A comparative review of relational and NoSQL data models for small application development

In recent years, the hegemony of traditional relational database management systems (RDBMSs) has declined in favour of non-relational databases (NoSQL). These database technologies are better adapted to meet the requirements of large-scale (web) infrastructures handling Big Data by providing elastic and horizontal scalability. Each NoSQL technology however is suited for specific use cases and data models. As a consequence, NoSQL adopters are faced with tremendous heterogeneity in terms of data models, database capabilities and application programming interfaces (APIs). Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in. A solution has been proposed in the shape of Object-NoSQL Database Mappers (ONDMs), which provide a uniform abstraction interface for different NoSQL technologies.Such ONDMs however come at a cost of increased performance overhead, which may have a significant economic impact, especially in large distributed setups involving massive volumes of data.In this paper, we present a benchmark study quantifying and comparing the performance overhead introduced by Object-NoSQL Database Mappers, for create, read, update and search operations. Our benchmarks involve five of the most promising and industry-ready ONDMs: Impetus Kundera, Apache Gora, EclipseLink, DataNucleus and Hibernate OGM, and are executed both on a single node and a 9-node cluster setup.Our main findings are summarised as follows: (i) the introduced overhead is substantial for database operations in-memory, however on-disk operations and high network latency result in a negligible overhead, (ii) we found fundamental mismatches between standardised ONDM APIs and the technical capabilities of the NoSQL database, (iii) search performance overhead increases linearly with the number of results, (iv) DataNucleus and Hibernate OGM’s search overhead is exceptionally high in comparison to the other ONDMs.

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

11 - Relational Languages

An increasing number of database management systems are expanding their functionality to work with various types of spatial data. This is true for both relational and NoSQL data models. The article describes the main features of those data models for which the functions of storing and processing spatial data are implemented. A comparative analysis of the performance of typical spatial queries for database management systems based on various data models, including multi-model ones, is carried out. The dataset on which the comparison is performed is presented in the form of three blocks of OpenStreetMap vector data for the territory of the Novosibirsk region. Based on the results of the study, recommendations are made on the use of certain data models, depending on the available data and the tasks to be solved.

NoSQL databases are suitable for network traffic loads experienced by today's websites. The important attributes that make them a favorable choice are inherent distributed nature, horizontal scalability and flexibility in data models. Hence, more data is now getting handled through NoSQL databases and sensible information extraction from these data stores is becoming a requirement. Information extraction may require sourcing data from multiple data sources, establishing relationship among them and querying across these data sources together. Ontology based semantic integration systems for Relational Database Management Systems (RDBMS) already exist that satisfies the above requirement. Many commercial systems are operational based on the above technique and tools and solutions for the above approach are very much mature. Hence, mapping the processing done in each stage of RDBMS based semantic integration systems to that of NoSQL systems can facilitate usage of existing tools and frameworks. The purpose of this work is to develop ontology based semantic integration system for a column-oriented NoSQL data store like HBase which is similar in architectural design to RDBMS.

Relational Database Management Systems (RDBMS) have been very successful at managing structured data with well-defined schemas. Despite this, relational systems are generally not the first choice for management of data where schemas are not pre-defined or must be flexible in the face of variations and changes. Instead, No-SQL database systems supporting JSON are often selected to provide persistence to such applications. JSON is a light-weight and flexible semi-structured data format supporting constructs common in most programming languages. In this paper, we analyze the way in which requirements differ between management of relational data and management of JSON data. We present three architectural principles that facilitate a schema-less development style within an RDBMS so that RDBMS users can store, query, and index JSON data without requiring schemas. We show how these three principles can be applied to industry-leading RDBMS platforms, such as the Oracle RDBMS Server, with relatively little effort. Consequently, an RDBMS can unify the management of both relational data and JSON data in one platform and use SQL with an embedded JSON path language as a single declarative language to query both relational data and JSON data. This SQL/JSON approach offers significant benefits to application developers as they can use one product to manage both relational data and semi-structured flexible schema data.

In the last few years, databases have become very important and very large because they play a strategic and important role in most organizations and because they receive a huge flow of information from multiple sources every moment in building BigData. This situation identified several limitations and weaknesses in relational database management systems (RDBMS), such as availability, real-time response, horizontal scalability, decision support, advanced data analysis, and especially the management of Bigdata which can reach zeta bytes in storage. This requires the storage and organization of this data in a new management system database not fixed by a rigid structure and resolves all problems associated with the storage of database in a relational system. In this view, organizations need a new NoSQL (not only SQL) system that overcomes the limitations of the relational system. The change of the database management system requires the migration of the databases from the relational system to another NoSQL, taking into consideration all stored data and keeping the majority of the possibilities and functionalities of the old system, with all the advantages of the new system. In this paper, we will identify the elements of relational databases that belong to nature: data, structure, and semantics, which we must migrate to a NoSQL system, such as a document-oriented system. Also in this paper, we will present the rules for transforming the structure of the relational system to another document-oriented NoSQL, according to the principal’s basics of a new approach to migration.KeywordsMigration DataBaseNoSQLDatabaseTransformation rulesBigData

This paper explores horizontal scaling of PHP MVC applications with session preservation for enhanced availability and resource efficiency. It covers theoretical aspects of MVC architecture, PHP in web development, session handling, and horizontal scaling methods, including load balancers, Docker, and Kubernetes. The practical methodology details environment setup, application development, session management, Dockerization, Kubernetes integration, and horizontal scaling configuration. Performance testing reveals significant improvements, showing a response time decrease from an unresponsive state at 1000 RPS (5111 ms) to 32 ms at 2500 RPS with horizontal scaling. The study contributes insights and practical guidance for highly available and scalable web applications.

As cloud computing continues to evolve, organizations are finding new ways to store the massive amounts of big data that are collected. Big data storage often require greater flexibility and scalability which can be provided by incorporating NoSQL technologies. NoSQL (Not Only SQL) is quickly becoming a popular approach to store large and unstructured data. This paper looks at the various classifications of NoSQL technologies as well as many of the notable characteristics of the technologies. The authors also describe some deficiencies of using NoSQL and give some explanation to why companies are adopting the technology. The paper concludes with suggestions for future research of NoSQL technologies and a content analysis of current articles in database management is provided in the appendix. Keywords: big data, cloud computing, document databases, MongoDB, NoSQL, Not Only SQL, unstructured database INTRODUCTION Since NoSQL database technologies entered the market in 1998, these technologies have challenged the widely-accepted relational structures and database management practices supported by traditional SQL databases. Despite this trend, the perception that NoSQL seeks to eliminate the use of relational databases is a common misconception. NoSQL is best considered with the acronym NOSQL - Not Only SQL - which more accurately represents an approach that combines non-relational databases with the use of relational ones. This approach seeks to leverage both NoSQL and SQL technologies in order to balance the demands of performance, scalability, and schema flexibility with data integrity and consistency (Purdue, 2014; Penchikala, 2013). The rise of web-scale applications creates performance, scalability, and schema flexibility demands that present significant challenges for traditional SQL / relational databases. Thus, a variety of new entrants have entered the market, each seeking to address specific challenges, including (Kent, 2012): * The need to store large volumes of data about users, objects and products * The need to provide near-real-time transaction processing * The need to provide high-speed analytics on large volumes of data (big data) * The need to support flexible data schemas for evolving business environments * The need to scale applications horizontally with predictable hardware and licensing cost structures Each NoSQL solution seeks to address one or more of the above issues, often by providing alternative approaches to long-established relational database norms. Traditional relational databases were not designed to cope with the scalability and flexibility challenges facing modern applications. Older databases were not designed to take advantage of the inexpensive storage and excess processing power available today. The defining characteristics of NoSQL technologies, as well as the relative advantages and disadvantages of NoSQL, and the subsequent influence on adoption of NoSQL technologies are each the subject of a small, yet growing body of research. This paper provides a literature review of that research and proposes potential areas where future research may focus. REVIEW OF LITERATURE NoSQL database technology is a relatively recent innovation and much of the research surrounding it is of an emerging nature. For the purpose of this literature review Galileo was primarily used for identifying the articles. Articles were selected that focused directly on NoSQL database technologies (unstructured / semi-structured databases, document databases, etc.) and articles that focused on relational databases. The contrasting articles provided the researchers with the ability to compare the features that are offered with a greater degree of variability in NoSQL databases (for example, an article on SQL scalability provided a foundation for comparing newer horizontal scaling techniques used in certain NoSQL databases). …

In relational and complex spreadsheets, relational and non-relational database models, high-speed data migration with scalable structure using visual Basic Excel programming language implementations with proposed migration model. One of the primary purposes behind making a point of interaction through the Visual Basic Application (VBA) is that most PC clients with logical preparation will currently know about Succeed and its significant capabilities (like arranging furthermore, plotting datasets). Even though Excel itself is in many cases utilized as an information storehouse by clients. The time factor, reliability, and credibility of migrating this data from one table to another through programming interfaces were measured using the link codes between the tables. In this paper, the migration and migration of homogeneous and heterogeneous data are investigated by using two types of different migration models of data to measure and match these data and the extent of their integration after migration. Specifies the target data for migration from the input tables in the Excel program to the target tables in larger databases. Furthermore, the two models (A, B) middleware provide an architecture that can be extended to support Relational database management systems (RDBMS) and other graphing databases. Experiments were performed using excel tables, both of which are related as source information bases, and as the data set for the source and target datasets, the migration time between these tables for the two models was calculated While retaining the same characteristics.

Aufgrund fallender Preise zur Speicherung von Daten kann man derzeit eine explosionsartige Zunahme in der Menge der verfügbaren Daten beobachten. Diese Entwicklung gibt Unternehmen und wissenschaftliche Institutionen die Möglichkeit empirische Daten in ungekannter Größenordnung zu analysieren. Für viele Firmen ist die Analyse der gesammelten Daten aus ihrem operationalen Geschäft längst zu einem zentralen strategischen Aspekt geworden. Im Gegensatz zu der seit längerem schon betriebenen Business Intelligence, bestehen diese Analysen nicht mehr nur aus traditionellen relationalen Anfragen. In zunehmendem Anteil kommen komplexe Algorithmen aus den Bereichen Data Mining und Maschinelles Lernen hinzu, um versteckte Muster in den Daten zu erkennen, oder Vorhersagemodelle zu trainieren. Mit zunehmender Datenmenge und Komplexität der Analysen wird jedoch eine neue Generation von Systemen benötigt, die diese Kombination aus Anfragekomplexität und Datenvolumen gewachsen sind. Relationale Datenbanken waren lange Zeit das Zugpferd der Datenanalyse im großen Stil. Grund dafür war zum großen Teil ihre deklarativen Anfragesprache, welche es ermöglichte die logischen und physischen Aspekte der Datenspeicherung und Verarbeitung zu trennen, und Anfragen automatisch zu optimieren. Das starres Datenmodell und ihre beschränkte Menge von möglichen Operationen schränken jedoch die Anwendbarkeit von relationalen Datenbanken für viele der neueren analytischen Probleme stark ein. Diese Erkenntnis hat die Entwicklung einer neuen Generation von Systemen und Architekturen eingeläutet, die sich durch sehr generische Abstraktionen für parallelisierbare analytische Programme auszeichnen; MapReduce kann hier beispielhaft genannt werden, als der zweifelsohne prominenteste Vertreter dieser Systeme. Zwar vereinfachte und erschloss diese neue Generation von Systemen die Datenanalyse in diversen neuen Anwendungsfeldern, sie ist jedoch nicht in der Lage komplexe Anwendungen aus den Bereichen Data Mining und Maschinelles Lernen effizient abzubilden, ohne sich dabei extrem auf spezifische Anwendungen zu spezialisieren. Verglichen mit den relationalen Datenbanken haben MapReduce und vergleichbare Systeme außerdem die deklarative Abstraktion aufgegeben und zwingen den Anwender dazu systemnahe Programme zu schreiben und diese manuell zu optimieren. In dieser Dissertation werden verschiedene Techniken vorgestellt, die es ermöglichen etliche der zentralen Eigenschaften von relationalen Datenbanken im Kontext dieser neuen Generation von daten-parallelen Analysesystemen zu realisieren. Mithilfe dieser Techniken ist es möglich ein Analysesystem zu beschreiben, dessen Programme gleichzeitig sowohl generische und ausdrucksstark, als auch prägnant und deklarativ sind. Im einzelnen stellen wir folgende Techniken vor: Erstens, eine Programmierabstraktion die generisch ist und mit komplexen Datenmodellen umgehen kann, aber gleichzeitig viele der deklarativen Eigenschaften der relationalen Algebra erhält. Programme, die gegen dies Abstraktion entwickelt werden können ähnlich optimiert werden wie relationale Anfragen. Zweitens stellen wir eine Abstraktion für iterative daten-parallele Algorithmen vor. Die Abstraktion unterstützt inkrementelle (delta-basierte) Berechnungen und geht mit zustandsbehafteteten Berechnungen transparent um. Wir beschreiben wie man einen relationalen Anfrageoptimierer erweitern kann so dass dieser iterative Anfragen effektiv optimiert. Wir zeigen dabei dass der Optimierer dadurch in die Lage versetzt wird automatisch Ausführungspläne zu erzeugen, die wohlbekannten, manuell erstellten Programmen entsprechen. Die Abstraktion subsumiert dadurch spezialisierte Systeme (wie Pregel) und bietet vergleichbare Performanz. Drittens stellen wir Methoden vor, um die Programmierabstraktion in eine funktionale Sprachen einzubetten. Diese Integration ermögliche es prägnante Programme zu schreiben und einfach wiederzuverwendenden Komponenten und Bibliotheken, sowie Domänenspezifische Sprachen, zu erstellen. Wir legen dar wie man die Übersetzung und Optimierung des daten-parallelen Programms mit dem Sprachübersetzer der funktionalen Sprache so integriert, dass maximales Optimierungspotenzial besteht.

In the recent era of data science, AI, and machine learning the amount of data has increased exponentially, and future growth is predicted to be even greater. For the last 4 decades RDBMS is way for data storage, but with the rise of cloud computing online applications have taken place of the stand-alone applications, while distributed servers have taken the place of traditional dedicated servers. Cloud computing has become very efficient because of the lower cost, ability to scale up and down, flexibility, and pay-as-you-go model. This is the major change after the birth of the Internet. Cloud computing has revolutionized the way data is stored and accessed. Cloud databases are an integral part of the cloud computing paradigm, offering scalability, flexibility, and cost-efficiency for managing large volumes of data. Cloud databases such as Amazon RDS, IBM DB2 and MongoDB are becoming popular. Cloud databases find the restrictions of older relational databases. Data storage, retrieval, modification, and analysis are essentially the functions of cloud databases. Cloud databases are mostly used by data-intensive applications including data warehousing, data mining, and business intelligence. Applications for supply chain management, online e-commerce, and transactional data management, including banking and airline reservations, also benefit from cloud storage. This research paper reviews the concept of cloud databases, exploring their features, it examines the architecture of cloud databases, the comparison between traditional Relational Database Management Systems (RDBMS) and NoSQL databases, and popular implementations in the cloud environment. Additionally, the challenges faced in their development and deployment.

Data model research is a fundamental part of the field of big data, and strongly supports the higher levels of database construction, data access, data analysis and data mining. This paper considers the shortcomings of traditional databases such as limited support for data types, low concurrent query performance and lack of horizontal scalability. And based on this, it proposes an Open Scalable Relational Data Model (OSRDM), which provides open support for varieties of data types and enables complete horizontal scalability on the basis of keeping and extending the relational descriptive power of traditional relational data models. It caters for the characteristics of big data such as volume, variety and velocity. Finally, the paper analyzes and evaluates OSRDM by comparing it with popular data models from two perspectives: functional features and performance indicators. The results show the superiority of OSRDM.

A distributed database system named “Data-Free-Way (DFW)” is under development by a cooperation among three Japanese national research organizations to support the creation of advanced nuclear materials. The development of DFW started in 1990 as a five-year program with a support from Science and Technology Agency of Japan. Before starting the program, a preliminary survey of both domestic and foreign databases of nuclear materials had been made for two years. Then, subjects for the construction of DFW were extracted. To meet the subjects, the development and construction programs were established. The DFW is constructed on the computer network which connects engineering workstations in the separate organizations. A relational database management system is used, a distributed material database is equipped on the hardware with specially designed common data structure. Data storage has been carried out continuously in each organization. The equipment of useful user-interface systems, such as retrieval, data...

In today's IT infrastructures, data is stored in SQL databases, non-SQL, databases, and host databases like ISAM/VSAM files. Non-SQL databases are specialized data stores controlled by applications like spreadsheets, mail, directory and index services. Developing applications accessing a variety of different data sources is challenging for application developers due to different environments, APIs, bindings, etc. 20 years ago, SQL was created to ease the life of database application developers and provide a uniform way for accessing data which is stored in SQL databases. The paper describes an implementation of table functions and its usage for accessing data stored outside SQL databases in diverse external data stores. Table functions are compliant with the relational data model, and therefore fit into the well established SQL language. The table-function architecture is open, and allows the deployment of generic data access infrastructures such as Microsoft's OLE DB or Java's JDBC (G. Hamilton et al., 1997). The paper describes a prototype implementation of OLE DB table functions with advanced query optimization techniques. The prototype is based on IBM DB2 UDB relational database system.