Relational Database Research Articles

Phanerozoic thermochronology record of Afro-Arabia through space and time

Low-temperature thermochronology has been widely used in eastern Africa and Arabia (Afro-Arabia) to investigate the long-term thermal evolution of the crust in response to Phanerozoic tectonism. Yet, utilisation of this invaluable thermochronology record to inform numerical investigations into the long-term tectonothermal, geodynamic and landscape evolution of the region has been limited by the dispersion of these data across numerous disparate case studies. Here, we present a relational database of apatite (1787), zircon (68) and titanite fission-track (97) analyses, and apatite (1,945), zircon (3310), and titanite (U-Th)/He (83) ages, including 465 new fission-track and 2,583 new single-grain (U-Th)/He analyses from the region. Where available, all detailed data needed for performing thermal history modelling are presented. Also included are 668 digitised thermochronology-derived thermal history simulations. Collectively, this comprehensive database records the Phanerozoic thermal evolution of Afro-Arabia through space and time. The machine-readable database is made publicly available through the EarthBank platform, enabling 4D (3D through time) geospatial data interrogation.

ADAPTIVE ENTERPRISE MANAGEMENT FORMATION FACTORS

The study explores adaptive enterprise management as a strategic framework for maintaining optimal efficiency in dynamic and uncertain environments. It was stated that modern technological platforms play a dual role, either accelerating or constraining adaptive management processes. On one hand, traditional data distribution across departments can limit real-time visibility required for timely and accurate economic judgments. On the other hand, cloud-based infrastructures with integrated data repositories empower managers to analyze aggregated indicators swiftly and adjust strategies based on updated patterns. As was declared, adaptive management in contemporary organizations faces challenges stemming from the complexity of data sources, such as transactional databases, consumer feedback, operational logs, and financial reports. Subsequently, these diverse and increasingly sophisticated data sources necessitate advanced analytical methodologies capable of extracting meaningful insights from unstructured text, high-dimensional numerical data, and time-series signals reflecting system performance. Relational databases, widely used in transactional processing and resource management, often rely on numerical and categorical metrics like query execution times and error counts. However, these metrics fail to capture the broader semantic context of issues documented in textual logs and feedback. BERT embeddings transform raw textual inputs, such as error messages or policy documentation, into high-dimensional vectors retaining semantic detail. Simultaneously, LSTM networks manage temporal dependencies in performance trends, selectively retaining long-term patterns while discarding irrelevant fluctuations. The combined feature vectors—comprising BERT embeddings and numerical metrics—are fed into LSTM layers to model fluctuations in database performance dynamically. The research emphasizes the critical role of real-time analytics and adaptive decision-making facilitated by advanced information architectures. It highlights the economic value of integrating data-driven insights into adaptive enterprise management, enabling rapid optimization of processes and resource allocation. However, technological advancements alone are insufficient without organizational readiness to act on insights, balanced incentives, and a culture of adaptability. The study underscores the balance between centralized oversight for compliance and decentralized decision-making for local adaptability. It was concluded, that while centralization supports risk management standardization, excessive control can stifle frontline innovation. Conversely, excessive decentralization risks incoherence and inefficiency. This work proposes an economic model that integrates organizational structures and technological infrastructures to improve decision-making processes in real-time.

Gymnotoa-db: a database and application to optimize functional annotation in gymnosperms.

Gymnosperms are a clade of non-flowering plants that include about 1000 living species. Due to their complex genomes and lack of genomic resources, functional annotation in genomics and transcriptomics on gymnosperms suffers from limitations. Here we present gymnotoa-db, which is a novel, publicly accessible relational database designed to facilitate functional annotation in gymnosperms. This database stores non-redundant records of gymnosperm proteins, encompassing taxonomic and functional information. The complementary software, gymnotoa-app, enables users to download gymnotoa-db and execute a comprehensive functional annotation pipeline for high-throughput sequencing-derived DNA or cDNA sequences. gymnotoa-app's user-friendly interface and efficient algorithms streamline the functional annotation process, making it an invaluable tool for researchers studying gymnosperms. We compared gymnotoa-app's performance against other annotation tools utilizing disparate reference databases. Our results demonstrate gymnotoa-app's superior ability to accurately annotate gymnosperm transcripts, recovering a greater number of transcripts and unique, non-redundant Gene Ontology terms. gymnotoa-db's distinctive features include comprehensive coverage with a non-redundant dataset of gymnosperm protein sequences, robust functional information that integrates data from multiple ontology systems, including GO, KEGG, EC, and MetaCYC, while keeping the taxonomic context, including Arabidopsis homologs. Database URL: https://blogs.upm.es/gymnotoa-db/2024/09/19/gymnotoa-app/.

Exporting Mycenaean: From a Relational Database to EpiDoc XML files (and back again?)

DAMOS is a (MariaDB) relational database, which contains all the published Mycenaean documents. These are administrative documents which constitute the earliest attestation (ca. 1400-1150 BCE) of the Ancient Greek language. The data in DAMOS can be accessed, searched and browsed online since the launch of its online interface in 2013. In this paper, after a general introduction on the data and the database structure, we describe the export function which we recently added to DAMOS and its online interface. This allows users to export data from the database as Epidoc-TEI compliant XML files. EpiDoc is an international, standard subset of TEI, used for the digital edition of documentary Ancient Greek and Latin texts, currently expanding to other epigraphic traditions (e.g., runes, ogham). The present work builds also on the collaboration with the EpiDoc community on the adaptation of EpiDoc to suitably represent the Mycenaean documents.The aim of the EpiDoc export function is, thus, in compliance with the FAIR data principles, to increase the integration of the Mycenaean data with other similar datasets, to provide a sustainable format for the long time archiving of the DAMOS data, and to make it easier to reuse these data.Finally, we briefly discuss the planned development of an import function, which would enable users to contribute data to DAMOS by uploading EpiDoc-XML files with new or revised versions of the Mycenaean texts, thus allowing, and fostering, collaborative work on the digital edition of the Mycenaean documents.

Leveraging Transformers-based models and linked data for deep phenotyping in radiology.

Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries. Transformers-based NLP Models, concretely pre-trained RoBERTa language models, were used to process radiology reports and annotate them identifying elements matching UMLS Concept Unique Identifiers (CUIs) definitions. CUIs were mapped into several biomedical ontologies useful for phenotyping (e.g., SNOMED-CT, HPO, ICD-10, FMA, LOINC, and ICPC2, among others) and represented as a lightweight ontology using OWL (Web Ontology Language) constructs. This process resulted in a Linked Knowledge Base (LKB), which allows running expressive queries to retrieve reports that comply with specific criteria using automatic reasoning. Although phenotyping tools mostly rely on relational databases, the combination of NLP and Linked Data technologies allows us to build scalable knowledge bases using standard ontologies from the Web of data. Our approach enables us to execute a pipeline which input is free text and automatically maps identified entities to a LKB that allows answering phenotyping queries. In this work, we have only used Spanish radiology reports, although it is extensible to other languages for which suitable corpora are available. This is particularly valuable in regional and national systems dealing with large research databases from different registries and cohorts and plays an essential role in the scalability of large data reuse infrastructures that require indexing and governing distributed data sources.

Cloud-based prototyping for scalable software development: A study on rapid deployment and testing using amazon web services (AWS)

This paper examines the role of cloud-based prototyping in scalable software development, focusing on the rapid deployment and testing capabilities provided by Amazon Web Services (AWS). Traditional approaches find it difficult to meet the need for quick, scalable, and affordable prototyping as software development needs more agility. To overcome these obstacles, cloud platforms such as AWS provide adaptable, on-demand infrastructure. This study assesses how AWS products, such as Relational Database Service (RDS), Lambda, and Elastic Compute Cloud (EC2), affect prototype cycles through a number of experiments. The results show that by automating infrastructure management, AWS greatly shortens deployment times, allowing for faster application delivery. Before a full-scale deployment, developers can improve apps thanks to AWS's support for effective load testing and quick iterations. The study shows how AWS improves scalability, reduces operational overhead, and expedites testing procedures, providing useful information for businesses looking to use cloud platforms to speed up software development.

THE ROLE OF NON-RELATIONAL DATABASES IN THE OPTIMIZATION OF MARKET MATCHING

The paper examines the role of NoSQL databases in optimizing market matching processes, especially with regard to supply and demand coordination. Traditional relational databases are reliable with structured data, but they are not much use in handling large volumes and diverse types of data on today's dynamic markets. Scalable and flexible NoSQL databases allow Netflix to efficiently manage and analyze big volumes of unstructured and semi-structured data. NoSQL databases apply advanced algorithms and real-time data analytics, thus allowing for personalized recommendations that improve customer satisfaction and enhance user retention. The findings of the study point to the conclusion that the usage of NoSQL solutions has become imperative for companies intending to maintain their competitiveness by optimal data management and market matching.

PROV-IDEA: Supporting Interoperable Schema and Data Provenance within Database Evolution

Database evolution and data provenance are two closely related research fields. On the one hand, the registry (via provenance) of the schema evolution allows the maintenance of its version record. On the other hand, the origin of the data (i.e., its provenance) will always be affected by modifications (i.e., the evolution) in the schema on which they are based. Despite these interrelationships, there are few works in the literature that have proposed advances in that direction. In particular, to the best of our knowledge, there is no research that has resulted in a general and interoperable solution to the problem of managing database evolution using provenance. In this article, we present PROV-IDEA: a PROV-Interoperable Database Evolution Approach. This is a proposal that allows the simultaneous management of the provenance of schemas (of relational databases) and data, using the PROV standard as a way to guarantee interoperability. Furthermore, it is an adaptable and expandable approach (by using PROV templates), which allows a non-intrusive and seamless integration with existing applications, as well as different aspects of provenance information generation. These properties are demonstrated in the article by presenting a proof of concept built on top of a third-party relational database evolution tool.

FSOSM: An Operational Knowledge Empowered Scenario Model for the Intelligent Farmland Supervision

The automation of extracting targeted decision-support information is a key task for achieving intelligent agricultural management. Essentially, this involves structurally representing agricultural operations based on knowledge, unified modeling and relational management of elements such as natural resources, human–land relationships, and spatiotemporal data. However, the traditional farmland supervision systems based on relational and object-oriented databases struggle to effectively integrate, model, and apply operational knowledge such as project requirements, work experience, policies, and regulations. This limits their application efficiency and automation level. Therefore, this paper proposes a modeling method for Farmland Supervision Operations Scenario Model (FSOSM) based on structured operational knowledge. First, by analyzing the elements, structure, and functions of farmland supervision business scenario, the paper abstracts “natural resources—human society—spatiotemporal data” into 8 categories of scenario elements and 22 types of multidimensional semantic relationships. Next, the operational knowledge is structured and integrated into various modeling steps, including scenario element extraction, association, expression, and application, thereby enhancing the model’s intelligent service capability. Finally, the model is applied in practice through visualization and service applications using the “Farmland Non-Grain Conversion Supervision Operation Scenario of Guangdong Province, China” as a case study. The model’s practicality and superiority are demonstrated by comparing the processing flows and effects of this model and traditional farmland management systems in terms of efficiency, automation level, knowledge service capability, and versatility.

Constructing a mental health analysis system for social media using large language models

As social media platforms have penetrated every aspect of peoples lives, many mental health problems have also arisen along with them. In todays digital age, analyzing mental health trends through these platforms has become critical. In this study, we present a system designed to identify the mental health trends of Weibo users by extracting and analyzing the content posted by users on Weibo, Chinas leading social media platform. The system is mainly composed of two parts: a data acquisition module and an analysis module. The data collection module uses the Python-based web scraping tool Scrapy to scrape comments from popular topics on Weibo. At the heart of the analysis module is a large language model fine-tuned from a psychological database. The module assesses the topic and specific content of the posts, scoring comments based on criteria such as positivity, alignment with mood disorders, and potential signs of psychoactive substance use. This data is stored and mediated using the relational database MySQL, and then analyzed and visualized using advanced data analysis tools. Through this method, we can timely and comprehensively monitor the mental health status of social media platforms, and provide a solid foundation for further academic research on public mental health.

A system for the cataloguing of occupational history exhibits in the Piedmont region

Background. ARPA Piemonte carries out occupational exposure measurements of chemical agents in workplace environment to support the Occupatonal Health and Safety Units of Local Health Boards for the assessment of the exposure level of dangerous chemical. Data are reported in internal documents, but are not available for other purposes.Objectives. Organize the occupational exposure measures collected by ARPA Piemonte, in order to facilitate their availability, in accordance with the indications of the European Foundation for the Improvement of Living and Working Conditions.Methods. An application in a relational database environment was defined and developed to collect and structure information relating to companies, interventions, tasks and / or monitored sampling points, samples and measurements performed Results. Data on chemical exposure related to interventions carried out by ARPA Piemonte in the period from 2005 to part of 2020 have been collected in the ESP ALP application. ESP ALP contains information about 161 companies, 193 interventions, 815 sampling points, 3.490 samples and 18.576 measurements and information on companies obtained from the 2011 Census of Economic Activities and the archives of the National Institute for Insurance against Accidents at Work (INAIL). Discussion. We verified the feasibility of retrieving and organizing the information collected in past years in an integrated environment that facilitates its processing and use. Finally, we confirm the usefulness of collaboration between multidisciplinary professionals.

Extended Relational Database Model for Interval Probability Set-Valued Attributes

In this paper, we introduce a new probabilistic relational database model as an extension of the classical relational database model for interval probability set-valued attributes to represent and handle uncertain and imprecise information in practice. To develop the new model, we use extended probabilistic values for representing interval probability set-valued relational attributes and the probabilistic interpretation of binary relations on sets for computing uncertain degree of functional dependencies, keys and relations on attribute values, and propose the new combination strategies of extended probabilistic values for building probabilistic relational algebraic operations. A set of the properties of the basic probabilistic relational algebraic operations is also formulated and proven

Comparative Analysis of Table Aliasing in SQL Queries: Functional and Semantic Implications

Table aliasing is a widely used technique in SQL queries that enhances readability, scalability, and maintainability, particularly in complex queries involving multiple tables. This study examines the functional and semantic implications of aliasing, highlighting its role in improving query clarity, reducing redundancy, and facilitating long-term database management. While the benefits of aliasing are evident in large-scale applications, potential limitations exist. In simple queries, aliasing may be unnecessary and could even introduce confusion when non-descriptive or overly abbreviated aliases obscure the meaning of the query. Additionally, inconsistent alias usage can lead to readability issues and hinder collaboration among database developers. This paper provides a comprehensive analysis of aliasing best practices, explores its impact on query optimization across different relational database management systems (RDBMS), and discusses scenarios where aliasing may be either advantageous or counterproductive. The findings underscore the importance of adopting a strategic approach to aliasing to balance clarity and efficiency in SQL query construction.

Development of an openoffice application for working with simple relational database text format

This work presented the development of an OpenOffice application for working withsimple relational database text format. OpenOffice application automatically loads all CSVfiles in the form of tables into a document, each table is created on a separate tab, supportsautomatic setting of column width, according to the length of its name. It is very importantthat the OpenOffice application signals the user if he accidentally violates the restriction onthe uniqueness of the primary key. Composite primary keys are also supported. Thus, thisapplication simplifies the development, editing and maintenance of databases in CSVDBformat. Further development of this application is possible in the direction of supportingwork with foreign keys and creating similar applications for various types of table proces-sors.

Patient Frailty in Total Knee Arthroplasty: The Implementation of a Frailty Score Using an Electronic Medical Record.

Patient Frailty in Total Knee Arthroplasty: The Implementation of a Frailty Score Using an Electronic Medical Record.

MacaqueNet: Advancing comparative behavioural research through large-scale collaboration.

There is a vast and ever-accumulating amount of behavioural data on individually recognised animals, an incredible resource to shed light on the ecological and evolutionary drivers of variation in animal behaviour. Yet, the full potential of such data lies in comparative research across taxa with distinct life histories and ecologies. Substantial challenges impede systematic comparisons, one of which is the lack of persistent, accessible and standardised databases. Big-team approaches to building standardised databases offer a solution to facilitating reliable cross-species comparisons. By sharing both data and expertise among researchers, these approaches ensure that valuable data, which might otherwise go unused, become easier to discover, repurpose and synthesise. Additionally, such large-scale collaborations promote a culture of sharing within the research community, incentivising researchers to contribute their data by ensuring their interests are considered through clear sharing guidelines. Active communication with the data contributors during the standardisation process also helps avoid misinterpretation of the data, ultimately improving the reliability of comparative databases. Here, we introduce MacaqueNet, a global collaboration of over 100 researchers (https://macaquenet.github.io/) aimed at unlocking the wealth of cross-species data for research on macaque social behaviour. The MacaqueNet database encompasses data from 1981 to the present on 61 populations across 14 species and is the first publicly searchable and standardised database on affiliative and agonistic animal social behaviour. We describe the establishment of MacaqueNet, from the steps we took to start a large-scale collective, to the creation of a cross-species collaborative database and the implementation of data entry and retrieval protocols. We share MacaqueNet's component resources: an R package for data standardisation, website code, the relational database structure, a glossary and data sharing terms of use. With all these components openly accessible, MacaqueNet can act as a fully replicable template for future endeavours establishing large-scale collaborative comparative databases.

Third and Boyce–Codd normal form for property graphs

Normalization minimizes sources of potential data inconsistency and costs of update maintenance incurred by data redundancy. For relational databases, different classes of dependencies cause data redundancy and have resulted in proposals such as Third, Boyce–Codd, Fourth and Fifth Normal Form. Features of more advanced data models make it challenging to extend achievements from the relational model to missing, non-atomic, or uncertain data. We initiate research on the normalization of graph data, starting with a class of functional dependencies tailored to property graphs. We show that this class captures important semantics of applications, constitutes a rich source of data redundancy, its implication problem can be decided in linear time, and facilitates the normalization of property graphs flexibly tailored to their labels and properties that are targeted by applications. We normalize property graphs into Boyce–Codd Normal Form without loss of data and dependencies whenever possible, but guarantee Third Normal Form in general. Experiments on real-world property graphs quantify and qualify various benefits of graph normalization: (1) removing redundant property values as sources of inconsistent data, (2) detecting inconsistency as violation of functional dependencies, (3) reducing overheads for updates by orders of magnitude, and (4) significant speed ups of queries.

Enhancing epidemic preparedness: a data-driven system for managing respiratory infections

BackgroundEffective epidemic preparedness is critical for minimizing the health and societal impacts of viral respiratory infections. This study details the development of a data-driven early warning system (EWS) designed to improve outbreak detection and response utilizing the data integration and visualization capabilities of Microsoft Power BI.MethodsThis research utilized a structured three-phase approach to design a respiratory infections (RIs) management dashboard. Phase 1, focused on identifying critical variables through literature reviews and expert interviews. In Phase 2, Microsoft Power BI was employed for dashboard development, integrating data from diverse sources. Phase 3 involved usability testing with health professionals who evaluated navigation, data accuracy, decision-support features, providing feedback to enhance visualization clarity and filtering capabilities.ResultsKey data categories include individual-level variables, such as age, symptoms, and vaccination records, alongside population-level metrics like infection rates and regional vaccination coverage enabling functionalities such as identifying high-risk individuals, tracking infection dynamics, and optimizing resource allocation. The dashboard, developed using Power BI visualizes epidemiological trends, intervention outcomes, and resource utilization. A relational database schema ensures efficient data retrieval, facilitating comprehensive analysis.ConclusionThe prototype EWS represents a scalable and integrative framework aimed at enhancing public health applications, particularly in the context of respiratory infections. By incorporating data from diverse health sectors, the system offers decision-makers access to critical epidemiological indicators, supporting early outbreak detection and improved epidemic management. Its potential to unify health institutions underscores its value in fostering a more cohesive and effective approach to epidemic preparedness. Nevertheless, while the system demonstrates significant promise, further evaluation in real-world settings is essential to determine its practical impact on public health outcomes and its ability to mitigate health crises.

BuffExDb: web-based tissue-specific gene expression resource for breeding and conservation programmes in Bubalus bubalis.

Amidst the global challenge of extreme poverty, the livestock sector can significantly contribute to global sustainable development goals by enhancing resilience, smallholder productivity, and market participation. The Indian livestock sector is one of the largest in the world with a total livestock population of 535.82 million, ∼10.7% of the world's livestock population. Buffalo (Bubalus bubalis) holds significant importance in India and other Asian countries, notably contributing to their economies by surpassing cattle in milk production and providing various valuable products. The limited availability of genomic and transcriptomic resources for buffaloes hinders the efforts to enhance their traits for increased milk and meat production. To address this gap, this study adopted the state-of-the-art bioinformatics tools to analyse 2429 transcriptomes representing 438 BioSamples from 23 BioProjects obtained from a public domain database, representing 76 different types of tissues and cell types from all major organ systems in buffalo species (river and swamp). The outcome of this exhaustive genomic data led to the development of a relational buffalo expression database based on a three-tier architecture named as BuffExDb (http://46.202.167.198/buffex/). The user-friendliness and flexibilities in retrieval of tissue-specific genes (TSGs) and their functional annotation are the major characteristics of BuffExDb. This is the first of its kind that offers an effortlessly navigable and filterable database, enabling users to examine and visualize the expression levels of each tissue across multiple samples, simultaneously. It also provides the Tau score parameter for the identification of TSGs along with their essential roles in tissue development, maintenance, and function as observed through the enrichment test for gene ontologies. The exhaustive outcome of this work would pave the way for the biological, functional, and evolutionary studies for easy access. This prior information based on tissue-specific mechanisms can be used for future genomic research, especially in association studies in endeavour of enhanced buffalo breeding and conservation programmes. Database URL: http://46.202.167.198/buffex/.

Sesterterpenoids: sources, structural diversity, biological activity, and data management.

Reviewing the literature published up to October 2024.Sesterterpenoids are one of the most chemically diverse and biologically promising subgroup of terpenoids, the largest family of secondary metabolites. The present review article summarizes more than seven decades of studies on isolation and characterization of more than 1600 structurally novel sesterterpenoids, supplemented by biological, pharmacological, ecological, and geographic distribution data. All the information have been implemented in eight tables available on the web and a relational database https://sesterterpenoids.unige.net/. The interface has two sections, one open to the public for reading only and the other, protected by an authentication mechanism, for timely updating of published results.