file) in a separate document. The document stores the metadata of the tile. The actual files are stored in a distributed file system emulated by the NoSQL database. We demonstrate the use of MongoDB a highly scalable document oriented NoSQL database for storing large LiDAR files. MongoDB like any NoSQL database allows for queries on the attributes of the document. As a specialty MongoDB also allows spatial queries. Hence we can perform spatial queries on the bounding boxes of the LiDAR tiles. Inserting and retrieving files on a cloud-based database is compared to native file system and cloud storage transfer speed.

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Narváez, Miryan Estela, Pablo Ronny Calapucha Grefa, Marco Vinicio Tarco Caisa, and Pamela Alexandra Buñay Guisñan. "Análisis de Desempeño entre MONGODB y COUCHDB utilizando Norma ISO/IEC 25000." Revista Perspectivas 2, no. 2 (July 5, 2020): 13–20. http://dx.doi.org/10.47187/perspectivas.vol2iss2.pp13-20.2020.

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Las bases de datos NoSQL han surgido para dar respuesta a problemas de escalabilidad y rendimiento, que en general, las bases de datos relacionales no pueden abarcar. La gran flexibilidad y las posibilidades de optimización en sus diseños las convierten en una atractiva variante a tener en cuenta para el desarrollo de aplicaciones de gestión de información. El objetivo principal del proyecto de investigación fue un estudio comparativo entre MongoDB y CouchDB utilizando la norma ISO/IEC 25010, con el fin de analizar y medir el desempeño de los gestores de base de datos NoSQL, en cuanto al consumo de recursos utilizados. Como resultado de la investigación se obtuvo que MongoDB fue ligeramente superior a CouchDB, demostrando que las bases de datos NoSQL tienen mejor rendimiento al momento de administrar grandes volúmenes de datos.

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Celesti, Antonio, Maria Fazio, and Massimo Villari. "A Study on Join Operations in MongoDB Preserving Collections Data Models for Future Internet Applications." Future Internet 11, no. 4 (March 27, 2019): 83. http://dx.doi.org/10.3390/fi11040083.

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Presently, we are observing an explosion of data that need to be stored and processed over the Internet, and characterized by large volume, velocity and variety. For this reason, software developers have begun to look at NoSQL solutions for data storage. However, operations that are trivial in traditional Relational DataBase Management Systems (DBMSs) can become very complex in NoSQL DBMSs. This is the case of the join operation to establish a connection between two or more DB structures, whose construct is not explicitly available in many NoSQL databases. As a consequence, the data model has to be changed or a set of operations have to be performed to address particular queries on data. Thus, open questions are: how do NoSQL solutions work when they have to perform join operations on data that are not natively supported? What is the quality of NoSQL solutions in such cases? In this paper, we deal with such issues specifically considering one of the major NoSQL document oriented DB available on the market: MongoDB. In particular, we discuss an approach to perform join operations at application layer in MongoDB that allows us to preserve data models. We analyse performance of the proposes approach discussing the introduced overhead in comparison with SQL-like DBs.

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George, Tudorica Bogdan. "A New Application for the Management of the MongoDB Servers." International Journal of Sustainable Economies Management 2, no. 3 (July 2013): 58–71. http://dx.doi.org/10.4018/ijsem.2013070105.

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The application presented in the following subsections intends to cover one of the noticeable gaps of the NoSQL domain, namely the relative lack of working tools and systems administration for new large data storage systems. Following the comparative analysis of the NoSQL solutions on the market, the MongoDB system was chosen as target application for this step of development, for reasons mainly related to proven performance, flexibility, market presence already in place and ease of use.

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Dai, Cheng, Yan Ye, Tai Jun Liu, and Jing Jing Zheng. "Design of High Performance Cloud Storage Platform Based on Cheap PC Clusters Using MongoDB and Hadoop." Applied Mechanics and Materials 380-384 (August 2013): 2050–53. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2050.

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To lay the foundation for the high performance private cloud storage platform, this paper proposes a new cloud storage structure with horizontal scalability using MongoDB and Hadoop. MongoDB is a powerful NOSQL database which is used to construct the cloud storage platform. In certain scenarios, the map-reduce provided by MongoDB can not meet the need of the complex data analysis, especially for the mass complex unstructured data such as videos and documents. This paper introduce the key technologies in MongoDB and Hadoop, then aggregate the advantages of them to build a high performance private cloud storage infrastructure based on cheap personal computer clusters. This infrastructure combines the high horizontal scalability of MongoDB and the high-performance analysis capability from Hadoop.

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Moreno Arboleda, Francisco Javier, Juan Esteban Quintero Rendón, and Robinson Rueda Vásquez. "Una comparación de rendimiento entre Oracle y MongoDB." Ciencia e Ingeniería Neogranadina 26, no. 1 (April 30, 2016): 109. http://dx.doi.org/10.18359/rcin.1669.

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La creciente y enorme cantidad de datos, del orden de exabytes, generados por las aplicaciones empresariales actuales han originado conjuntos masivos de estos. Los sistemas de gestión de bases de datos (SGBD) NoSQL han surgido como una alternativa a los SGBD relacionales para la gestión de estos conjuntos. Entre los principales SGBD NoSQL está MongoDB. En este artículo se compara el rendimiento entre MongoDB y Oracle (uno de los principales SGBD que soporta bases de datos relacionales). La comparación se basa en las operaciones de inserción, consulta, actualización y borrado (CRUD, por sus siglas en inglés). Aunque se requieren experimentos más exhaustivos y muchos otros tipos de pruebas, los resultados ofrecen un punto de partida para el análisis de rendimiento en estos SGBD.

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Jia, Xin Wei, and Gui Cheng Shen. "Research on Relational Database and NoSQL Based on XML Data." Applied Mechanics and Materials 713-715 (January 2015): 2329–34. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2329.

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With the rapid development of electronic commerce, huge transaction data is produced every day. How to store these data effectively and mine knowledge from these data has become the key urgent problem to solve. This paper first introduces the NO_SQL database MongoDB and its advantages. Then we have a compare on writing and reading performance between MogoDB and MySQL when storing XML document. Finally we have a conclusion that MongoDB has higher performance than MySQL by experiment, and No-SQL is a good choice when querying in massive data.

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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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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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Sahu, Arvind, and Swati Ahirrao. "Graph Based Workload Driven Partitioning System by using MongoDB." International Journal of Advances in Applied Sciences 7, no. 1 (March 1, 2018): 29. http://dx.doi.org/10.11591/ijaas.v7.i1.pp29-37.

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The web applications and websites of the enterprises are accessed by a huge number of users with the expectation of reliability and high availability. Social networking sites are generating the data exponentially large amount of data. It is a challenging task to store data efficiently. SQL and NoSQL are mostly used to store data. As RDBMS cannot handle the unstructured data and huge volume of data, so NoSQL is better choice for web applications. Graph database is one of the efficient ways to store data in NoSQL. Graph database allows us to store data in the form of relation. In Graph representation each tuple is represented by node and the relationship is represented by edge. But, to handle the exponentially growth of data into a single server might decrease the performance and increases the response time. Data partitioning is a good choice to maintain a moderate performance even the workload increases. There are many data partitioning techniques like Range, Hash and Round robin but they are not efficient for the small transactions that access a less number of tuples. NoSQL data stores provide scalability and availability by using various partitioning methods. To access the Scalability, Graph partitioning is an efficient way that can be easily represent and process that data. To balance the load data are partitioned horizontally and allocate data across the geographical available data stores. If the partitions are not formed properly result becomes expensive distributed transactions in terms of response time. So the partitioning of the tuple should be based on relation. In proposed system, Schism technique is used for partitioning the Graph. Schism is a workload aware graph partitioning technique. After partitioning the related tuples should come into a single partition. The individual node from the graph is mapped to the unique partition. The overall aim of Graph partitioning is to maintain nodes onto different distributed partition so that related data come onto the same cluster.

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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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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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SARAN, Murat, and Nurdan SARAN. "Cassandra ve MongoDB NoSQL Veri Tabanlarının Karşılaştırmalı Güvenlik Analizi." ULUSLARARASI BİLGİ GÜVENLİĞİ MÜHENDİSLİĞİ DERGİSİ 5, no. 2 (December 30, 2019): 1–11. http://dx.doi.org/10.18640/ubgmd.655489.

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Et. al., Shivakumar C,. "CONTEXT STORAGE USING CLOUD-BASED MONGODB WITH RULE-BASED RETE ALGORITHM." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (April 13, 2021): 1022–30. http://dx.doi.org/10.17762/itii.v9i2.448.

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In this Context-aware computing era, everything is being automated and because of this, smart system’s count been incrementing day by day. The smart system is all about context awareness, which is a synergy with the objects in the system. The result of the interaction between the users and the sensors is nothing but the repository of the vast amount of context data. Now the challenging task is to represent, store, and retrieve context data. So, in this research work, we have provided solutions to context storage. Since the data generated from the sensor network is dynamic, we have represented data using Context dimension tree, stored the data in cloud-based ‘MongoDB’, which is a NoSQL. It provides dynamic schema and reasoning data using If-Then rules with RETE algorithm. The Novel research work is the integration of NoSQL cloud-based MongoDB, rule-based RETE algorithm and CLIPS tool architecture. This integration helps us to represent, store, retrieve and derive inferences from the context data efficiently..

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Nikitina, T. S., and О. I. Morozova. "ПОРІВНЯЛЬНИЙ АНАЛІЗ ПРОДУКТИВНОСТІ БАЗ ДАНИХ SQL ТА NOSQL." Системи управління, навігації та зв’язку. Збірник наукових праць 1, no. 53 (February 5, 2019): 125–28. http://dx.doi.org/10.26906/sunz.2019.1.125.

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В роботі було проведено короткий аналіз функцій баз даних SQL та NoSQL, були приведені їх основні відмінності. На сьогоднішній день існують два найбільш поширених типу систем управління даними: реляційні бази даних та NoSQL. Існує величезне різноманіття моделей даних та API (Application Programming Interface) запитів для NoSQL. Зокрема для порівняння були обрані Apache Cassandra, DynamoDB, MongoDB. Модель даних та функціональність Apache Cassandra має схожість з іншими масштабованими сховищами. Оновлення та угруповання стовпців кешується в оперативній пам'яті, після чого скидаються на диск. Основною метою роботи було порівняння продуктивності реляційних SQL баз даних та NoSQL, на прикладі PostgreSQL, MySQL, Apach Cassandra, MongoDB, Amazon DynamoDB. Для тестування продуктивності було розроблено окремий програмний продукт. Основним предметом дослідження є продуктивність базових операцій цих систем. Результати про продуктивність кожної з них були отримані за допомогою розробленої системи тестування, адаптованої для потреб дослідження. Розроблена система тестування озволила тестувати швидкість виконання складних аналітичних операцій, робити додаткові налаштування, використовувати великий обсяг даних. Система була розширена для виконання тестування розширеного набору операцій над схемою даних, що містить зв'язки між таблицями. Ця система тестування містить набір готових навантажень, які покривають основні аспекти функціонування й підтримують створені користувачем навантаження. За допомогою системи тестування були отримано дані про продуктивність представлених систем управління базами даних для набору різних запитів. Для аналізу продуктивності вимірювався час відгуку систем на запит – час між початком запиту й одержанням відповіді. Порівнювалися два види показників – середній відгук по виконані операції й деталізований аналіз. Отримані дані були представлені у вигляді діаграм, і по ним був зроблений висновок про продуктивність баз даних SQL та NoSQL. Вибір баз даних повинен максимально ґрунтуватися на типі вирішуваних завдань й також повинен враховувати обсяги даних, час відгуку системи.

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Alenezi, Mamdouh, Muhammad Usama, Khaled Almustafa, Waheed Iqbal, Muhammad Ali Raza, and Tanveer Khan. "An Efficient, Secure, and Queryable Encryption for NoSQL-Based Databases Hosted on Untrusted Cloud Environments." International Journal of Information Security and Privacy 13, no. 2 (April 2019): 14–31. http://dx.doi.org/10.4018/ijisp.2019040102.

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NoSQL-based databases are attractive to store and manage big data mainly due to high scalability and data modeling flexibility. However, security in NoSQL-based databases is weak which raises concerns for users. Specifically, security of data at rest is a high concern for the users deployed their NoSQL-based solutions on the cloud because unauthorized access to the servers will expose the data easily. There have been some efforts to enable encryption for data at rest for NoSQL databases. However, existing solutions do not support secure query processing, and data communication over the Internet and performance of the proposed solutions are also not good. In this article, the authors address NoSQL data at rest security concern by introducing a system which is capable to dynamically encrypt/decrypt data, support secure query processing, and seamlessly integrate with any NoSQL- based database. The proposed solution is based on a combination of chaotic encryption and Order Preserving Encryption (OPE). The experimental evaluation showed excellent results when integrated the solution with MongoDB and compared with the state-of-the-art existing work.

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Winaya, I. Gede, and Ahmad Ashari. "Transformasi Skema Basis Data Relasional Menjadi Model Data Berorientasi Dokumen pada MongoDB." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (January 31, 2016): 47. http://dx.doi.org/10.22146/ijccs.11188.

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MongoDB is a database that uses document-oriented data storage models. In fact, to migrate from a relational database to NoSQL databases such as MongoDB is not an easy matter especially if the data are extremely complex. Based on the documentation that has been done by several global companies related to the use of MongoDB, it can be concluded that the process of migration from RDBMS to MongoDB require quite a long time. One process that takes quite a lot is transformation of relational database schema into a document-oriented data model on MongoDB. This research discusses the development transformation system of relational database schema to the document oriented data model in MongoDB. The process of transformation is done by utilizing the structure and relationships between tables in the scheme as the main parameters of the modeling algorithm. In the process of the modeling documents, it necessary to adjustments the specifications of MongoDB document that formed document model can be implemented in MongoDB. Document models are formed from transformation process can be a single document, embedded document, referenced document or combination of these. Document models are formed depending on the type, rules, and the value of the relationships cardinality between tables in the relational database schema.

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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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Silalahi, Mesri. "PERBANDINGAN PERFORMANSI DATABASE MONGODB DAN MYSQL DALAM APLIKASI FILE MULTIMEDIA BERBASIS WEB." Computer Based Information System Journal 6, no. 1 (March 31, 2018): 63. http://dx.doi.org/10.33884/cbis.v6i1.574.

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Database appeared and began to develop in line with the needs of processing and data storage to meet the information needs. Database is part of an important building block in an information system. In addition to a relational database (SQL), which stores structured datas in tables with defined schemes, there is a non-relational databases (NoSQL) with a dynamic scheme or unstructured. This study will compare the performance between NoSQL database (MongoDB) and SQL database (MySQL) for a web-based multimedia file storage application that stores files as BLOBs. Performance comparison is based on the speed of execution and the computer resources usage (CPU, memory, and virtual memory).

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McClay, Wilbert. "A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases." Diseases 6, no. 4 (September 28, 2018): 89. http://dx.doi.org/10.3390/diseases6040089.

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In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.

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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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Germano, Kevin Lucas, and Eder Carlos Salazar Sotto. "UMA COMPARAÇÃO ENTRE MONGODB E COUCHDB." SIMTEC - Simpósio de Tecnologia da Fatec Taquaritinga 4, no. 1 (May 16, 2018): 14. http://dx.doi.org/10.31510/simtec.v4i1.302.

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Este artigo apresenta uma comparação de performance entre os SGBDs NoSQL MongoDB e CouchDB. O estudo de caso realizado apresenta os tempos de execução de conjuntos de operação de inserção, busca, atualização e exclusão (ações estas denominadas CRUD na linguagem dos bancos de dados), dos dois sistemas de gerenciamento de banco de dados, permitindo obter estas métricas de maneira quantitativa, e apresentá-las em forma de tabelas comparativas para cada operação, comparando a performance de ambos os SGDBs executando o mesmo conjunto de operações. Pelos resultados obtidos no ambiente de teste criado, é evidente a diferença entre o tempo de inserção, busca, atualização e exclusão entre MongoDB e CouchDB, permitindo assim concluir que o MongoDB é o mais performático para ser utilizado em conjunto com a linguagem PHP no desenvolvimento de aplicações Web.

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Datt, Niteshwar. "Comparative Study of CouchDB and MongoDB – NoSQL Document Oriented Databases." International Journal of Computer Applications 136, no. 3 (February 17, 2016): 24–26. http://dx.doi.org/10.5120/ijca2016908457.

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Pyo, Hye Jin, Hoon Jeong, Nan Ju Kim, and Eui In Choi. "Using Term-Based Partitioning Framework on MongoDB and Elastic Search." Applied Mechanics and Materials 590 (June 2014): 698–701. http://dx.doi.org/10.4028/www.scientific.net/amm.590.698.

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It's a major issue that how can find worthy information in big data. Because big datacan be used in company's success according how to take full advantage of big data analysis. Currently, search technologies aboutbeing stored distributed and duplicated data does not need to strong consistency. Therefore, nowadays we utilize variety of storage based on NoSQL for allowing loosens of strict consistency. MongoDB and Elastic Search have been focused of search and store unstructured data. But they have weak points. So, in this paper, we are going to propose new framework using term-based partitioning which can make up MongoDB and Elastic Search’s limitations.

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Gupta, Sangeeta, and Narsimha Gugulothu. "Secure NoSQL for the Social Networking and E-Commerce Based Bigdata Applications Deployed in Cloud." International Journal of Cloud Applications and Computing 8, no. 2 (April 2018): 113–29. http://dx.doi.org/10.4018/ijcac.2018040106.

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The work presented in this article brings into light the security issues with NoSQL databases- MongoDB, HBase and Cassandra. A literature survey is carried out to identify the modern world scenarios of the applications using NoSQL databases and limitations are identified. A solution is proposed by designing a framework to achieve security for the web crawler applications using Cassandra, a NoSQL data store. Experimental results are presented to show the effectiveness of the work by designing an appropriate algorithm to trigger security for scalable web crawler architecture. Amazon Web Services (AWS), a familiar cloud platform, and bitnami cloud hosting services are used to procure the required servers and virtual machines. Performance changes on the virtual machines are brought into consideration before and after encrypting and decrypting the voluminous data and an improvement in efficiency is observed with the proposed model.

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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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Srai*, Dr Aziz, Prof Fatima Guerouate, and Prof Hilal Drissi Lahsini. "The Integration of the MDA Approach in Document-Oriented NoSQL Databases, the case of Mongo DB." International Journal of Engineering and Advanced Technology 10, no. 3 (February 28, 2021): 115–22. http://dx.doi.org/10.35940/ijeat.c2235.0210321.

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Today with the growth of the internet, the use of social networks, mobile telephony, connected and communicating objects. The data has become so big, hence the need to exploit that data has become primordial. In practice, a very large number of companies specializing in the health sector, the banking and financial sector, insurance, manufacturing industry, etc… are based on traditional databases which are often well organized of customer data, machine data, etc ... but in most cases, very large volumes of data from these databases, and the speed with which they must be analyzed to meet the business needs of the company are real challenges. This article aims to respond to a problem of generating NoSQL MongoDB databases by applying an approach based on model-driven engineering (Model Driven Architecture Approach). We provide Model to Model (using the QVT model transformation language), and Model to Code transformations (using the code generator, Acceleo). We also propose vertical and horizontal transformations to demonstrate the validity of our approach on NoSQL MongoDB databases. We have studied in this article the PSM transformations towards the implementation. PIM to PSM transformations are the subject of another work.

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Ситник, Ніна Василівна, and Ірина Сергіївна Зінов'єва. "СУЧАСНІ БАЗИ ДАНИХ NoSQL У ПІДГОТОВЦІ БАКАЛАВРІВ СПЕЦІАЛЬНОСТІ "КОМП'ЮТЕРНІ НАУКИ"." Information Technologies and Learning Tools 81, no. 1 (February 23, 2021): 255–71. http://dx.doi.org/10.33407/itlt.v81i1.3098.

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Стаття присвячена проблемі опанування сучасними підходами організації баз даних майбутніми фахівцями зі спеціальності 122 «Комп’ютерні науки». Бази даних NoSQL і NewSQL – новітні тренди у сфері IT, які потребують детального ознайомлення та вивчення студентами комп’ютерних спеціальностей. У статті досліджено та розкрито поняття баз даних NoSQL і NewSQL. Проведена порівняльна характеристика реляційних баз даних з базами даних типу NoSQL, NewSQL. У зв’язку з поширенням різного виду WEBсервісів, потребою розподіленого оброблення великих масивів даних (Big Data) обґрунтовано необхідність повноцінного вивчення нового покоління баз даних та отримання фахових компетентностей щодо їх практичного застосування. Вивчення новітніх баз даних не має бути альтернативою вивченню реляційних баз даних. Враховуючи концептуальні відмінності і технологічні особливості використання сучасних баз даних, у Київському національному економічному університеті імені Вадима Гетьмана до навчальних планів спеціальності 122 «Комп’ютерні науки» додано нову дисципліну «Організація баз даних NoSQL», вивчення якої поповнить знання студентів про основні тенденції розвитку баз даних та підвищить їх конкурентоздатність на ринку праці. Під час розробки навчальної програми дисципліни були проаналізовані навчальні плани підготовки спеціалістів з комп’ютерних наук провідних закладів вищої освіти України, літературні та інтернет-джерела, які підтвердили необхідність та доцільність вивчення баз даних NoSQL та NewSQL. Дослідження критеріїв та аналіз рейтингу систем керування базами даних дозволили дійти висновку про доцільність введення до навчальної програми дисципліни «Організація баз даних NoSQL» тем з вивчення найбільш поширених у світовій практиці баз даних типу NoSQL (MongoDB, Redis, Cassandra) та NewSQL (Microsoft SQL Server 2017), яка має розширений функціонал і підтримує роботу з графами.

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Marrara, Stefania, Mauro Pelucchi, and Giuseppe Psaila. "Blind Queries Applied to JSON Document Stores." Information 10, no. 10 (September 21, 2019): 291. http://dx.doi.org/10.3390/info10100291.

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Social Media, Web Portals and, in general, information systems offer their own Application Programming Interfaces (APIs), used to provide large data sets concerning every aspect of day-by-day life. APIs usually provide data sets as collections of JSON documents. The heterogeneous structure of JSON documents returned by different APIs constitutes a barrier to effectively query and analyze these data sets. The adoption of NoSQL document stores, such as MongoDB, is useful for gathering these data sets, but does not solve the problem of querying the final heterogeneous repository. The aim of this paper is to provide analysts with a tool, named HammerJDB, that allows for blind querying collections of JSON documents within a NoSQL document database. The idea below is that users may know the application domain but it may be that they are not aware of the real structures of the documents stored in the database—the tool for blind querying tries to bridge the gap, by adopting a query rewriting mechanism. This paper is an evolution of a technique for blind querying Open Data portals and of its implementation within the Hammer framework, presented in some previous work. In this paper, we evolve that approach in order to query a NoSQL document database by evolving the Hammer framework into the HammerJDB framework, which is able to work on MongoDB databases. The effectiveness of the new approach is evaluated on a data set (derived from a real-life one), containing job-vacancy ads collected from European job portals.

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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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Jose, Benymol, and Sajimon Abraham. "Performance analysis of NoSQL and relational databases with MongoDB and MySQL." Materials Today: Proceedings 24 (2020): 2036–43. http://dx.doi.org/10.1016/j.matpr.2020.03.634.

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Mokrova, N. V., and A. M. Mokrov. "Software for implementing distance education technologies." Informacionno-technologicheskij vestnik 15, no. 1 (March 30, 2018): 120–26. http://dx.doi.org/10.21499/2409-1650-2018-1-120-126.

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The system of online broadcasting of multimedia presentations is proposed. The main advantages are the cross-platform solution and data processing speed. These features achieved due to the use of modern technologies as HTML5, JavaScript, NoSQL MongoDB database and PDF. Elaborated software product allows to broadcast using native browser. Users' authentication and the possibility of the author’s corrections on the fly are also represented to improve the quality of training.

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Calatrava, Carlos Garcia, Yolanda Becerra Fontal, Fernando M. Cucchietti, and Carla Diví Cuesta. "NagareDB: A Resource-Efficient Document-Oriented Time-Series Database." Data 6, no. 8 (August 13, 2021): 91. http://dx.doi.org/10.3390/data6080091.

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The recent great technological advance has led to a broad proliferation of Monitoring Infrastructures, which typically keep track of specific assets along time, ranging from factory machinery, device location, or even people. Gathering this data has become crucial for a wide number of applications, like exploration dashboards or Machine Learning techniques, such as Anomaly Detection. Time-Series Databases, designed to handle these data, grew in popularity, becoming the fastest-growing database type from 2019. In consequence, keeping track and mastering those rapidly evolving technologies became increasingly difficult. This paper introduces the holistic design approach followed for building NagareDB, a Time-Series database built on top of MongoDB—the most popular NoSQL Database, typically discouraged in the Time-Series scenario. The goal of NagareDB is to ease the access to three of the essential resources needed to building time-dependent systems: Hardware, since it is able to work in commodity machines; Software, as it is built on top of an open-source solution; and Expert Personnel, as its foundation database is considered the most popular NoSQL DB, lowering its learning curve. Concretely, NagareDB is able to outperform MongoDB recommended implementation up to 4.7 times, when retrieving data, while also offering a stream-ingestion up to 35% faster than InfluxDB, the most popular Time-Series database. Moreover, by relaxing some requirements, NagareDB is able to reduce the disk space usage up to 40%.

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FOTACHE, Marin, and Dragos COGEAN. "NoSQL and SQL Databases for Mobile Applications. Case Study: MongoDB versus PostgreSQL." Informatica Economica 17, no. 2/2013 (June 30, 2013): 41–58. http://dx.doi.org/10.12948/issn14531305/17.2.2013.04.

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Aniceto, Rodrigo, Rene Xavier, Valeria Guimarães, Fernanda Hondo, Maristela Holanda, Maria Emilia Walter, and Sérgio Lifschitz. "Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency." International Journal of Genomics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/502795.

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Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

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Nichie, Aaron, and Heung-Seo Koo. "A Comparison of Performance Between MSSQL Server and MongoDB for Telco Subscriber Data Management." Transactions of The Korean Institute of Electrical Engineers 65, no. 3 (March 1, 2016): 469–76. http://dx.doi.org/10.5370/kiee.2016.65.3.469.

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Zhapsarbek, N. B. "MODELING OF LARGE VOLUMES OF DATA WITH THE USE OF NoSQL." BULLETIN Series of Physics & Mathematical Sciences 69, no. 1 (March 10, 2020): 323–26. http://dx.doi.org/10.51889/2020-1.1728-7901.57.

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In the modern world, specialists and the information systems they create are increasingly faced with the need to store, process and move huge amounts of data. The definition of large amounts of data, Big Data, is used to denote technologies such as storing and analyzing large amounts of data that require high speed and real-time decision making during processing. In this case, large volumes, high accumulation rate, and the lack of a strict internal structure of "big data" are considered. All of this also means that classic relational databases are not well suited for storing them. In this article, we showed solutions for processing large amounts of data for pharmacy chains using NoSQL. This paper presents technologies for modeling large amounts of data using NoSQL, including MongoDB, and also analyzes possible solutions, limitations that do not allow this to be done effectively. This article provides an overview of three modern approaches to working with big data: NoSQL, DataMining and real-time processing of event flows. In this article, as an implementation of the studied methods and technology, we consider a database of pharmacies for processing, searching, analyzing, forecasting big data. Also, when using NoSQL, we showed work with structured and poorly structured data in parallel in different aspects and showed a comparative analysis of the newly developed application for pharmacy workers.

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Handika, I. Putu Susila, Gede Bagus Arya Tama, and Ni Putu Mega Krisnayanti. "PENERAPAN TEKNOLOGI DATAWAREHOUSE NOSQL DAN BUSINESS INTELLIGENCE UNTUK ANALISA TRANSAKSI PENJUALAN." Jurnal RESISTOR (Rekayasa Sistem Komputer) 3, no. 2 (November 6, 2020): 120–27. http://dx.doi.org/10.31598/jurnalresistor.v3i2.626.

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Business process are increasingly developing, causing data generated will be even greater. The application of datawarehouse technology and MongoDB as one of the NoSQL technologies can solve large historical data processing problems. In addition, business intelligence technology that is able to visualize data in into graphics and tables will make it easier for stakeholders to analyze sales transactions. The process for transforming transaction data into a datawarehouse is the Extract Transform Load (ETL) process. The data generated from the ETL process is quality data and is really needed for analysis. Black box testing results and data validity indicate the system can be accepted and can produce the right information for decision support.

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Almeida, Arthur Lorenzi, Vinícius Junqueira Schettino, Thiago Jesus Rodrigues Barbosa, Pedro Fernandes Freitas, Pedro Gabriel Silva Guimarães, and Wagner Arbex. "Relative Scalability of NoSQL Databases for Genotype Data Manipulation." Revista de Informática Teórica e Aplicada 25, no. 2 (July 17, 2018): 93. http://dx.doi.org/10.22456/2175-2745.79334.

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Genotype data manipulation is one of the greatest challenges in bioinformatics and genomics mainly because of high dimensionality and unbalancing characteristics. These peculiarities explains why Relational Database Management Systems (RDBMSs), the "de facto" standard storage solution, have not been presented as the best tools for this kind of data. However, Big Data has been pushing the development of modern database systems that might be able to overcome RDBMSs deficiencies. In this context, we extended our previous works on the evaluation of relative performance among NoSQLs engines from different families, adapting the schema design in order to achieve better performance based on its conclusions, thus being able to store more SNP markers for each individual. Using Yahoo! Cloud Serving Benchmark (YCSB) benchmark framework, we assessed each database system over hypothetical SNP sequences. Results indicate that although Tarantool has the best overall throughput, MongoDB is less impacted by the increase of SNP markers per individual.

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Shichkina, Yulia, and Muon Ha. "Creating Collections with Embedded Documents for Document Databases Taking into Account the Queries." Computation 8, no. 2 (May 15, 2020): 45. http://dx.doi.org/10.3390/computation8020045.

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In this article, we describe a new formalized method for constructing the NoSQL document database of MongoDB, taking into account the structure of queries planned for execution to the database. The method is based on set theory. The initial data are the properties of objects, information about which is stored in the database, and the set of queries that are most often executed or whose execution speed should be maximum. In order to determine the need to create embedded documents, our method uses the type of relationship between tables in a relational database. Our studies have shown that this method is in addition to the method of creating collections without embedded documents. In the article, we also describe a methodology for determining in which cases which methods should be used to make working with databases more efficient. It should be noted that this approach can be used for translating data from MySQL to MongoDB and for the consolidation of these databases.

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Journal articles on the topic 'NoSQL MongoDB'

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