Database Schema Research Articles

A Mobile App Development for E-Waste ‎Management in Bahrain (Athar)‎‎

Bahrain still suffers from electronic solid waste annually due to ineffective waste ‎disposal management. Compared to other Gulf Cooperation Council (GCC) countries, Bahrain ‎generates the largest waste quantity per person, approximately 1.2 to 1.8 ‎million tons of hazardous waste annually. Therefore, the present research article aims to manage the e-waste discarding mitigation by arranging the waste collection, ‎presenting it for sale/purchase/donation (C2C, C2B, B2G), and organizing the waste recycling ‎process. Derived from this aim, the article explores the e-waste ‎phenomena and proposes the development of an e-waste mobile application named “Athar”. ‎ The significance of “Athar” lies in firstly, enabling citizens to live in a ‎clean environment with limited diseases. Secondly, assisting the ‎Ministry of Tourism attract more tourists. ‎ The research article embraces a seven-phased Agile-based SDLC method to analyze, design, implement, test, and evaluate Athar through the utilization of several research techniques, including questionnaires to collect user and system requirements, other techniques such as data flow diagrams, entity relationship diagram, ‎database schema, etc. for the system design, ‎OutSystems programming language to implement the mobile app, and one questionnaire based on ‎Nielsen heuristics for usability evaluation. Findings demonstrate the adequacy of the Athar application with an outstanding usability score of 89.1%.

Enhancing Healthcare Informatics: Integrating Category Theory Reasoning into OMOP-CDM Ontology Model.

The task of managing diverse electronic health records requires the consolidation of data from different sources to facilitate clinical research and decision-making support, with the emergence of the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) as a standard relational database schema for structuring health records from different sources. Working with ontologies is strongly associated with reasoners. Implementing them over expansive and intricate Ontologies can pose computational challenges, potentially resulting in slow performance. In this paper, we propose the implementation of a new reasoner based on categorical logic over a translation of OMOP-CDM into an ontology model. This enables enhancements to the efficiency and scalability of implementing such models.

Quality Assessment Framework of Clinical Routine Data for Secondary Use.

To systematically and comprehensively identify data issues in large clinical datasets, we adopted a harmonized data quality assessment framework with Python scripts before integrating the data into FHIR® for secondary use. We also added a preliminary step of categorizing data fields within the database scheme to facilitate the implementation of the data quality framework. As a result, we demonstrated the efficiency and comprehensiveness of detecting data issues using the framework. In future steps, we plan to continually utilize the framework to identify data issues and develop strategies for improving our data quality.

Symposium 1: Analyzing Special Needs Support through Networks and Learning Analytics in Primary Education

In this short paper, analytical issues of networked learning are related to special needs support and learning analytics research in primary education. Very brief suggestions for these analytical issues are discussed for the networked learning research field to continue to expand upon. In part, the discussion is intended to form the underpinnings of a work-in-progress research project with cross-disciplinary networked connections across multiple universities. Currently, teachers are faced with an expansion of their professional role that may be too economically demanding, which may result in consequences that is not beneficial for children with special needs. Researchers that aim to study inclusive education face the challenge to represent findings in a way that does not increase workload for teachers in primary education. Any failings in this regard may strengthen the intertwining of educational results and economical interest, which to a large extent could be considered as opposed to networked learning theory, dualistically separated from pedagogical concerns. The inclusive goal of addressing diversity for children with special needs may be promoted by providing an educational setting with networked connections that enhances variation to modes of behavior, by addressing specific and contextual strengths and challenges of students appropriately. With learning analytics, specific and contextual strengths and challenges of students might be easier to highlight with new database schemas, algorithms, and user interfaces to represent networked data. Radical behaviorist methodology may further emphasize specific contextual findings from primary education through strictly defined events of behavior, which may be of interest to the networked learning research community to further expand upon. Thus, this paper proposes suggestions for networked learning research of primary education, with approaches commonly implemented in special education, such as radical behaviorist methodology. The suggestions are based on networked consequences from three social dimensions: political, economic, and technology-developmental. 1 The political dimension is discussed through the notion of inclusive education for children with special needs and its current state, 2 the economic dimension is discussed through the 21st century role expansion of teachers, 3 the technology-developmental dimension is discussed through learning analytic research and its connection to bodily and biological associations. This dimension is especially noteworthy, as learning analytics is currently underutilized in primary education research as opposed to common higher education research.

APPOINTMENT MAKER USING COMPUTERIZED VOICE

The Appointment Maker Using Computerized Voice is a novel system designed to streamline the process of scheduling appointments in healthcare settings through the integration of voice recognition and natural language processing technologies. By leveraging speech-to-text and text-to-speech capabilities, the system enables users to interact with the appointment booking interface using spoken commands, eliminating the need for manual input and enhancing accessibility for individuals with diverse technological proficiencies. This intuitive interface simplifies the appointment scheduling process, allowing users to book, reschedule, or cancel appointments with ease. Furthermore, the Appointment Maker employs machine learning models to understand and interpret user queries, providing accurate responses and assistance in real-time. By leveraging pre-trained language models and fine-tuning them on domain-specific data, the system demonstrates robust performance in understanding diverse user inputs and generating contextually relevant responses. This capability enhances user experience by providing personalized and timely assistance, akin to interacting with a human receptionist.The Appointment Maker features a backend database for storing and managing appointment and user information, ensuring data integrity, security, and efficient resource utilization. The relational database schema facilitates seamless coordination between users, healthcare providers, and administrative staff, enabling efficient scheduling and management of appointments. Overall, the Appointment Maker Using Computerized Voice represents a significant advancement in healthcare administration, offering a user-friendly and efficient solution for appointment scheduling while improving accessibility and patient experience. Key Words: Computerized Voice ,Natural Language Understanding (NLU), Speech to Text, Text to Speech.

Knowledge-based query system for the critical minerals

Critical minerals are increasingly used in advanced, modern technologies. Exploration for these minerals require efficient mechanisms to search for the latest geological knowledge about the petrogenesis and spatial distribution of these essential resources. Although the current text-based deposit classification schemes help geoscientists to understand how and where these critical minerals form, they cannot easily be queried by software without extensive natural language processing and knowledge modeling. Ontologies can explicitly specify the knowledge scattered in the texts and tables of these schemes and the Critical Minerals Mapping Initiative (CMMI) database by way of logical structures whose results can automatically be processed and queried. They can also draw new knowledge by inference from the ones that are explicitly specified in them. These qualities make ontologies a perfect choice for digital knowledge storage, search, and extraction. The Critical Minerals Ontology (CMO) is described herein by reusing the logical class and property structures of the top-level Basic Formal Ontology (BFO) and mid-level Common Core Ontologies (CCO) and Relation Ontology (RO). The CMO formally models the knowledge about the critical mineral systems using the latest deposit classification scheme and the CMMI database schema. The ontology specifies the geochemical and geological processes that operate in various geotectonic environments of mineral systems to form the critical minerals in different deposit types. It models the properties of both the host minerals that contain the rare-earth elements and those that bear other types of elements. The CMO also represents uses of specific critical minerals in the manufacturing of industrial products, their alternate substitutes, and countries that produce, import, and export them. A query system, applying the Python programming language, accesses the knowledge modeled in the CMO and allows users through interactive web pages to query the ontology and extract different types of information from it. The ontology and the query system are useful for research in ore mineralogy and critical mineral prospecting. The information modeled by the ontology and served by the query system allows users to classify their ore specimen data into specific deposit types.

An empirical hybrid strategy for NoSQL database distribution

A case study of a pharmaceutical distribution company with a relational database that is migrated to a document-oriented NoSQL database for which a hybrid distribution strategy based on fragmentation and replication is implemented. The legacy relational database schema was thoroughly analyzed and evaluated to determine the collections and documents needed to move all the data and leverage the capabilities of the document-based database. In addition, a hybrid data distribution strategy was designed to effectively balance the performance, scalability, and fault tolerance of the system. Finally, integrated operational tests were performed on the distributed system nodes to verify that they function properly and ensure their availability for the company's operations.

Consistency of Relations over Monoids

The interplay between local consistency and global consistency has been the object of study in several different areas, including probability theory, relational databases, and quantum information. For relational databases, Beeri, Fagin, Maier, and Yannakakis showed that a database schema is acyclic if and only if it has the local-to-global consistency property for relations, which means that every collection of pairwise consistent relations over the schema is globally consistent. More recently, the same result has been shown under bag semantics. In this paper, we carry out a systematic study of local vs. global consistency for relations over positive commutative monoids, which is a common generalization of ordinary relations and bags. Let K be an arbitrary positive commutative monoid. We begin by showing that acyclicity of the schema is a necessary condition for the local-to-global consistency property for K-relations to hold. Unlike the case of ordinary relations and bags, however, we show that acyclicity is not always sufficient. After this, we characterize the positive commutative monoids for which acyclicity is both necessary and sufficient for the local-to-global consistency property to hold; this characterization involves a combinatorial property of monoids, which we call the transportation property. We then identify several different classes of monoids that possess the transportation property. As our final contribution, we introduce a modified notion of local consistency of K-relations, which we call pairwise consistency up to the free cover. We prove that, for all positive commutative monoids K, even those without the transportation property, acyclicity is both necessary and sufficient for every family of K-relations that is pairwise consistent up to the free cover to be globally consistent.

A Portable and Reusable Database Infrastructure for Mass Spectrometry, and Its Associated Toolkit (The DIMSpec Project).

Nontargeted analysis (NTA) is a rapidly growing field of techniques that includes the identification of unknown chemical analytes in complex mixtures such as environmental, biological, and food matrices. The use of reference mass spectral databases is a key component of most NTA workflows, providing a high level of confidence for chemical identification when analytical standards are not available, yet effective interlaboratory sharing of research grade spectra remains challenging. The Database Infrastructure for Mass Spectrometry (DIMSpec) project focused on the creation of an open-source toolkit supporting storage and sharing of high-resolution mass spectra with attached sample and methodological metadata. As a demonstration of its utility, the DIMSpec toolkit was used to create a database of curated mass spectra for per- and polyfluoroalkyl substances (PFAS) generated from various sources. While the underlying toolkit is agnostic to analytical targets, this initial release (along with the database schema, mass spectral data, and database tools) should enable PFAS researchers to use these data for their own studies, including the identification of novel PFAS in the environment.

ORDerly: Data Sets and Benchmarks for Chemical Reaction Data.

Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market. Chemical reactions play a significant role in these fields, but there is a lack of high-quality open-source chemical reaction data sets for training machine learning models. Herein, we present ORDerly, an open-source Python package for the customizable and reproducible preparation of reaction data stored in accordance with the increasingly popular Open Reaction Database (ORD) schema. We use ORDerly to clean United States patent data stored in ORD and generate data sets for forward prediction, retrosynthesis, as well as the first benchmark for reaction condition prediction. We train neural networks on data sets generated with ORDerly for condition prediction and show that data sets missing key cleaning steps can lead to silently overinflated performance metrics. Additionally, we train transformers for forward and retrosynthesis prediction and demonstrate how non-patent data can be used to evaluate model generalization. By providing a customizable open-source solution for cleaning and preparing large chemical reaction data, ORDerly is poised to push forward the boundaries of machine learning applications in chemistry.

Efficient schema-less text-to-SQL conversion using large language models

Large language models (LLMs) are increasingly being applied to several tasks including text-to-SQL (the process of converting natural language to SQL queries). While most studies revolve around training LLMs on large SQL corpora for better generalization and then perform prompt engineering during inference, we investigate the notion of training LLMs for schema-less prompting. In particular, our approach uses simple natural language questions as input without any additional knowledge about the database schema. By doing so, we demonstrate that smaller models paired with simpler prompts result in considerable performance improvement while generating SQL queries. Our model, based on the Flan-T5 architecture, achieves logical form accuracy (LFA) of 0.85 on the MIMICSQL dataset, significantly outperforming current state-of-the-art models such as Defog-SQL-Coder, GPT-3.5-Turbo, LLaMA-2-7B and GPT-4. This approach reduces the model size, lessening the amount of data and infrastructure cost required for training and serving, and improves the performance to enable the generation of much complex SQL queries.

Ethical Encrypted Search Scheme for Medical Database

E-medical records are highly sensitive and require secure storage, typically in encrypted form. However, encrypting the records can make them difficult to search and utilize within existing medical database systems. Additionally, managing access to these records, especially when multiple authorities are involved, presents challenges in ensuring security and scalability.To address these issues, we propose an authorized searchable encryption scheme designed for a multi-authority setting. This scheme utilizes the RSA (Rivest-Shamir-Adleman) function to enable each authority to control and limit search capabilities based on clients' privileges. Furthermore, to enhance scalability, we employ multi-authority attribute-based encryption, allowing the authorization process to be streamlined even across policies from multiple authorities.We have conducted thorough security and cost analyses, as well as experimental evaluations, demonstrating that our proposed scheme introduces only moderate overhead to existing searchable encrypted systems. Keywords--e-medical system, cloud storage, forward security.

Grader system built on ruby on rails

This paper presents a Ruby on Rails-based grader application system designed to streamline the process of matching students with grading positions within the Computer Science and Engineering (CSE) department. Motivated by the need for efficiency and consistency, the system offers role-based user access, enabling students, instructors, and administrators to engage seamlessly. Leveraging Model-View-Controller (MVC) architecture, the system integrates external tools for enhanced development, while the dynamic database schema efficiently manages data. Key functionalities encompass application submission, administrator interface, and real-time course management. This innovative system fosters collaboration, improves administrative oversight, and adapts to changing academic demands. In conclusion, the presented grader application system has achieved powerful functions which enables users to login, view available courses, and most importantly, accept or decline the applications from students whom want to be a grader for a specific course.

Infrastructure for 3D Modeling of Historical Fountains in Istanbul with GIS-Based Procedural Approach

Along with the concepts of “smart city” and “digital twin,” the 3D (threedimensional) city models have started to be used as a basis for many studies that have been carried out in recent years. The most important and basic components of 3D city models are the structures that are located in the cities. However, some small structures of historical importance are within the category of the buildings in a city but are often not as large as a building. Historical fountains are some of these structures. For this reason, modeling with existing3D city model-production methods is a little more complicated and requires local measurements and detailed modeling studies.As a result, this study has designed a database scheme design in which the historical city wall fountains in Istanbul can be stored in a spatial database and modeled in three dimensions (with procedural modeling when necessary). In addition, the conveniences that this infrastructure will provide in the production of 3D structure models and some difficulties that were encountered during these studies are also discussed and examined.

From Data to Decisions: Enhancing Retail with AI and Machine Learning

Purpose: The recent advancements in computational power have presented unprecedented opportunities for businesses to harness data. A noteworthy development in December 2022 was the introduction of OpenAI's [1] ChatGPT, signifying the rise of generative AI tools including, but not limited to, Bard [2], Midjourney [3], GitHub Copilot [4], Amazon Bedrock, and Google's Gemini [5]. This research paper aims to harness AI capabilities within retail organizations, using data (customer) to expand business reach and enhance customer satisfaction. Data and AI form the core of this research.
 Methodology: In this research, we have trained a Large Language Model (LLM) by providing it with database schemas, including tables, to interact with centralized data and gain insights through simple prompts. We can leverage data for data Analysis and create reports, dashboards, understand customer behavior.
 Findings: Our research findings that AI serves as a pivotal force in amplifying the retail industry's potential. AI's applications span from improving customer experience by enabling voice orders, emotional insight, exclusive Deals just for you, product design, email campaign, optimizing inventory to facilitating targeted marketing strategies, list goes on. Yet, as we navigate this AI-augmented retail landscape, it is imperative to address challenges related to data privacy, algorithmic bias, implementation costs, and the need for expertise.
 Unique contributor to theory, policy and practice: In essence, generative AI is more than a fleeting trend; it epitomizes the future of retail, demanding both adoption and circumspection. Our recommendation to use AI for enhancing retail business and use it ethically.

Development and application of Chinese medical ontology for diabetes mellitus

ObjectiveTo develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.Materials and methodsWe used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering.ResultsThe current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes.ConclusionCDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

3D Cadastral Database Systems—A Systematic Literature Review

Cadastral databases have been used for over 20 years, but most contain 2D data. The increasing presence of high-rise buildings with modern architecture complicates the process of determining property rights, restrictions, and responsibilities. It is, therefore, necessary to develop an efficient system for storing and managing multidimensional cadastral data. While there have been attempts to develop 3D cadastral database schemas, a comprehensive solution that meets all the requirements for effective data storage, manipulation, and retrieval has not yet been presented. This study aims to analyse the literature on 3D cadastral databases to identify approaches and technologies for storing and managing these data. Based on a systematic literature review integrated with a snowballing methodology, 108 documents were identified. During the analysis of the related documents, different parameters were extracted, including the conceptual data model, query type, and evaluation metrics, as well as the database management system (DBMS) used and technologies for visualisation, data preparation, data transformation, and the ETL (extract, transform, and load) process. The study emphasised the importance of adhering to database design principles and identified challenges associated with conceptual design, DBMS selection, logical design, and physical design. The study results provide insights for selecting the appropriate standards, technologies, and DBMSs for designing a 3D cadastral database system.

GTR: An SQL Generator With Transition Representation in Cross-Domain Database Systems.

Recent studies have focused on using natural language (NL) to automatically retrieve useful data from database (DB) systems. As an important component of autonomous DB systems, the NL-to-SQL technique can assist DB administrators in writing high-quality SQL statements and make persons with no SQL background knowledge learn complex SQL languages. However, existing studies cannot deal with the issue that the expression of NL inevitably mismatches the implementation details of SQLs, and the large number of out-of-domain (OOD) words makes it difficult to predict table columns. In particular, it is difficult to accurately convert NL into SQL in an end-to-end fashion. Intuitively, it facilitates the model to understand the relations if a "bridge" transition representation (TR) is employed to make it compatible with both NL and SQL in the phase of conversion. In this article, we propose an automatic SQL generator with TR called GTR in cross-domain DB systems. Specifically, GTR contains three SQL generation steps: 1) GTR learns the relation between questions and DB schemas; 2) GTR uses a grammar-based model to synthesize a TR; and 3) GTR predicts SQL from TR based on the rules. We conduct extensive experiments on two commonly used datasets, that is, WikiSQL and Spider. On the testing set of the Spider and WikiSQL datasets, the results show that GTR achieves 58.32% and 71.29% exact matching accuracy which outperforms the state-of-the-art methods, respectively.

Extracting structured data from organic synthesis procedures using a fine-tuned large language model.

The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD "messages" (e.g., full compound, workups, or condition definitions) and 92.25% for individual data fields (e.g., compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification.

Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub.

Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets. The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves. Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research. The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.