Extended Tuple Constraint Type in Relational and XML Data Model

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.

Relational database management systems (RDBMSs) have been a common option to manage structured data over the past decades. In recent years, with the prevalence of big data applications, vast unstructured and semi-structured data are generated, deeply challenging the relational model used in RDBMSs. For this reason, a wide spectrum of NoSQL databases are developed for managing unstructured, semi-structured or structured data. For example, graph database management systems (GDBMSs) are commonly used as an important category of NoSQL databases, to manage sophisticated graph data as well as relational data. Nonetheless, as claimed in existing literatures, both RDBMSs and GDBMSs are capable of managing graph data and relational data, the boundaries of them still remain unclear. In this paper, we propose a unified benchmark for RDBMSs and GDBMSs, to evaluate them under the same metrics, and report which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join and deep recursive operations, (3) GDBMSs and RDBMSs are comparable for two-table join and shallow recursive operations.

Small-scale application development has evolved rapidly due to increasing demands for flexibility, scalability, and faster deployment cycles. Data management plays a central role in these applications, influencing performance, maintainability, and long-term adaptability. Traditionally, relational database management systems have dominated application development because of their structured schemas, strong consistency guarantees, and mature query capabilities. However, the emergence of NoSQL data models has introduced alternative approaches designed to handle unstructured data, horizontal scalability, and agile development requirements. This review examines the comparative strengths and limitations of relational and NoSQL data models in the context of small application development. Key aspects such as data modeling flexibility, query expressiveness, scalability mechanisms, consistency models, development complexity, and operational overhead are critically analyzed. The study highlights how relational models remain effective for applications requiring complex relationships, transactional integrity, and standardized query support, while NoSQL models provide advantages in schema flexibility, rapid iteration, and scalability for data-intensive or evolving workloads. Particular emphasis is placed on decision-making factors relevant to small development teams, including resource constraints, ease of maintenance, and future growth considerations. By synthesizing findings from prior empirical and conceptual studies, this review aims to clarify common misconceptions surrounding database selection and to provide a balanced perspective on model suitability. The analysis suggests that neither approach is universally superior; instead, the choice depends on application requirements, data characteristics, and expected evolution. The review concludes that informed database selection, aligned with project scale and objectives, can significantly enhance development efficiency and application reliability in small-scale software projects while reducing technical debt and long-term operational risks.

Most database models are extended to fuzzy database models for managing and retrieving imperfect information. In recent years, considerable attention was focused on the relational data model but more recently greater attention is being turned to the investigation of object-oriented data models. The incorporation of these research results into commercial relational databases management systems (RDBMS) has not as yet a reality. The main reason for this has to do with the rigidity/inflexibility of actual RDBMS. New generation object-oriented databases management systems (OODBMS) offer greater possibilities for incorporating imperfect information thanks to the reusability and extensibility properties of OODBMS. Strategic information systems of the 1990’s should be able to deal with nuanced information, i.e. imprecision, uncertainty, subjective characteristics of economic and sociological data. In this paper, we propose a solution for the representation of fuzzy information in an object-oriented environment. The solution offers facilities for defining membership functions necessary for managing fuzzy information and for retrieving objects close to an established profile in a complex objects base. The economic sciences serve as testing ground for these concepts.

Database model for hierarchical control of manufacturing cell systems in a manufacturing shop

The architecture of Geographic Information Systems (GISs) is changing: more and more systems are based on the integrated architecture, i.e. storing geometric data in the Data Base Management System (DBMS) together with administrative data. The first step in building a Geo-DBMS is by having data types and operators for simple features (i.e. geometric primitives): point, line and polygon. This has reached a level of standardisation and is now implemented in several commercial DBMSs. The next step is to have support for the topologically structured features in the DBMS, i.e. complex features. The DBMS can then check and guarantee consistency. In addition, complex operations can be executed within the DBMS. Despite the fact that topologically structured models are well known and that it is not difficult to store the topological references, it still remains an unresolved issue as to how to effectively implement these models within a relational DBMS. In this paper, we describe the design and implementation of a topologically structured management at the DBMS level. Our focus is to translate topological structures into geometric primitives. It is then possible to define a DBMS view on a topological primitive, which makes this appear as a geometric primitive. This process supports the best of both worlds: on the one hand there are advantages of the topological structure (no redundancy) and on the other hand the ease of explicit geometric primitives in querying, analysis and presentation is available.

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

An overview of the Object Protocol Model (OPM) and the OPM data management tools

DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic datasets. Specifically, there is a need for handling strings with variable sizes while keeping their biological meaning. Relational database management systems (RDBMS) provide several data types that could be further explored for the genomics context. Besides, they enforce integrity, consistency, and enable good abstractions for more conventional data. We propose the relational text data type to represent and manipulate biological sequences and their derivatives. We present a logical schema for representing the core biological information, which may be inferred from a given biological conceptual data schema and the corresponding function manipulations. We implement and evaluate these stored functions into an actual RDBMS for both efficacy and efficiency. We show that it is possible to enforce basic and complex requirements for the genomic domain. We claim that the well-established relational text data type in RDBMS may appropriately handle the representation and persistency of biological sequences. We base our approach on the idea of domain-specific abstract data types that can store data with semantically defined functions while hiding those details from non-technical end-users.

We propose relational data modeling as a tool for replacing the ad hoc and uncoordinated approaches commonly used throughout the social sciences to gather, store, and disseminate data. We demonstrate relational data modeling using global electoral and political institutional data. We define a relational data model as a map of concepts, their attributes, and the relationships between concepts developed using a formal language and according to a set of rules. To demonstrate the methodology, we design a simple relational data model of six concepts: countries, parties, elections, districts, institutions, and election results. Furthermore, we introduce a data model to solve the particularly vexing issue of party discontinuity (party splits, mergers, and alliances). We show how the solution facilitates computational tasks, such as the calculation of core measures of political phenomena (ex: electoral volatility). Ultimately, a relational data approach will play a central role in collective investments to develop advanced data capabilities, and thereby advance the accuracy, pace, and transparency of scholarship in the social sciences.

A database management system is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications. The main characteristic of the ‘database approach’ is that it increases the value of data by its emphasis on data independence. DBMSs, and in particular those based on the relational data model, have been very successful at the management of administrative data in the business domain. This thesis has investigated data management in multimedia digital libraries, and its implications on the design of database management systems. The main problem of multimedia data management is providing access to the stored objects. The content structure of administrative data is easily represented in alphanumeric values. Thus, database technology has primarily focused on handling the objects’ logical structure. In the case of multimedia data, representation of content is far from trivial though, and not supported by current database management systems.

In this work we describe the TreeRuler tool, which makes it possible for inexperienced users to access huge XML (or relational) datasets. TreeRuler encompasses two main features: (1) it mines all the frequent association rules from input documents without any a-priori specification of the desired results, and (2) it provides quick, summarized, thus often approximate answers to user’s queries, by using the previously mined knowledge. TreeRuler has been developed in the scenario of the Odyssey EU project dealing with information about crimes, both for the relational and XML data model. In this chapter we mainly focus on the objectives, strategies, and difficulties encountered in the XML context.

11 - Relational Languages

The relational data model is the dominant paradigm in the commercial database market today, and it has been for several years. However, there have been challenges to the model over the years, and they have influenced its evolution and that of database technology. The object-oriented revolution that got started in programming languages arrived to the database area in the form of a brand new data model. The relational model managed not only to survive the newcomer but to continue becoming a dominant force, transformed into the object-relational model (also called extended relational, or universal) and relegating object-oriented databases to a niche product. Although this market has many nontechnical aspects, there are certainly important technical differences among the mentioned data models. In this article I describe the basic components of the relational, object-oriented, and object-relational data models. I do not, however, discuss query language, implementation, or system issues. A basic comparison is given and then future trends are discussed.

One of the most important qualities of a database management system running on a small system is its ability to provide data independence. A relational database management system provides data independence through its use of tables for data storage. Use of a standard operating system means that a database management system can be run on a wide variety of systems, allowing data to be transported not only between small systems but between small and large systems.Assuming that a database management system provides data independence, what other features are needed? A comparison of the features provided by two such database management systems and how easy they are to use would be helpful in answering this question. This paper presents a comparison of UNIFY and Intel Database Management System (iDB). Both are relational, multi-user systems that run under the UNIX operating system.