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Krapans, Jānis, and Sergejs Kodors. "NOSQL DATABASES." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 25 (April 23, 2021): 55–58. http://dx.doi.org/10.17770/het2021.25.6780.
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Darbā ir aprakstīts trīs NoSQL datubāžu salīdzinājums: Mango, CouchBase un Cassandra. Datubāžu salīdzinājuma dati tika iegūti izmantojot YCSB- programmu, kura noslogo datubāzi ar dažādām darba slodzēm, kuras atbilst mūsdienu moderno aplikāciju prasībām. Testu izpildes beigās tiks iegūti katras datubāzes veiktspējas dati, kā arī tās latentums
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Lalwani, Raj. "NOSQL Databases." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 877–82. http://dx.doi.org/10.22214/ijraset.2018.6133.
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Tsai, Chia-Ping, Che-Wei Chang, Hung-Chang Hsiao, and Haiying Shen. "The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase." Future Internet 14, no. 3 (March 15, 2022): 92. http://dx.doi.org/10.3390/fi14030092.
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Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.
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Ashwaq A. Alotaibi, Reem M. Alotaibi and Nermin Hamza, Ashwaq A. Alotaibi, Reem M. Alotaibi and Nermin Hamza. "Access Control Models in NoSQL Databases: An Overview." journal of king abdulaziz university computing and information technology sciences 8, no. 1 (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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Aftab, Zain, Waheed Iqbal, Khaled Mohamad Almustafa, Faisal Bukhari, and Muhammad Abdullah. "Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping." Scientific Programming 2020 (July 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8813350.
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Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.
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Chaudhary, Renu, and Gagangeet Singh. "A NOVEL TECHNIQUE IN NoSQL DATA EXTRACTION." International Journal of Research -GRANTHAALAYAH 1, no. 1 (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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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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<em>Flight ticket booking services have become more advanced, where bookings can be made through the android / iOS application and through a web browser, ticket reservations, no longer have to come to travel agents or come to the airport to book plane tickets. In this study using an online ticket booking database where one database uses the NoSQL database and another database uses SQL Server. The purpose of this research is to test the performance of NoSQL speed with SQL Server with the Insert, Delete and Select commands. The testing method uses 100 records, 500 records, 1000 records, and 5000 records, with each record being tested four times and then taken on average. The results of this study are that the NoSQL database Insert command has a speed 4 times faster than the SQL Server database for under 500 records, whereas above 500 NoSQL database records 5 times slower, the Delete NoSQL database command has a speed 3 times faster than the SQL database Server, and the command Select 1 NoSQL database table 55 times faster than SQL Server databases, while 2 NoSQL database tables are 18 times slower than SQL Server databases, while 3 NoSQL database tables are 10 times slower than SQL Server databases, whereas 4 database tables NoSQL is 16 times slower than SQL Server databases.</em>
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Koutroumanis, Nikolaos, Nikolaos Kousathanas, Christos Doulkeridis, and Akrivi Vlachou. "A demonstration of NoDA." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2851–54. http://dx.doi.org/10.14778/3476311.3476361.
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In this demo paper, we present a system prototype, called NoDA, that unifies access to NoSQL stores, by exposing a single interface to big data developers. This hides the heterogeneity of NoSQL stores, in terms of different query languages, non-standardized access, and different data models. NoDA comprises a layer positioned on top of NoSQL stores that defines a set of basic data access operators (filter, project, aggregate, etc.), implemented for different NoSQL engines. The provision of generic data access operators enables a declarative interface using SQL as query language. Furthermore, NoDA is extended to provide more complex operators, such as geospatial operators, which are only partially supported by NoSQL stores. We demonstrate NoDA by showcasing that the exact same query can be processed by different NoSQL stores, without any modification or transformation whatsoever.
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Shah, Monika, Amit Kothari, and Samir Patel. "A Comprehensive Survey on Energy Consumption Analysis for NoSQL." Scalable Computing: Practice and Experience 23, no. 1 (April 25, 2022): 35–50. http://dx.doi.org/10.12694/scpe.v23i1.1971.
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During the last few years, we are witnessing increasing development in the Internet of Things (IoT) and big data. To address increasing workload complexity with better performance and to handle scalability issues of such applications, non-relational (NoSQL) has started taking the place of relational databases. With increasing load, it is challenging to maintain NoSQL’s performance, scalability, and availability without expanding the capacity of hosts and power budget of computing resources. Future scaling of data center capabilities depends on the improvement of server power efficiency. Considering the rise of energy costs and environmental sustainability, we can not ignore this high energy consumption caused by NoSQL. Despite the increasing popularity and share of NoSQL in the software market, little is still known about its energy footprint. To the best of our knowledge, there are no comprehensive studies that analyze the energy consumption by various modules of NoSQL. This article, therefore, conducts a comprehensive survey on the energy consumption analysis of NoSQL. There are limited proposals to reduce the energy consumption of NoSQL. This paper also provides a brief description of these little efforts on reducing the energy consumption of NoSQL. Based on the review, this paper discusses the research scope and opportunities for researchers to improve the energy conservation of NoSQL systems.
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BenAli-Sougui, Ines, Minyar Sassi Hidri, and Amel Grissa-Touzi. "No-FSQL." International Journal of Fuzzy System Applications 5, no. 2 (April 2016): 54–63. http://dx.doi.org/10.4018/ijfsa.2016040104.
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NoSQL (Not only SQL) is an efficient database model for storing and manipulating huge quantities of precise data. However, most NoSQL databases scale well as data grows and often are flexible enough to accommodate imprecise and ambiguous data. This comprehensive hands-on guide presents fundamental concepts and practical solutions for using fuzziness with NoSQL to deals with fuzzy databases (FDB). In this paper, the authors present a graph-based fuzzy NoSQL model to deal with large fuzzy databases while extending the NoSQL one. The authors consider the cypher declarative query language proposed for Neo4j which is the current leader on this market to querying fuzzy databases.
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Saundatt, Sujay i. "Databases In The 21’st Century." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1440–44. http://dx.doi.org/10.22214/ijraset.2022.43982.
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Abstract: NoSQL databases are the 21’st century databases created to defeat the disadvantages of RDBMS. The objective of NoSQL is to give versatility, accessibility and meet different necessities of distributed computing.The main motivations for NoSQL databases systems are achieving scalability and fail over needs. In the vast majority of the NoSQL data set frameworks, information is parceled and repeated across numerous hubs. Innately, the majority of them utilize either Google's MapReduce or Hadoop Distributed File System or Hadoop MapReduce for information assortment. Cassandra, HBase and MongoDB are for the most part utilized and they can be named as the agent of NoSQL world.
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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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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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Khan, Wisal, Teerath Kumar, Cheng Zhang, Kislay Raj, Arunabha M. Roy, and Bin Luo. "SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review." Big Data and Cognitive Computing 7, no. 2 (May 12, 2023): 97. http://dx.doi.org/10.3390/bdcc7020097.
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The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.
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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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Maharani, Anggita. "Perancangan Data Base Kasir Menggunakan MongoDB." Jurnal Data Mining dan Sistem Informasi 3, no. 1 (February 27, 2022): 19. http://dx.doi.org/10.33365/jdmsi.v3i1.1939.
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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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Maharani, Anggitaseptia. "PERANCANGAN DATA BASE KASIR DAN PERSEDIAAN BARANG MENGGUNAKAN MONGODB." Jurnal Data Mining dan Sistem Informasi 3, no. 1 (February 27, 2022): 32. http://dx.doi.org/10.33365/jdmsi.v3i1.1941.
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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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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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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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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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Abdel-Fattah, Manal A., Wael Mohamed, and Sayed Abdelgaber. "A Comprehensive Spark-Based Layer for Converting Relational Databases to NoSQL." Big Data and Cognitive Computing 6, no. 3 (June 27, 2022): 71. http://dx.doi.org/10.3390/bdcc6030071.
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Currently, the continuous massive growth in the size, variety, and velocity of data is defined as big data. Relational databases have a limited ability to work with big data. Consequently, not only structured query language (NoSQL) databases were utilized to handle big data because NoSQL represents data in diverse models and uses a variety of query languages, unlike traditional relational databases. Therefore, using NoSQL has become essential, and many studies have attempted to propose different layers to convert relational databases to NoSQL; however, most of them targeted only one or two models of NoSQL, and evaluated their layers on a single node, not in a distributed environment. This study proposes a Spark-based layer for mapping relational databases to NoSQL models, focusing on the document, column, and key–value databases of NoSQL models. The proposed Spark-based layer comprises of two parts. The first part is concerned with converting relational databases to document, column, and key–value databases, and encompasses two phases: a metadata analyzer of relational databases and Spark-based transformation and migration. The second part focuses on executing a structured query language (SQL) on the NoSQL. The suggested layer was applied and compared with Unity, as it has similar components and features and supports sub-queries and join operations in a single-node environment. The experimental results show that the proposed layer outperformed Unity in terms of the query execution time by a factor of three. In addition, the proposed layer was applied to multi-node clusters using different scenarios, and the results show that the integration between the Spark cluster and NoSQL databases on multi-node clusters provided better performance in reading and writing while increasing the dataset size than using a single node.
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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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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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Yusuf, Roby Firnando, and Daniel Rudiaman Sijabat. "Optimasi Serangan Blind NoSQL Injection Dengan Pendekatan Algoritma Binary Search." J-INTECH 11, no. 2 (December 22, 2023): 244–56. http://dx.doi.org/10.32664/j-intech.v11i2.980.
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NoSQL Injection adalah salah satu jenis serangan pada Database Management System (DBMS) NoSQL. Serangan ini dapat dimaanfaatkan oleh attacker dengan cara mengirim request arbitrary code ke server database. Apabila server memberikan respon error query atau query tidak valid, attacker akan melakukan manipulasi pada query. Proses melakukan Blind NoSQL Injection itu cukup rumit. Akibatnya, Pentester seringkali membutuhkan waktu yang lama untuk dapat memperoleh informasi dan menembus server database. Berdasarkan permasalahan tersebut, penelitian ini akan memberikan solusi dengan mengembangkan tool untuk mengotomasi serangan Blind NoSQL Injection. Hasil penelitian ini menunjukkan bahwa pengembangan tool exploit dapat meningkatkan kinerja dan efisiensi. Algoritma binary search memiliki keunggulan waktu yang lebih singkat pada parameter runtime dibandingkan linear search, sehingga binary search lebih efektif digunakan. Selain itu, pendekatan mitigasi dengan sanitasi dan validasi input pada setiap key object terbukti efektif dalam mencegah serangan NoSQL Injection.
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Razu Ahmed, Md, Mst Arifa Khatun, Md Asraf Ali, and Kenneth Sundaraj. "A literature review on NoSQL database for big data processing." International Journal of Engineering & Technology 7, no. 2 (June 5, 2018): 902. http://dx.doi.org/10.14419/ijet.v7i2.12113.
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Objective: Aim of the present study was to literature review on the NoSQL Database for Big Data processing including the structural issues and the real-time data mining techniques to extract the estimated valuable information.Methods: We searched the Springer Link and IEEE Xplore online databases for articles published in English language during the last seven years (between January 2011 and December 2017). We specifically searched for two keywords (“NoSQL” and “Big Data”) to find the articles. The inclusion criteria were articles on the use of performance comparison on valuable information processing in the field of Big Data through NoSQL databases.Results: In the 18 selected articles, this review identified 8 articles which provided various suitable recommendations on NoSQL databases for specific area focus on the value chain of Big Data, 5 articles described the performance comparison of different NoSQL databases, 2 articles presented the background of basics characteristics data model for NoSQL, 1 article denoted the storage in respect of cloud computing and 2 articles focused the transactions of NoSQL.Conclusion: In this literature, we presented the NoSQL databases for Big Data processing including its transactional and structural issues. Additionally, we highlight research directions and challenges in relation to Big Data processing. Therefore, we believe that the information contained in this review will incredible support and guide the progress of the Big Data processing.
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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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Utomo, Muhammad Nur Yasir. "Pengembangan Model Migrasi Database Relational ke NoSQL Memanfaatkan Metadata SQL." Jurnal Teknologi Elekterika 4, no. 2 (December 17, 2020): 1. http://dx.doi.org/10.31963/elekterika.v4i2.2212.
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Penyimpanan data merupakan isu krusial pada teknologi Big Data karena membutuhkan teknologi penyimpanan data yang profisien agar dapat menyimpan data (terstruktur dan tidak terstruktur) secara cepat dalam jumlah besar. Hal ini sudah tidak bisa lagi dipenuhi oleh model database relational (SQL) yang saat ini masih banyak digunakan. Kelemahan tersebut dapat diatasi dengan menggunakan database NoSQL, namun sayangnya proses migrasi data dari relational/SQL database ke NoSQL masih sulit dilakukan karena perbendaan skema dan format penyimpanan data. Berdasarkan masalah tersebut, maka penelitian mengenai migrasi database relational ke NoSQL sangat diperlukan. Penelitian ini mencoba mengajukan pengembangan model perangkat lunak untuk migrasi database relational ke NoSQL menggunakan pendekatan aturan migrasi dan data transformasi yang memanfaatkan metadata SQL. Berdasarkan eksperimen yang telah dilakukan aturan migrasi yang diterapkan pada model yang dikembangkan berhasil melakuakn migrasi database SQL ke NoSQL dengan kecepatan rerata 0.978 detik untuk 5 table dalam 1 database.
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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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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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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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Muhammad A. Lawal and Mostaf A. Saleh, Muhammad A. Lawal and Mostaf A. Saleh. "Security Testing Tool for NoSQL Systems." journal of king abdulaziz university computing and information technology sciences 8, no. 1 (April 5, 2019): 85–93. http://dx.doi.org/10.4197/comp.8-1.8.
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NoSQL systems are becoming more popular due to their inherent advantages and solutions it provides to the limits of a relational database. However, despite its benefits, it comes with security challenges. In this paper, an input validation mechanism architecture is proposed for Mongo DB to detect and prevent NoSQL injection attacks, the mechanism employs a Deterministic Finite Automaton (DFA) approach to detect and prevent attacks on NoSQL systems. Furthermore, a security comparison of some NoSQL systems is provided based on recent literature. The security features compared are authentication, authorization, data encryption and input validation. The proposed mechanism will improve the security of Mongo DB system because invalid inputs requests will be detected and prevented from being processed.
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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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<span>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.</span>
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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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Abdi, Muhammad Firdaus, Agung Susanto, and Kusnawi Kusnawi. "Perbandingan Kecepatan Pencarian Data SQL dan NOSQL." Jurnal Teknologi Informasi 5, no. 1 (June 29, 2021): 7–11. http://dx.doi.org/10.36294/jurti.v5i1.1696.
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Abstract - The development of database technology requires a quality of data, where a database is needed in various applications that support the performance of a company or office, so that the database is said to be good, it must be managed appropriately, efficiently in terms of speed or time. For this reason, the use of this database is still the prima donna used in running a system. In this study, testing a database to determine the query response time from a Structured Query Language (SQL) and NoSQL (Non-SQL) system which was tested with a sales or sales database where the data records were (100,500,1000,100000, 2279879). Testing focused on data search includes queries displaying all data, one of the data, AND, OR, AND and OR, Order By (asc), and Order By (desc). The purpose of this research is to produce a comparative analysis of the query response time between SQL and NoSQL so that it is known which database is superior in a data search for use. The results of this study indicate that NoSQL is superior in searching for a database, but there are certain NoSQL searches that are slower than the SQL search process. Keywords - NoSQL, SQL, Response Time, Search Data, Comparison Base Data. Abstrak – Perkembangan teknologi basis data dituntut sebuah kualitas sebuah data, yang dimana sebuah basis data sangat diperlukan di berbagai aplikasi penunjang kinerja sebuah perusahaan atau kantor, untuk itu agar basis data dikatakan baik harus dikelola dengan tepat, efesien dari segi kecepatan atau waktu. Untuk itu penggunaan basis data ini masih menjadi primadona digunakan dalam menjalankan sebuah sistem. Pada penelitian ini melakukan pengujian sebuah database untuk menentukan waktu query response dari sebuah sistem Structured Query Language (SQL) dan NoSQL(Non SQL) yang diujikan dengan database sales atau penjualan yang dimana record data (100,500,1000,100000, 2279879). Pengujian terfokus pada pencarian data meliputi query tampil seluruh data, salah satu data, AND, OR, AND dan OR, Order By (asc), dan Order By (desc). Tujuan penelitian ini menghasilkan analisis perbandingan waktu query response antara SQL dan NoSQL agar diketahui basis data yang lebih unggul dalam sebuah pencarian data untuk digunakan. Hasil penelitian ini didapat bahwa NoSQL lebih unggul dalam pencarian sebuah datam namun ada pencarian tertentu NoSQL lebih lambat dari proses pencarian SQL. Kata Kunci - NoSQL,SQL, Response Time,Pecarian Data,Perbandingan Basis Data.
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Kotsilieris, Theodore. "An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications." Healthcare 9, no. 3 (March 13, 2021): 322. http://dx.doi.org/10.3390/healthcare9030322.
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Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape such innovative HIT architectures include: Mobile agents (Mas) and NoSQL technologies. Mobile agents provide an inherent way of tackling distributed problems of accessing heterogeneous and spatially diverse data sources. NoSQL technology gains ground for the development of scalable applications with non-static and open data schema from complex and diverse sources. Methods and Design: This paper conducts a twofold study: It attempts a literature review of the applications based on the mobile agent (MA) and NoSQL technologies for healthcare support services. Subsequently, a pilot system evaluates the NoSQL technology against the relational one within a distributed environment based on mobile agents for information retrieval. Its objective is to study the feasibility of developing systems that will employ ontological data representation and task implementation through mobile agents towards flexible and transparent health data monitoring. Results and Discussion: The articles studied focus on applying mobile agents for patient support and healthcare services provision thus as to make a positive contribution to the treatment of chronic diseases. In addition, attention is put on the design of platform neutral techniques for clinical data gathering and dissemination over NoSQL. The experimental environment was based on the Apache Jena Fuseki NoSQL server and the JAVA Agent DEvelopment Framework -JADE agent platform. The results reveal that the NoSQL implementation outperforms the standard relational one.
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Kumar, M. Sandeep, and Prabhu .J. "Comparison of NoSQL Database and Traditional Database-An emphatic analysis." JOIV : International Journal on Informatics Visualization 2, no. 2 (March 3, 2018): 51. http://dx.doi.org/10.30630/joiv.2.2.58.
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A Huge amount of data is manipulated by using the web application, Facebook, Twitter, social sites etc. Most of the data are unstructured data. It is not desirable for storing, performing and analyzing data in the relational database for huge data. It affords way towards performing NoSQL database and uses fully for handling the big data. In this paper, we present the performance in store and query operation in NoSQL database, estimating the performance of both reads and write operation using simple and complex queries. Result represents that comparing Cassandra with relation database, Cassandra outperforms the relation database. Most of the organization used only Hbase and Cassandra for benefit of cost. Comparison Various NoSQL Database, issues while performing NoSQL database.
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Boya Marqas, Ridwan, Saman M. Almufti, and Renas Rajab Asaad. "Data Exchange with CSV Files in PHP-Based Websites using FIREBASE." Academic Journal of Nawroz University 11, no. 3 (August 26, 2022): 410–14. http://dx.doi.org/10.25007/ajnu.v11n3a1480.
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A database is a collection of data that can be classified into two types: SQL (structure query language), which is associated with relational databases, and NoSQL (non-relational SQL), which is associated with distributed databases. As the world becomes increasingly technologically advanced and computerized, the amount of information grows exponentially, resulting in data. In the majority of studies, the NoSQL database is referred to as "NoSQL." Firebase, a NoSQL database, is used to exchange data with a CSV file on a php-based website in this study. Two CSV files of 1000 and 4997 records were used for both the importing and exporting processes to collect the experimental results. Firebase's speed was examined by exchanging csv files with a php-based website in this research.
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Ben Messaoud, Ines, Abdulrahman A. Alshdadi, and Jamel Feki. "Building a Document-Oriented Warehouse Using NoSQL." International Journal of Operations Research and Information Systems 12, no. 2 (April 2021): 33–54. http://dx.doi.org/10.4018/ijoris.20210401.oa3.
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The traditional data warehousing approaches should adapt to take into consideration novel needs and data structures. In this context, NoSQL technology is progressively gaining a place in the research and industry domains. This paper proposes an approach for building a NoSQL document-oriented warehouse (DocW). This approach has two methods, namely 1) document warehouse builder and 2) NoSQL-Converter. The first method generates the DocW schema as a galaxy model whereas the second one translates the generated galaxy into a document-oriented NoSQL model. This relies on two types of rules: structure and hierarchical rules. Furthermore, in order to help understanding the textual results of analytical queries on the NoSQL-DocW, the authors define two semantic operators S-Drill-Up and S-Drill-Down to aggregate/expand the terms of query. The implementation of our proposals uses MangoDB and Talend. The experiment uses the medical collection Clef-2007 and two metrics called write request latency and read request latency to evaluate respectively the loading time and the response time to queries.
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F, Apriandi Syafiq, Rifki Arif Rahman, Rakha Dighdaya Putra, and Irpan Saputra. "Mengoptimalkan Analisis Big Data Melalui Implementasi Teknologi Basisdata Nosql." Karimah Tauhid 3, no. 2 (February 6, 2024): 2565–82. http://dx.doi.org/10.30997/karimahtauhid.v3i2.11946.
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Meningkatnya volume dan keragaman data yang diperoleh dari berbagai sumber mendorong kebutuhan akan teknologi database yang mampu menangani Big Data. Penelitian ini bertujuan untuk mengoptimalkan analisis Big Data dengan menerapkan teknologi database NoSQL. Metode penelitian ini melibatkan analisis literatur tentang karakteristik dan keunggulan teknologi NoSQL, pengembangan model implementasi, dan evaluasi kinerja berbasis kasus. Hasil penelitian menunjukkan bahwa penerapan teknologi database NoSQL memungkinkan analisis Big Data lebih efisien dan memfasilitasi skema penyimpanan yang lebih fleksibel dan terukur.
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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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Banerjee, Shreya, Sourabh Bhaskar, Anirban Sarkar, and Narayan C. Debnath. "A Unified Conceptual Model for Data Warehouses." Annals of Emerging Technologies in Computing 5, no. 5 (March 20, 2021): 162–69. http://dx.doi.org/10.33166/aetic.2021.05.020.
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These days, NoSQL (Not only SQL) databases are being used as a deployment tool for Data Warehouses (DW) due to its support for dynamic and scalable data modeling capabilities. Yet, decision-makers have faced several challenges to accept it as a major choice for implementation of their DW. The most significant one among those challenges is a lack of common conceptual model and a systematic design methodology for different NoSQL databases. The objective of this paper is to resolve these challenges by proposing an ontology based formal conceptual model for NoSQL based DWs. These proposed concepts are capable of realizing the cube concepts for visualization of multi-dimensional data in NoSQL based DW solutions. In this context, two strategies are specified, implemented and illustrated using a case study for devising of the proposed conceptual model.
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Sholeh, Muhammad, RR Yuliana Rachmawati, and Erma Susanti. "Pemodelan Basis data Graph dengan Neo4j (Studi Kasus : Basis Data Sistem Informasi Penjualan pada UMKM)." Jurnal Teknologi Informasi dan Terapan 7, no. 1 (June 11, 2020): 25–32. http://dx.doi.org/10.25047/jtit.v7i1.129.
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Penelitian ini mengimplementasikan penyimpanan data dengan menggunakan basis data graph. Basis data graph merupakan salah satu kategori dari basis data noSQL. Dalam basis data model SQL data dibentuk dalam tabel –tabel yang terdiri dari baris dan kolom, sedangkan pada basis data model NoSQL data tidak memiliki skema standar yang harus didefinisikan. NoSql merupakan sistem manajemen basis data yang tidak mempunyai atau mematuhi aturan tertentu seperti pada model sistem manajemen basis data relasional. NoSQL memiliki skema yang dinamis sedangkan pada database SQL mengikuti skema yang telah ditetapkan pada awal perancangan. Pengembangan basis data graph sistem penjualan UMKM ini dilakukan dengan mengembangkan terlebih dahulu dalam konseptual dan membandingkan dengan membuat terlebih dahulu model database relasional dan model database graph. Hasil penelitian ini menghasilkan basis data graph yang mengelola data-data penjualan serta menghubungkan berbagai simpul yang
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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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Panwar, Avnish. "Data migration from SQL to NoSQL using snapshot- Livestream migration." Mathematical Statistician and Engineering Applications 70, no. 2 (February 26, 2021): 1600–1608. http://dx.doi.org/10.17762/msea.v70i2.2450.
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The process of moving data from a source database to a destination database is known as data migration. For a variety of reasons, including higher data handling capacity, improved speed, and scalability, many businesses are choosing to convert their databases from one kind (e.g., RDBMS) to another (e.g., NoSQL). Sqoop [3], mongoimport [2], and mongify [1] are a few techniques and technologies that have been developed to help with this transition from RDBMS to NoSQL databases. NoSQL databases use different models, as opposed to the relational model employed by RDBMS, including document, graph, and key-value. Large data volumes were the main focus of the design of NoSQL databases. The database migration model we provide in this paper can effectively transfer both real-time and historical data in parallel. Our Java-based model focuses on transferring data from MongoDB, a document-oriented NoSQL database, to MySQL, an RDBMS. The prototype we created can migrate both live data and a snapshot of the database at a particular moment in time simultaneously. Our experimental evaluation shows that, in terms of performance for both snapshot and live data migration, our model beats competing approaches.
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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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Antonielli Scardoelli, Keren, and Giuliano Scombatti Pinto. "BANCO DE DADOS NOSQL." Revista Interface Tecnológica 17, no. 2 (December 18, 2020): 219–30. http://dx.doi.org/10.31510/infa.v17i2.949.
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O modelo de banco de dados relacional tem sido muito utilizado no mercado pelos seus pontos fortes, mas com o surgimento da Web, empresas tiverem que começar a trabalhar com uma grande demanda de dados. Com isso, o modelo relacional acabou apresentando limitações em relação a escalabilidade, flexibilidade e disponibilidade. Este trabalho tem como objetivo apresentar algumas características dos bancos de dados relacionais e não relacionais, focando nos elementos do banco de dados não relacionais, e apresentar algumas grandes empresas que utilizam o modelo não relacional. A metodologia utilizada foi a pesquisa bibliográfica. Por meio das pesquisas realizadas, foi possível identificar que a escolha do melhor banco de dados depende da aplicação que será feita. O modelo não relacional não veio substituir o modelo relacional, mas para ser utilizado somente em ocasiões que o modelo relacional não conseguir atender determinadas necessidades.
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Schaeffer, Donna M., and Patrick C. Olson. "“NoSQL” And Service Science." Journal of Service Science (JSS) 5, no. 2 (December 28, 2012): 65–70. http://dx.doi.org/10.19030/jss.v5i2.7575.
Full textAbstract:
Discussions of “NoSQL” naturally tend to contrast a new approach with a tired old approach. An example is the discussion in Linux Journal (Batholomew, 2010) which focused on differentiating between these new “nonrelational” products and “traditional” (relational) systems. While enthusiasm for the new (and the unjustified raised expectations) has often been the hallmark of information systems and computing, in this instance the enthusiasm may be providing a poor and misleading explanation. A more careful explanation is needed, particularly as these approaches and products may be needed to provide the realization of many promising ideas such as those associated as with Web Services.
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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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Kuznetsov, S. D., and A. V. Poskonin. "NoSQL data management systems." Programming and Computer Software 40, no. 6 (November 2014): 323–32. http://dx.doi.org/10.1134/s0361768814060152.
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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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