Relational Model Research Articles

Digital technology for the national sport development: Designing a database model to analyse elite sports data

Relational database models offer a pathway for the storage, standardization, and analysis of factors influencing national sports development. While existing research delves into the factors linked with sporting success, there remains an unexplored avenue for the design of databases that seamlessly integrate quantitative analyses of these factors. This study aims to design a relational database to store and analyse quantitative sport development data by employing information technology tools. The database design was carried out in three phases: (i) exploratory study for context analysis, identification, and delimitation of the data scope; (ii) data extraction from primary sources and cataloguing; (iii) database design to allow an integrated analysis of different dimensions and production of quantitative indicators. An entity-relationship diagram and an entity-relationship model were built to organize and store information relating to sports, organizations, people, investments, venues, facilities, materials, events, and sports results, enabling the sharing of data across tables and avoiding redundancies. This strategy demonstrated potential for future knowledge advancement by including the establishment of perpetual data updates through coding and web scraping. This, in turn, empowers the continuous evaluation and vigilance of organizational performance metrics and sports development policies, aligning seamlessly with the journal’s focus on cutting-edge methodologies in the realm of digital technology.

Care coordination models for transition and long-term follow-up among childhood cancer survivors: a scoping review

ObjectivesChildhood cancer survivors may experience complex health issues during transition and long-term follow-up (LTFU); therefore, high-quality healthcare is warranted. Care coordination is one of the essential concepts in advanced healthcare....

What Goes Around Comes Around... And Around...

Two decades ago, one of us co-authored a paper commenting on the previous 40 years of data modelling research and development [188]. That paper demonstrated that the relational model (RM) and SQL are the prevailing choice for database management systems (DBMSs), despite efforts to replace either them. Instead, SQL absorbed the best ideas from these alternative approaches. We revisit this issue and argue that this same evolution has continued since 2005. Once again there have been repeated efforts to replace either SQL or the RM. But the RM continues to be the dominant data model and SQL has been extended to capture the good ideas from others. As such, we expect more of the same in the future, namely the continued evolution of SQL and relational DBMSs (RDBMSs). We also discuss DBMS implementations and argue that the major advancements have been in the RM systems, primarily driven by changing hardware characteristics.

Data management of X bill of material of missile and rocket products based on digital thread

The X bill of material (XBOM) of a missile and rocket product is a comprehensive and basic support document produced by a multi-stakeholder that considers structural composition, data information and interactional processes in different life stages. The XBOM data management of missile and rocket products coordinates the data organization and control in the whole life cycle, eliminates the interactional disconnection and one-way transmission between related parties, business and data in different life stages and provides effective data source support for the full digital model supply. To meet the requirements of organic integration and multi-party collaboration, this paper proposes the XBOM data management method based on digital thread. Firstly, the digital thread framework is analyzed, and the XBOM data management architecture based on the digital thread is proposed and established. Secondly, guided by the hierarchy of "data-interaction-modeling-application", the XBOM data management method for the missile and rocket product are developed, including data organization, data relational analysis, data modeling and data application. Finally, the structural system of a certain model of carrier rocket is studied, and the XBOM data management system based on digital thread is developed and its application is validated.

ANALYSIS OF MODERN DATABASES

In the current digital landscape, handling the vast and varied data requires customized processing and storage methods. Within a system, data within the same domain often uses different models, underscoring the importance of multi-model databases that can accommodate diverse data structures. These databases typically differentiate between primary and secondary models, necessitating clear data schema mappings. While the traditional relational data model persists as a well-studied and widely used approach, graph data models are gaining traction in areas like social networks, recommendation systems, and transportation networks. This article offers insights into the pros and cons of graph databases, providing a clearer understanding of their role in modern data management. Keywords: Graph databases, Database architecture, Graph-based data management, Multi-model DBMS, Relational databases.

TypeQL: A Type-Theoretic & Polymorphic Query Language

Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This creates many challenges in data modeling and engineering tasks, and has led to the rise of more flexible NoSQL databases, such as graph and document databases. In the absence of structured schemas, however, we can neither express nor validate the intention of data models, making long-term maintenance of databases substantially more difficult. To resolve this dilemma, we argue that, parallel to the role of classical predicate logic for relational algebra, contemporary foundations of mathematics rooted in type theory can guide us in the development of powerful new high-level data models and query languages. To this end, we introduce a new polymorphic entity-relation-attribute (PERA) data model, grounded in type-theoretic principles and accessible through classical conceptual modeling, with a near-natural query language: TypeQL. We illustrate the syntax of TypeQL as well as its denotation in the PERA model, formalize our model as an algebraic theory with dependent types, and describe its stratified semantics.

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.

ArchEthno - a new tool for sharing research materials and a new method for archiving your own research

The archiving of ethnographic material is generally considered a blind spot in ethnographic working methods which place more importance on actual investigations and analysis than on how archives are constructed. A team of computer scientists and ethnographers has built an initial tool for sharing ethnographic materials, based on an SQL relational data model that suited the first survey processed but proved difficult to transpose to other surveys. The team developed a new tool based on dynamic vocabularies of concepts which breaks down archiving into three stages. Firstly ethnographers can select and contextualise their survey materials; secondly they structure them in a database according to the research question discovered during their survey; finally, they share this data with other researchers subject to the opinion of an ethics committee whose members are competent in ethnography.

A study on latent structural models for binary relational data with attribute information

A study on latent structural models for binary relational data with attribute information

Concurrency versus consistency in NoSQL databases

<p>With the advent of cloud services, the proliferation of data has reached unprecedented levels. The load distribution across multiple servers, driven by web and mobile applications, has become a defining characteristic of contemporary data management. In contrast to this surge in data complexity, traditional relational databases have proven inadequate in handling vast amounts of unstructured data due to their inherent focus on structured data models. Additionally, the concept of clustering, vital for efficient unstructured data management, eluded relational databases, rendering them ill-equipped for customized clustering techniques and the optimal execution of queries. SQL (Structured Query Language) databases earlier emerged as a groundbreaking solution, introducing the relational database model that organized data into structured tables. They employed ACID (atomicity, consistency, isolation, durability) properties to maintain data integrity and enabled intricate querying through SQL. However, as applications grew in complexity, SQL databases encountered hurdles in handling various data types, rapid data expansion, and concurrent workloads. The limitations of SQL databases propelled the rise of NoSQL (Not Only Structured Query Language) databases, which prioritized adaptability, scalability, and performance. NoSQL databases embraced diverse data models such as documents, key-values, column families, and graphs, enabling effective management of structured, semi-structured, and unstructured data. The transition to NoSQL databases was justified by several factors; horizontally scaled across nodes, handling extensive read-write operations effectively, Agile development of accommodating changing data structures without schema constraints, optimization for specific tasks, providing low-latency access and high throughput, dynamic schemas aligned with modern iterative development, promoting adaptability, and adeptly managed diverse data types, spanning text, geospatial, time-series, and multimedia data. These databases are purposefully designed to accommodate the escalating demands of data storage. Notably, this data emanates from diverse nodes and is susceptible to concurrent access by numerous users. However, a critical challenge surfaces as the data present on one node may diverge from its counterpart on another node replica. In this context, the simultaneous execution of database operations, while preserving the integrity of the data, emerges as a pivotal concern. Maintaining data consistency amid concurrent access hinges upon the synchronization of operations across all replica nodes. Achieving this synchronization necessitates the adoption of a robust concurrency control technique. Concurrency control acts as the linchpin for upholding accuracy and reliability within a system where operations unfold concurrently. Hence, the focal point of this investigation lies in examining the assorted concurrency control methodologies employed by NoSQL systems. The objective is to dissect the intricate interplay between concurrency and consistency, shedding light on the strategies these systems employ to strike an optimal balance between the two. In summation, as the landscape of data management witnesses an era of exponential growth catalyzed by cloud services, the dynamics of load distribution and unstructured data have necessitated a departure from traditional relational databases. NoSQL databases have risen to the fore, demonstrating the ability to grapple with these challenges. However, the quest for concurrent data access without compromising data consistency propels the exploration of various concurrency control methods. The aim of this study is to look at some of the different concurrency control approaches employed by NoSQL systems, highlighting how they priorities concurrency and consistency.</p>

БАЗА ДАНИХ ДЛЯ АВТОМАТИЗОВАНОГО РОЗПІЗНАВАННЯ ТИПУ БЕЗПІЛОТНОГО АВІАЦІЙНОГО КОМПЛЕКСУ ЗА ЙОГО РАДІОСИГНАЛАМИ

The detection and technical analysis of the radio transmitters radiation of the unmanned aerial complexes elements allows to establish the fact of their use of and to identify type. Recognizing the type of unmanned aerial complex by their radio signals is carried out by comparing the estimated parameters of radio signals with known values stored in the database. The structure of the unmanned aerial complexes radio signal database based on the relational data model is proposed in the article. It is a set of interdependent two-dimensional tables that store information about parameters and characteristics of radio signals of control, telemetry and data radio lines, operating frequencies of receivers of the global navigation satellite systems, as well as purpose, belonging, aerodynamic scheme, appearance, main tactical and technical characteristics and recommendations for countermeasures. Information about the characteristics of unmanned aerial complex channels radio signals contains data on the frequency bands of the radio channel, spread spectrum parameters, modulation parameters, images of amplitude-frequency spectra and spectrograms. The use of the information collected in the database makes it possible to recognize the unmanned aerial complex type by its radio signals and to make a decision about the frequency bands in which the suppression of radio channel receivers, navigation equipment will be carried out, and to choose the most effective radio interference. Special software of the unmanned aerial complexes radio signal database was developed in accordance with the proposed structure. Special software allows you to view, edit, copy, add new information about unmanned aerial complexes, parameters and characteristics of their radio channels, as well as search. The special software user interface provides a simultaneous view of all information about unmanned aerial complex, parameters and characteristics of its radio signals. Keywords: database; radio signal; unmanned aircraft complex; parameter; characteristic; identification; countermeasure; special software.

GENERAL CHARACTERISTICS AND SELECTION OF A DATABASE MANAGEMENT SYSTEM IN DISTANCE LEARNING

The relevance of studying the discipline "Databases" for students of computer and engineering specialties is determined and their special needs and emphasis in learning are shown.
 The definition of the data model stored in the database is given. The features of the relational data model are considered, the main advantage of which is simplicity and accessibility for the user to understand and the availability of a fairly simple and at the same time powerful mathematical apparatus of set theory and relational algebra. The main educational elements of studying databases based on the relational data model are highlighted.
 The client-server architecture of modern DBMSs is described. The advantages of client-server technologies in the process of learning to work with databases are noted.
 The results of the study of the functionality of existing software products for working with databases have shown the expediency of choosing MySQL Community Edition as a training tool, which is free and in its functionality is not inferior to commercial systems by any criteria. The disadvantages of the system, which include some limitations when executing complex queries and creating analytical reports that require a high load and a large number of simultaneous queries, are not critical for training purposes. The MySQL server can be accessed both from a console client running any of the modern operating systems and from many GUI clients. The paper describes the software product MySQL Workbench Community Edition, which is offered by Oracle for free use both as part of the MySQL server and as a separate GUI client.
 As a result of the study, it was concluded that the free MySQL Community Edition DBMS together with the MySQL Workbench Community Edition GUI client fully develops the competencies of students of both engineering and computer specialties in working with databases and can be used in the study of the educational component "Databases" in the context of distance education.

Observatory: Characterizing Embeddings of Relational Tables

Language models and specialized table embedding models have recently demonstrated strong performance on many tasks over tabular data. Researchers and practitioners are keen to leverage these models in many new application contexts; but limited understanding of the strengths and weaknesses of these models, and the table representations they generate, makes the process of finding a suitable model for a given task reliant on trial and error. There is an urgent need to gain a comprehensive understanding of these models to minimize inefficiency and failures in downstream usage. To address this need, we propose Observatory, a formal framework to systematically analyze embedding representations of relational tables. Motivated both by invariants of the relational data model and by statistical considerations regarding data distributions, we define eight primitive properties, and corresponding measures to quantitatively characterize table embeddings for these properties. Based on these properties, we define an extensible framework to evaluate language and table embedding models. We collect and synthesize a suite of datasets and use Observatory to analyze nine such models. Our analysis provides insights into the strengths and weaknesses of learned representations over tables. We find, for example, that some models are sensitive to table structure such as column order, that functional dependencies are rarely reflected in embeddings, and that specialized table embedding models have relatively lower sample fidelity. Such insights help researchers and practitioners better anticipate model behaviors and select appropriate models for their downstream tasks, while guiding researchers in the development of new models.

Air Quality Data Acquisition Through Temporal Data Modeling

The paper presents a novel temporal data model for acquiring air quality data. The proposed model uses the free and open-source Postgres database with tuple time stamping, resulting in rapid and effective data extraction. The conventional relational database SQL server for data extraction is used in this paper as the foundation for an air quality temporal database model. The air quality data set collected for MDU Rohtak (source: Central Control Room for Air Quality Management - All India) is utilized to test and determine the outcomes of the proposed model. Valid time is used as the time dimension for capturing data values, together with tuple timestamping. The Postgres "tsrange" datatype is used to record the timestamp. To eliminate data duplication, the dataset is cleaned up and normalized. Using the air quality temporal data model, AQI data at various time points is effectively extracted. The present model data extraction is compared with the conventional relational data model using SQL Server database. The air quality data extraction process takes a remarkably minimal amount of time, with an average of 23% lesser time than SQL server database. It takes only a few milliseconds to extract the AQI data, and the results of the experiment are ready immediately after that. The suggested model is helpful in decision-making and offers decision-makers useful information and the tool they need to enhance air quality. The results are relevant to the study of the AQI components by different pollution management agencies like central and state pollution control boards, national green tribunal and Ministry of the Environment for efficient air pollution control measures.

A systematic literature review of authorization and access control requirements and current state of the art for different database models

Purpose Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art. Design/methodology/approach This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements. Findings As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements. Originality/value This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

An Extended Type-2 Fuzzy Relational Database Model for Aggregate and Grouping Operations

This paper introduces an extended type-2 fuzzy relational database model (ET-2FRDB) for aggregate and grouping operations that can represent and query uncertain and imprecise information in the real world applications. In ET-2FRDB, each fuzzy relation is represented by a type-2 fuzzy set whose membership degree of each tuple is a fuzzy number on [0, 1], the fuzzy aggregate functions, the fuzzy relational algebraic, aggregate and grouping operations are defined as extensions of those in the classical relational database model thereby the membership degree of tuples associated by using the minimum and maximum of fuzzy numbers. Also, some properties of the fuzzy relational algebraic, aggregate and grouping operations in ET-2FRDB are formulated and proven.

Key-Value Pairs and NoSQL Databases: A Novel Concept to Manage Biologging Data in Data Repositories

Key-Value Pairs and NoSQL Databases: A Novel Concept to Manage Biologging Data in Data Repositories

Information and analytical platform “Digital Phenotype” to identify the relationship between phenotypic and genetic data of patients with hereditary diseases

This paper presents the possibilities of using the Information and Analytical Platform “Digital Phenotype” for a formalized quantitative description of the clinical manifestations of hereditary diseases in children based on deep phenotyping in order to analyze and identify geno-phenotypic correlations, form registers of hereditary diseases and improve the reliability of predicting the course of these diseases. The platform was developed on the basis of the combined use of expert deep phenotyping of a number of rare hereditary disabling diseases manifesting in childhood and modern information technologies for building web applications that allow entering and assessing the severity of phenotypic features, checking the correctness of the input data, forming search queries, exporting chosen digital data. The software design technology is based on the architecture of a relational data model using a cross-platform solution based on the Laravel-AngularJS-mySQL stack. The results of the application of statistical methods for the analysis of geno-phenotypic correlations are presented on the example of identifying the connection of lens dislocation with the presence of a missense mutation in Marfan syndrome. It was shown that in children with mutations at the C-terminus of the MECP2 protein, the level of blood phosphates is lower than in patients with mutations at the N-terminus of the protein.

Scallop: A Language for Neurosymbolic Programming

We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability.

HTA63 The Data Lifecycle: Towards a Standardized Relational Data Model to Support Living Systematic Literature Reviews? – Use Case Demonstration with LiveSLR®

Systematic literature reviews (SLR) offer an unbiased evidence summary for informing policy. Keeping SLRs current is paramount for healthcare decision making. However, traditional, static SLRs cannot accommodate constant data inflow especially resulting from multiple updates of studies. LiveSLR® is an up-to-date Cochrane/NICE-compliant curated SLR library following PRISMA standards, maintained by experienced reviewers. We seek understanding if the LiveSLR® data model could bridge this gap, leading to a “living" workflow.