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NoSQL databases are critical for managing big data. Fowler provides specific evaluation criteria for choosing the NoSQL database that's ideal for your organization, and gives real-world examples of using NoSQL databases for mission-critical enterprise architectures and projects.

As the COVID-19 crisis endures and the virus continues to spread globally, the need for collecting epidemiological data and patient information also grows exponentially. The race against the clock to find a cure and a vaccine to the disease means researchers require storage of increasingly large and diverse types of information; for doctors following patients, recording symptoms and reactions to treatments, the need for storage flexibility is only surpassed by the necessity of storage security. The volume, variety, and variability of COVID-19 patient data requires storage in NoSQL database management systems (DBMSs). But with a multitude of existing NoSQL DBMSs, there is no straightforward way for institutions to select the most appropriate. And more importantly, they suffer from security flaws that would render them inappropriate for the storage of confidential patient data. This paper develops an innovative solution to remedy the aforementioned shortcomings. COVID-19 patients, as well as medical professionals, could be subjected to privacy-related risks, from abuse of their data to community bullying regarding their medical condition. Thus, in addition to being appropriately stored and analyzed, their data must imperatively be highly protected against misuse. This paper begins by explaining the five most popular categories of NoSQL databases. It also introduces the most popular NoSQL DBMS types related to each one of them. Moreover, this paper presents a comparative study of the different types of NoSQL DBMS, according to their strengths and weaknesses. This paper then introduces an algorithm that would assist hospitals, and medical and scientific authorities to choose the most appropriate type for storing patients' information. This paper subsequently presents a set of functions, based on web services, offering a set of endpoints that include authentication, authorization, auditing, and encryption of information. These functions are powerful and effective, making them appropriate to store all the sensitive data related to patients. This paper presents an algorithm to select the most convenient NoSQL DBMS for COVID-19 patients, medical staff, and organizations data. In addition, the paper proposes innovative security solutions that eliminate the barriers to utilizing NoSQL DBMSs to store patients' data. The proposed solutions resolve several security problems including authentication, authorization, auditing, and encryption. After implementing these security solutions, the use of NoSQL DBMSs will become a much more appropriate, safer, and affordable solution to storing and analyzing patients' data, which would contribute greatly to the medical and research effort against COVID-19. This solution can be implemented for all types of NoSQL DBMSs; implementing it would result in highly securing patients' data, and protecting them from any downsides related to data leakage.

Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net .

To date, most large corporations still have their core solutions on relational databases but only use non-relational (i.e. NoSQL) database management systems (DBMS) for their non-core systems that favour availability and scalability through partitioning while trading off consistency. NoSQL systems are built based on the CAP (i.e., Consistency, Availability and Partitioning) database theorem, which trades off one of these features while maintaining the others. The need for systems availability and scalability drives the use of NoSQL, while the lack of consistency and robust query engines as obtainable in relational databases, impede their usage. To mitigate these drawbacks, researchers and companies like Amazon, Google, and Facebook run ’SQL over NoSQL’ systems such as Dynamo, Google’s Spanner, Memcache, Zidian, Apache Hive and SparkSQL. These systems create a query engine layer over NoSQL systems but suffer from data redundancy and lack consistency obtainable in relational DBMS. Also, their query engine is not relational complete because they cannot process all relational algebra-based queries as obtainable in a relational database. In this paper, we present a ’Unique NoSQL over SQL Database’ (UniqueNOSD) system, an extension of NOSD and an inverse of existing approaches. This approach is motivated by the need for existing systems to fully deploy NoSQL data store functionalities without the limitation of building an extra SQL layer for querying. To allow appropriate storage and retrieval of data on document-based NoSQL databases without data redundancy and inconsistency while encouraging both horizontal and vertical partitioning, we propose NoSQL over SQL Block as a Value ( ) data storage strategy. Unlike relational database model where a relation is represented as , with a key attribute and is the primary key to the set of attributes of the relation, in (represented as a tuple ( K ,  B ) where K means key and B means block). We represent a relation as with a key attribute K and a set of n relations (i.e., r ) called blocks B and each r contains a set of its own attributes and is denoted as with a key attribute k and a set of n attributes typical to a relational model. The relations in R of are related through foreign key relationships. Using existing benchmark systems of ’SQL over NoSQL’, relational databases and real-life datasets for our experiments, we demonstrated that our NoSQL over SQL system outperforms existing relational databases, SQL over NoSQL systems and is novel in ensuring data consistency, scalability, query execution and improving data storage and retrieval in large database systems without data loss and enhancing improved performance on NoSQL database.

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.

The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches. However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data. This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data. In particular, we compare and contrast various distributed file systems and MapReduce-supported NoSQL databases concerning certain parameters in data management process. Further, we present distinct distributed/cloud-based machine learning (ML) tools that play a key role to design, develop and deploy data models. The paper investigates case studies on distributed ML tools such as Mahout, Spark MLlib, and FlinkML. Further, we classify analytics based on the type of data, domain, and application. We distinguish various visualization tools pertaining three parameters: functionality, analysis capabilities, and supported development environment. Furthermore, we systematically investigate big data tools and technologies (Hadoop 3.0, Spark 2.3) including distributed/cloud-based stream processing tools in a comparative approach. Moreover, we discuss functionalities of several SQL Query tools on Hadoop based on 10 parameters. Finally, We present some critical points relevant to research directions and opportunities according to the current trend of big data. Investigating infrastructure tools for big data with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications.

Maintaining strong consistency and throughput in distributed NoSQL systems is very important these days, but often impossible to achieve at the same time. Consistency problems in distributed systems result in serious consequences, e.g. data loss, while throughput problems lead to poor system performance. This paper goes beyond solving consistency issues in distributed NoSQL systems and addresses the important problem of ensuring high throughput in such systems at the same time. To this end, we proposed a novel architecture of the layered NoSQL system that enables greater throughput by using replication of data items in a dynamic way while still preserving strong consistency. It is characterized by high availability, enhanced throughput, strong consistency, and space scalability. This system was built on top of a very efficient NoSQL system, called Scalable Distributed Two-Layer Data Store (SD2DS). To develop our system, we identified and analyzed the inconsistencies in the case of SD2DS architecture with increased throughput in the case of concurrent operations execution, as well as in the case of unfinished operations. The theoretical correctness of the proposed solution was proved. Its performance was experimentally evaluated in comparison with common NoSQL systems such as MemCached and MongoDB. The results confirmed its superiority in terms of performance, so it can successfully compete with publicly available solutions. It makes it possible to use the SD2DS system in practical IT systems that require high performance and consistency.

NoSQL databases were created with the primary goal of addressing the shortcomings in the efficiency of relational databases, and can be of four types: document, column, key-value, and graph databases. Graph databases can store data and relationships efficiently, and have a flexible and easy-to-understand data schema. In this paper, we perform an experimental evaluation of the four most popular graph databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph. Database performance is evaluated using the Linked Data Benchmark Council’s Social Network Benchmark (LDBC SNB). In the experiments, we analyze the execution time of the queries, the loading time of the nodes and the RAM and CPU usage for each database. In our analysis, Neo4j was the graph database with the best performance across all metrics.

The development of the Internet of Things (IoT) is closely related to a considerable increase in the number and variety of devices connected to the Internet. Sensors have become a regular component of our environment, as well as smart phones and other devices that continuously collect data about our lives even without our intervention. With such connected devices, a broad range of applications has been developed and deployed, including those dealing with massive volumes of data. In this paper, we introduce a Distributed Data Service (DDS) to collect and process data for IoT environments. One central goal of this DDS is to enable multiple and distinct IoT middleware systems to share common data services from a loosely-coupled provider. In this context, we propose a new specification of functionalities for a DDS and the conception of the corresponding techniques for collecting, filtering and storing data conveniently and efficiently in this environment. Another contribution is a data aggregation component that is proposed to support efficient real-time data querying. To validate its data collecting and querying functionalities and performance, the proposed DDS is evaluated in two case studies regarding a simulated smart home system, the first case devoted to evaluating data collection and aggregation when the DDS is interacting with the UIoT middleware, and the second aimed at comparing the DDS data collection with this same functionality implemented within the Kaa middleware.

In case report studies (adverse effects of drugs/vaccines) that are unstructured, a structured relational model is not applicable for designing a database, necessitating the use of an unstructured data model, specifically NoSQL. Therefore, the most important and optimal unstructured data model for eCRF, which has the nature of a form, is the document-oriented model. This paper develops and evaluates a reporting system for drug intervention studies with high variability in adverse events that utilizes a document-based NoSQL data model and the eCRF nature, allowing for the management of structured and unstructured data. The main objective of the research is to create a flexible, fast, and efficient system for collecting and analyzing data related to drug/vaccine adverse events, especially the COVID-19 vaccine, for stakeholders. This research is of an applied-descriptive type. In this research, first, after studying library resources, the requirements and requirements for the design of the proposed system were determined in the form of the Software requirements specification (SRS) standard. Then, the design and implementation included modeling and creating a prototype of the web-based system. To evaluate usability, the User Experience Questionnaire (UEQ) was used, system security was assessed using the Application Security Verification Standard (ASVS) questionnaire, and a comparative evaluation of the performance between MongoDB and SQLServer was performed. This research was conducted with the aim of designing and evaluating a reporting system for COVID-19 vaccine side effects, based on a document-oriented data model. It includes key components such as the information and side effects collection module, the document management module, and the reporting module. The results indicated that the user experience of the system, in terms of attractiveness, transparency, efficiency, reliability, motivation, and innovation, had an average score of 2.31, placing it within the top 10% of results. Additionally, the evaluations showed that the system employs effective security controls; however, improvements were needed in certain areas such as meeting management and authentication. A comparative assessment of the performance between the document-oriented data model and the relational data model demonstrated that the proposed system was able to provide better performance in response time and management of unstructured data. Evaluations have shown that utilizing case report forms, along with the advantages of a document-oriented data model, can be effective in collecting the minimum necessary data set for interventional studies, particularly those related to drug side effects such as the COVID-19 vaccine. Given the variable nature of the virus and the potential for unknown side effects, this requires flexible and precise approaches. Additionally, the use of the existing system, considering the results of the security and usability assessment, could be effective if access to external systems is improved.