Non-relational Database Management Systems Research Articles

MogoDB: A NoSQL Database with Amazing Advantages and Features

The extremely compressed data stores on non-relational database management systems, commonly referred to as NoSQL data stores, which facilitate the management of data for internet user programs, do not now provide this feature. Some of the words that come to mind are data security, encryption methods, MongoDB, and NoSQL. Without negatively compromising the database's performance in terms of speed or memory utilization, the appropriate encryption methods utilized to various essential data fields provide data protection. This is possible because the right encryption methods are being used. Databases are often used to manage different types of data since document-oriented and unstructured data are typically stored there. These databases include Cassandra, MongoDB, CouchDB, Redis, Hyper-table, and others. They must provide strong security and safeguard users' private data when it is in use or at rest because they are open source. There is a need for a single solution that may enhance data transmission security by offering an improved encryption technique that expedites operations and consumes less memory when managing databases.Today's IT growth is attributed to the development of more data-intensive applications. Due to stringent limitations on data structure, data relations, and other factors, relational databases do not allow us to work with huge data sets or maintain high volume databases. Unstructured data from many industries, including a variety of forms, must be processed and stored in databases. Thus, NoSQL types offer solutions to a number of problems associated with huge data bases. Largely because of benefits like flexibility and horizontal scalability, NOSQL (Not Only SQL) is in demand. This essay explores many NoSQL database types, such as MongoDB, CouchDB, HBase, Cassandra, etc.

An Art of Handling NoSQL Databases with Respect to Big Data

The cyber-physical world's computer and communication capacity is rapidly growing. To handle these tasks, a lot of data is consequently produced. The four main problems with big data are volume, diversity, velocity, and authenticity. Hadoop is one example of a storage-based data processing system that controls volume and diversity. However, an unduly complex method is necessary to process such a large number of data quickly and accurately.Structured data cannot be effectively managed using typical relational databases, which are extensively used in information management systems. NoSQL, a non-relational database management system for unstructured data, is a novel technology that was developed to handle unstructured data. Scalability is what drives NoSQL the most. One application of big data is in the field of medicine. Every person's data collection yields a significant amount of information quickly. Early detection, diagnosis, and the recommendation of efficient medications can all be achieved through the analysis of stored healthcare data through querying. The application must explicitly encrypt any sensitive data before writing it to the database in order to offer security. Additionally, joining operations are avoided while querying the NoSQL database MongoDB, resulting in effective data retrieval. NoSQL, MongoDB, Big Data, Security, Healthcare Record.

MogoDB: A NoSQL Database with Amazing Advantages and Features

The extremely compressed data stores on non-relational database management systems, commonly referred to as NoSQL data stores, which facilitate the management of data for internet user programs, do not now provide this feature. Some of the words that come to mind are data security, encryption methods, MongoDB, and NoSQL. Without negatively compromising the database's performance in terms of speed or memory utilization, the appropriate encryption methods utilized to various essential data fields provide data protection. This is possible because the right encryption methods are being used. Databases are often used to manage different types of data since document-oriented and unstructured data are typically stored there. These databases include Cassandra, MongoDB, CouchDB, Redis, Hyper-table, and others. They must provide strong security and safeguard users' private data when it is in use or at rest because they are open source. There is a need for a single solution that may enhance data transmission security by offering an improved encryption technique that expedites operations and consumes less memory when managing databases.Today's IT growth is attributed to the development of more data-intensive applications. Due to stringent limitations on data structure, data relations, and other factors, relational databases do not allow us to work with huge data sets or maintain high volume databases. Unstructured data from many industries, including a variety of forms, must be processed and stored in databases. Thus, NoSQL types offer solutions to a number of problems associated with huge data bases. Largely because of benefits like flexibility and horizontal scalability, NOSQL (Not Only SQL) is in demand. This essay explores many NoSQL database types, such as MongoDB, CouchDB, HBase, Cassandra, etc.

"Using MongoDB to Understand the Underlying Methods Techniques Encryption in NoSQL database"

There is a need for appropriate encryption methods, which must be adhered by the concerned parties, in order to provide a high level of security for the secret data. In this study, an examination of the different encryption algorithms and their performance in the management of private data with authentication, access control, secure configuration, and data encryption is presented. It includes enhancing MongoDB's level-based access protected model and adding privacy keys for security and monitoring purposes respectively. The NoSQL data stores, also known as highly compressed data on non-relational database management systems, which provide support for data management of internet user programs, do not now offer this service. Data security, encryption techniques, MongoDB, and NoSQL are some of the keywords that come to mind.The proper encryption methods used to various crucial data fields provide data protection without adversely affecting the database's performance in terms of speed or memory usage. Due to the use of appropriate encryption techniques, this is achievable. Since document-oriented and unstructured data are generally kept in databases, it is frequently used to handle various sorts of data. Examples of such databases include MongoDB, Cassandra, CouchDB, Redis, Hyper-table, and others. Since they are open source, there is a critical necessity to offer good security and protect the user's private information when it is in transit or at rest. There is a need for a single solution that can improve the security of data transfer by providing an improved encryption method that speeds up processes and uses less memory when maintaining databases.

Non-relational approach to developing knowledge bases of expert system prototype

Purpose. Use of a non-relational database management system is proposed while developing a database of a prototype of expert system with using a semantic model of the knowledge. Methodology. The study compares traditional relational approach with the proposed non-relational one in terms of the formation of certain queries. The following indices are used to compare efficiency of two management systems for the databases: particular query set (in MySQL and Cypher languages); runtime for the specified record size (i.e. their processing speed); ease of understanding: and software support of the queries. Findings. It has been identified that the graph model is a more expedient solution in the process of designing semantic networks and their development where complex hierarchical relationships between objects have to be stored and processed. Architecture of the graph database has been applied in terms of the specific example. A prototype of an expert system has been developed to demonstrate the capabilities of the created system of logical inference. The classifier of sciences was chosen as an example in the subject area. Originality. A prototype of the expert system, using the proposed non-relational approach, has been designed involving modern service-oriented architecture (SOA). The abovementioned helped separate the database from the inference engine and the user interface, facilitate perception as well as update and code debugging. Service-oriented architecture makes the system more flexible and robust. Practical value. The developed software is meant to develop both simple expert systems and medium-complex ones.

SPATIAL DATABASES OF AGRONOMIC GEOINFORMATION SYSTEMS

The work is devoted to the development of spatial databases for the assessment of agricultural land. The geodatabase is aimed at geoinformation support of assessment, agroecological typing (groups, types of land) of agricultural lands, development of adaptive-landscape farming systems. The paper presents the structure of the database of regional and local levels. Three different ways of user interaction with the AgroGIS database are proposed. For practical implementation, it is proposed to use the object-functional approach to database development, based on the use of cloud data storage technology in the DBMS SQlite.
 Background. Geoinformation support of agronomic geoinformation systems (AgroGIS) is aimed at assessing natural-territorial conditions and environmental factors in agrolandscapes, development of adaptive landscape farming systems. AgroGIS geodatabases serve to store, analyze and present spatial information on agricultural land. As shown by an analysis of literary sources, the term “geodatabase” was formed more than twenty years ago. At the same time different geodatabases are known: archaeological, cartographic, soil and others. They differ in the object of research, structure and content, as well as the way of data organization. This indicates the relevance of the topics of the present research.
 Purpose. The purpose of the work is to develop the structure and content of the geodatabase agronomic GIS.
 Materials and methods. The object-functional approach to database development, which is supported by object-oriented database management systems (DBMS) and classical relational DBMS, is used. The essence of this approach is to implement functional tasks taking into account the needs of the user.
 Results. The main components of geodatabase in the form of separate sets of spatial classes (Climate, Relief, Soil, Vegetation, Hydrography, Agrolandscape) are proposed. At the same time the paper shows the need for practical implementation of agronomic geoinformation geodatabases from several aspects.
 Conclusion. In the development of spatial databases of agronomic GIS the most important feature is the ability to constantly update information in the form of temporal component. Practical implementation of temporal geodatabases is possible with the use of non-relational database management systems, as well as methods of processing big data (Big Data).

Comparative Case Study Difference Between Azure Cloud SQL and Mongo Atlas MongoDB NoSQL Database

Azure SQL and Atlas Mongodb NoSQL(Azure instance) databases are the most popular, systematic process to database solutions. Which Azure SQL database is also referred to as RDBMS (Relational Database Management Systems). The data are structured into tables or associations. The Atlas Mongodb NoSQL database is called a non-relational database management systems. The data are included in unstructured tables or associations. In this research, evaluate both the Azure SQL and Atlas Mongodb NoSQL databases. During the experiment compare the loading time, response time, and retrieval time of both Azure SQL and Atlas Mongodb NoSQL databases, and justify which one is fast, efficient and better performance.

Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

INFORMATION TECHNOLOGY OF SUPPORTING ARCHITECTURAL SOLUTIONS USING POLYGLOT PERSISTENCE CONCEPT IN LEARNING MANAGEMENT SYSTEMS

This paper shows that performance of the learning management systems heavily depends on the choice, made during designing, of architectural solution for storage and processing of data. Based on analysis of evolution of the various architectural solutions dur-ing the information system design, beginning with monolith platform and ending with decentralized microservices, it has been de-termined that architecture based on microservices for a server side with code-level isolation and database-level decentralization for components is proved to be effective solution for high-performance system complexes for learning management system. However, for implementation of polyglot persistence concept based on multiple database managementsystems with various logical schemas, there is also a need for developing an information technology to support such architectural solutions. It has been shown that the de-velopment of databases for such learning management system, that operate with a large amount of various information, consists of the stages of conceptual, logical and physical modeling, and, precisely during the creation of logical models the requirements for the storage and processing of data, that are used by the selected entities for the implementation of business functions, are determined. The peculiar properties of using relational and non-relational database management systems such as: document, key-value, graph and column storages have been examined and analyzed in detail. A method for automated selection of logical data models based on initial information about a limited context has been developed, then used to develop a classifier. The efficiency of the classifier was tested on a dataset for two hundred thirty entities. As a result of the experiment, the reliability of the classification was ninety-three percent. The advantages of the developed information technology are shown on the example of designing JustStart learning management system. Analysis of the stress testing results of the developed system shows that due to the distribution of the load between the three databases, its average response time with simultaneous operation of one hundred fifty users was one point two seconds. At thesame time, simulation of the same number of users with only one database management system, the response time increased and the aver-age was approximately two point six seconds. Thus, the use of the developed information technology of supporting architectural solutions for organizing storage of large volumes of diverse data according to the polyglot persistence concept, that allowed to design and implement learning management system, the performance of which, if it is used simultaneously by a large audience, is on aver-age twice as fast as the average educational resource on the market.

Performance Evaluation of IoT Data Management Using MongoDB Versus MySQL Databases in Different Cloud Environments

The Internet of Things (IoT) introduces a new challenge for Database Management Systems (DBMS). In IoT, large numbers of sensors are used in daily lives. These sensors generate a huge amount of heterogeneous data that needs to be handled by the appropriate DBMS. The IoT has a challenge for the DBMS in evaluating how to store and manipulate a huge amount of heterogeneous data. DBMS can be categorized into two main types: The Relational DBMSs and the Non-relational DBMSs. This paper aims to provide a thorough comparative evaluation of two popular open-source DBMSs: MySQL as a Relational DBMS and MongoDB as a Non-relational DBMS. This comparison is based on evaluating the performance of inserting and retrieving a huge amount of IoT data and evaluating the performance of the two types of databases to work on resources with different specifications in cloud computing. This paper also proposes two prediction models and differentiates between them to estimate the response time in terms of the size of the database and the specifications of the cloud instance. These models help to select the appropriate DBMS to manage and store a certain size of data on an instance with particular specifications based on the estimated response time. The results indicate that MongoDB outperforms MySQL in terms of latency and the database size through increasing the amount of tested data. Moreover, MongoDB can save resources better than MySQL that needs resources with high capabilities to work with less performance.

NoSQL Database Classification

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.

Secure Access Image with an Encrypted key in NOSQL

Space, Time and Privacy has become a key requirement for data management systems. The, NoSQL data stores, namely highly compress data on non-relational database management systems, which provides data management of internet user program, still do not provide support. It consists of the enhancement of the MongoDB level based access protected model along with privacy keys for security and monitor. The suggested system monitor is easily used for any MongoDB application control with secured protection for data security. Using the Encrypted storage engine, the plaintext is encrypted using an algorithm and generates ciphertext, this ciphertext can only be decrypted with the decryption key. In addition, wide experiments show that the MongoDB achieves better efficiency than relational database in terms of data access and calculating.

Graph-based post incident internal audit method of computer equipment

Graph-based post incident internal audit method of computer equipment is proposed. The essence of the proposed solution consists in the establishing of relationships among hard disk damps (image), RAM and network. This method is intended for description of information security incident properties during the internal post incident audit of computer equipment. Hard disk damps receiving and formation process takes place at the first step. It is followed by separation of these damps into the set of components. The set of components includes a large set of attributes that forms the basis for the formation of the graph. Separated data is recorded into the non-relational database management system (NoSQL) that is adapted for graph storage, fast access and processing. Damps linking application method is applied at the final step. The presented method gives the possibility to human expert in information security or computer forensics for more precise, informative internal audit of computer equipment. The proposed method allows reducing the time spent on internal audit of computer equipment, increasing accuracy and informativeness of such audit. The method has a development potential and can be applied along with the other components in the tasks of users’ identification and computer forensics.

Una comparación de rendimiento entre Oracle y MongoDB

<p>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.</p>

Mining spatiotemporal co-occurrence patterns in non-relational databases

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining.

Adding a relational interface to a nonrelational database

Today, thanks to more powerful machines and more robust data models, relational databases have gained broad industry acceptance. As relational databases grow in power and popularity, developers face pressure to convert legacy databases to this newer model. The author proposes a technique for doing so efficiently. The approach involves a bridge program that adds a relational software layer atop the nonrelational database management system. The interface translates the relational database management language into the nonrelational database management language to access the existing nonrelational database.

Design of hospital database systems in a non-relational environment.

The lack of an appropriate, widely accepted, design procedure for use with non-relational database management systems has been a major, common problem in many efforts to develop an integrated hospital database. The availability of such a design procedure is particularly important today since the majority of the database management systems currently in use are non-relational. A database design procedure is proposed which should assist with the design of optimal non-relational hospital database systems. The proposed design procedure is separated into two distinct steps: the development of an appropriate intermediate relational database structure and the subsequent development of an equivalent non-relational one. A set of rules is given for the move from the proposed intermediate relational to the equivalent non-relational structure. An example application from the hospital environment is also included in order to better demonstrate the process.