NoSQL Database MongoDB Research Articles

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.

Examining and juxtaposing the two NoSQL databases MongoDB and CouchDB

Relational databases have been an essential component of technology for many years. It has been, and in some cases, still is, the foundation of both large and small organizations. However, a few flaws in relational databases gave rise to NoSQL databases. NoSQL is not a particularly new technology, but it is one that is rapidly expanding. The structured query language is also known as NoSQL. Today, a variety of NoSQL databases are accessible depending on the user's needs. This review combines numerous comparisons of these databases based on various attributes. A few terms relating to NoSQL databases are also clarified and brought to light. Additionally, a detailed comparison of the two most popular NoSQL databases is evaluated.NoSQL is a component of a very narrow ecosystem with fewer applications than relational databases. Relational databases are fairly straightforward, which makes them easy to use, but the non-uniformity of the expanding data causes an issue with them. Organizations manage millions of pieces of data, which makes using a relational database challenging. Additionally, the IoT is right outside our door, and NoSQL is better for IoT applications. NoSQL was developed to address this non-uniformity and the rapidly expanding data sets. NoSQL is mostly utilized in distributed, cloud, and IoT systems. NoSQL databases eschew rigid schemas and prioritize availability, scalability, and fault tolerance as key properties.

"Evaluation of NoSQL Database MongoDB with Respect to JSON Format Data Representation "

The current information technology advancement is attributed to the development of more data-intensive applications. However, 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 the benefits like flexibility and horizontal scalability, NOSQL (Not Only SQL) is in demand. This essay explores many No SQL database types, such as MongoDB, CouchDB, HBase, Cassandra, etc. A document-oriented, cross-platform database called MongoDB offers excellent performance, high availability, and simple scalability. The MongoDB Query Language (MQL), created for ease of use by developers, is used by MongoDB. The documentation contrasts the syntax of MQL versus SQL for typical database operations. When your data is document-centric and doesn't fit well into the schema of a relational database, when you need to support vast size, when you need to quickly prototype, and for a few additional use cases, NoSQL databases like MongoDB are a solid option.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.

Delivering Personalized Content to Open-air Museum Visitors Using Geofencing

This paper presents the architecture and implementation of a service that delivers personalized content to open-air museum visitors. The service uses push notifications to deliver this content. Notifications can be delivered to all visitors or personalized - to a group of visitors or to a specific visitor. The service segments museum visitors according to their location and their profile, which is built dynamically over time. For the geospatial segmentation of visitors, geofencing is used - each visitor is assigned to a segment that corresponds to a specific geographic area - part of an open space or exhibit. The service allows localization of visitors by their GPS coordinates or by estimating their distance from Bluetooth Low Energy (BLE) beacons. The geofences are described as a polygons or circles. Geospatial segmentation is implemented using a NoSQL database MongoDB, which has built-in capabilities for working with geospatial queries. Depending on the profile, each visitor falls into one or several segments: professional researcher, non-professional researcher, inspiration seeker, casual visitor, and visitors with disabilities. For each visitor, personalized content is delivered, depending on the segments to which the visitor is assigned. The necessary experiments have been conducted and analyzed to prove the applicability of the service for real-time delivery of personalized content.

Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis

Big data's infrastructure is a technology that provides the ability to store, process, analyze, and visualize large data. The tools and applications used are one of the challenges when building big data's infrastructure. In the study, we offered a new strategy to optimize big data infrastructure design that was an essential part of big data processing by performing performance analysis applications used at each stage of big data processing. The process started from collecting data sourcing online news using web crawler methods using Scrapyand Apache Nutch. Next, implement Hadoop technologies to facilitate the distribution of big data storage and computing. No-sql databases Mongo DB and HBase made it easier to query data, after which they built search engines using Elasticsearch and Apache Solr. Data saved later in analysis using hive apache, pig, and spark. The data has been analyzed was shown on the website using Zeppelins, Metabolase, Kibana, and Tableau. The test scenario consisted of the number of servers and files used. Testing parameters started from process speed, memory usage, CPU usage, throughput, etc. The performance testing results of each application were compared to and analyzed to see the merits and defaults of the application as a reference to building optimal infrastructure design to meet the needs of the user. This research has produced two big data infrastructure design alternatives. The suggested infrastructure has been implemented on computer nodes in the big data pens for processing big data from online media and proving to be running well.

Construction of a Wireless Sensing Network System for Leisure Agriculture for Cloud‐Based Agricultural Internet of Things

This paper provides an in‐depth study and analysis of the construction of a cloud‐based agricultural Internet of Things system for a wireless sensing network system for leisure agriculture. Using more mature agricultural sensor technology, compliant economy designed for indoor feeding and planting of distributed and integrated two sensor collection and transmission scheme, analysis of environmental factors selected high‐performance various types of sensors and regulation equipment, between nodes based on SI4432 for wireless communication, and controller nodes selected STM32 as a microprocessor, through the W5500‐based network port access module or ESP8266‐based WiFi module for broadband access. In response to the development of mobile technology and the reality of diversified types of mobile terminals, to make all kinds of terminals accessible to the leisure agriculture system, the server software adopts the SOA software architecture, which makes the system have good openness and scalability. The NoSQL database MongoDB is used for the cloud storage of massive data, and the data structure design is completed after analyzing the database requirements, including collections, documents, and fields. The autosharding technology is used to build a database sharding cluster in the cloud, which realizes the high‐speed cloud elastic storage of massive data and rewrites the database access object DAO to ensure that the WEB application is normal. Traditional leisure agriculture is mostly based on field tourism and agritourism methods, and the model is developing slowly and has increasingly failed to attract the interest of urban residents. The introduction of IoT technology in traditional leisure agriculture can increase the interest of leisure agriculture and improve the interest of urban residents in leisure agriculture.

A Comparative Study of NOSQL System Vulnerabilities with Big Data

With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption, authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.

Semi-structured data analysis and visualisation using NoSQL

In the field of computing, every day huge amounts of data are created by scientific experiments, companies and users' activities. These large datasets are labelled as 'big data', presenting new challenges for computer science researchers and professionals in terms of storage, processing and analysis. Traditional relational database systems (RDBMS) supported with conventional searches cannot be effectively used to handle such multi-structured data. NoSQL databases complement to the challenges of managing RDBMS with big data and facilitate in further analysis of data. In this paper, we introduce a framework that aims at analysing semi-structured data applications using NoSQL database MongoDB. The proposed framework focuses on the key aspects needed for semi-structured data analytics in terms of data collection, data parsing and data prediction. The layers involved in the framework are request layer facilitating the queries from user, input layer that interfaces the data sources and the analytics layer; and the output layer facilitating the visualisation of the analytics performed. A performance analysis for select+fetch operations needed for analytics, of MySQL and MongoDB is carried out where NoSQL database MongoDB outperforms MySQL database. The proposed framework is applied on predicting the performance and monitoring of cluster of servers.

Comparative Study of Role Based Access Control in Cloud Databases and NoSQL Databases

Role Based Access Control (RBAC) is a mechanism to restrict access to unauthorized users for security of computer systems. Many organizations have adopted non-relational databases which are generally referred to as NoSQL databases. Security issues are the main concerns, as sensitive data is stored in both Cloud Databases and NoSQL Databases, especially access control to those data by unauthorized users. The working conditions and characteristics of RBAC of different Cloud Databases and NoSQL Databases vary. The main objective of this paper is to give clear-cut idea about RBAC in different Cloud Databases and NoSQL Databases. As a consequence, we have compared the RBACs of the most popular Cloud Databases Oracle 12c, Microsoft SQL Azure and NoSQL Databases MongoDB, Cassandra. We have presented the extensive comparative study of Role Based Access Control Model in Cloud Databases and NoSQL Databases under the specific parameters.

Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects

Three-dimensional (3D), city models have been applied in a variety of fields. One of the main problems in 3D city model utilization, however, is the large volume of data. In this paper, a method is proposed to generalize the 3D building objects in 3D city models at different levels of detail, and to combine multiple Levels of Detail (LODs) for a progressive distribution and visualization of the city models. First, an extended structure for multiple LODs of building objects, BuildingTree, is introduced that supports both single buildings and building groups; second, constructive solid geometry (CSG) representations of buildings are created and generalized. Finally, the BuildingTree is stored in the NoSQL database MongoDB for dynamic visualization requests. The experimental results indicate that the proposed progressive method can efficiently visualize 3D city models, especially for large areas.

Application of NoSQL Database in Web Crawling

Web crawling is one of the most important applications of the Internet; the selection of database storage directly affects the performance of search engines. In the past few decades, the traditional relational databases almost monopolize all areas of the database applications. However, with the continuous development of web applications, they are facing the severe challenges. NoSQL database is the broad definition of non-relational data storage. This article gives the data storage structure and query of relational database and NoSQL database MongoDB to meteorological BBS information collection system, and the advantage and the disadvantage are listed in data structure, query and scalability. Compared to relational database, MongoDB supports schema-free, has great query performance with huge amount of data and provides easy horizontal scalability with low cost of hardware. It is more suitable for data storage in Web crawling.