Entity-relationship 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.

SRank: Guiding schema selection in NoSQL document stores

The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores.

КОМПЛЕКСНАЯ ИНФОЛОГИЧЕСКАЯ МОДЕЛЬ ОБЪЕКТОВ ЗАЩИТЫ КОНФИДЕНЦИАЛЬНОЙ ИНФОРМАЦИИ

The problem of harmonization of approaches to the definition of information security objects is becoming increasingly relevant due to the growth of information arrays, the complexity of information technologies, technical and cryptographic means of information protection, and the increasing number of computer attacks, including on critical information infrastructure. Historically, the concept of objects of protection in Russia is associated with the requirements of state regulators, so it has different interpretations. The purpose of the study is to develop an infological model of information security objects. The goal of infological modeling may be to provide the most natural ways for a person to collect and present information about objects of protection for its consideration when creating protection systems and storing in a database. The author based on the analysis of the requirements of the main regulators of the Federal service according to the technical and export control of Russia and the Federal security service of Russia and the modeling method, an infological model of information security objects has been developed. This model is most consistent with the concept of object-oriented design, which is the basis for the development of complex software systems, the development of technology for designing databases of information security objects based on the ER-model. The entity-relationship model (ER-model) has several basic concepts, from which more complex objects are built according to predefined rules.

A Systematic Overview of Fuzzy Extended Entity Relationship (Fuzzy EER) Model Using Practical Examples

Most real-world data are vague and imprecise, so traditional relational databases cannot represent, integrate, and manage them effectively. In this research, we gave an overview of the application of fuzzy logic to the conceptual modeling facet of database design, which is an enhancement of the Extended Entity Relationship (EER) model. This is done to give a clearer picture of how imprecise and vague data are represented and managed, especially in multiple-criteria decision-making database systems. To achieve this, an overview of fuzzy attributes, fuzzy values in the attributes, notation for fuzzy representations, fuzzy degree in each value of an attribute, fuzzy degree in a set of values of different attributes, fuzzy degree with its own meaning and fuzzy degree to the model was carried out with practical examples.

Unifying heterogeneous and homogeneous relations for personalized compatibility modeling

In recent years, the fashion industry has developed rapidly, leading to an increasing demand for personalized clothing matching. Although noteworthy progress has been made by existing work with advanced graph neural networks, there still remain three prominent challenges: comprehensive entity relation modeling, adaptive neighbor node sampling, and thorough characteri-zation of fashion items based on multi-modalities. To solve the above problems, we propose a novel personalized compatibility modeling framework, which explores both heterogeneous (i.e., item-user and user-item) and homogeneous (i.e., item-item and user-user) relation graphs with multi-modalities. Moreover, we design a correlation-guided neighbor sampling strategy to learn the correlation-aware subgraphs, which plays a crucial role in facilitating the exploration of compatibility relationships or personalized preferences. Finally, we conduct extensive experiments on two publicly available fashion datasets to demonstrate the superiority of our proposed model over existing benchmark methods.

On the Question of Authorship in Large Language Models

Adoption of pre-trained large language models (LLMs) across an increasingly diverse range of tasks and domains poses significant problems for authorial attribution and other basic knowl­edge organization practices. Utilizing methods from value-sensitive design, this paper examines the theoretical, practical, and ethical issues introduced by LLMs and describes how their use challenges the supposedly firm boundaries separating specific works and creators. Focusing on the implications of LLM usage for higher education, we use hypothetical value scenarios and stakeholder analysis to weigh the pedagogical risks and benefits of LLM usage, assessing the consequences of their use on and beyond college campuses. While acknowledging the unique challenges presented by this emerging educational trend, we ultimately argue that the issues associated with these novel tools are indicative of preexisting limitations within standard entity-relationship models, not wholly new issues ushered in by the advent of a relatively young technology. We contend that LLM-generated texts largely exacerbate, rather than invent from scratch, the preexisting faults that have frequently posed problems to those seeking to determine, ascribe, and regulate authorship attributions. As the growing popularity of generative AI raises concerns about plagiarism, academic integrity, and intellectual property, we advocate for a reevaluation of reductive work-creator associations and encourage the adoption of more expansive authorial concepts.

Designing PostGIS Database System with Fuzzy Theory to Support Accessibility Tools for Urban Pedestrians

The combination of fuzzy logic and crowdsourcing can be a powerful tool for generating geospatial data for pedestrians with mobility challenges in urban areas. Although potentially useful, information about the accessibility of paths that is generated through crowdsourcing is susceptible to a high degree of imprecision. Spatial data management is required for such systems, which supports the management of uncertain data. Fuzzy theory allows us to model ambiguous information. To fill this gap, an improved method based on a fuzzy relational PostGIS database (FPostGIS) is proposed. The method includes extensions to represent imprecise data within an entity-relationship (ER) data model specifically tailored for path accessibility, and a set of steps for the derivation of FPostGIS from this extended ER model. According to the case study, this methodology has been applied in the design and development of decision support application within the Maps for Easy Paths (MEP) project. This application stores and retrieves accessibility information about a particular path and allows performing spatial operations and analysis inside the database.

Gas Store Data Analysis Using ERD Method and Constitutional Data Warehouse Model

A data warehouse is a data storage system that plays a crucial role in business analysis. It collects, integrates, and stores data from multiple sources in a structured format, providing holistic insight into organizational performance. Entity-Relationship Model (ERD) is a visual tool for designing database structures. It uses entities to represent real-world objects and the relationships between them. ERD helps plan an efficient and coherent database design. A conceptual model is an abstract visual representation of information structures and relationships within a domain. It covers key concepts and business rules, assisting in building a solid foundation of understanding before technical designing begins. All three are interrelated in the development of successful information systems. Data warehouses use conceptual models to direct effective data storage design, while ERD helps describe the entities and relationships to be stored in the data warehouse. The combination of all three enables organizations to design, develop, and maintain adequate information systems, based on a deep understanding of data and its relationships. This results in better decision making, more efficient innovation, and optimal utilization of resources. The purpose of this study is to produce optimal data using the ERD method. The main objective is to explain how much data in an information system in the Company and how data management is crucial for effective decision making.

Are NoSQL Databases Affected by Schema?

During data modelling, NoSQL databases are usually considered schema-less databases. They do not support rigid schema as traditional databases. Instead, offer flexibility and support dynamic schema, which means schema design can be changed at run time to support analytical tasks. NoSQL databases allow storing similar information with multiple schemas, which facilitates data evolution by eliminating restrictions on the schema. It raises the question of whether the schema matters in NoSQL databases, given that explicit schema declaration is not required before data storage. Furthermore, if the schema does matter, what is its potential impact on application performance? This paper examines these questions by taking three types of NoSQL databases: Document store, Column store, and Key-Value store. All possible schema structure alternatives are designed using a sample Entity Relationship (ER) model. Then, a set of common queries are performed on various schema alternatives to analyse the impact of query performance in terms of query response time, speedup factor, read and write Latency, and database size. The results suggest that the scheme alternatives affect the database performance in different NoSQL models.

Analisis Teknik Entity-Relationship Diagram Dalam Perancangan Database

The purpose of this research is to find out how to develop the data entity relationship model in the data based data management system. Where the entity relationship is a system that is important for the company or as a liaison to explain the relationship between data and database systems. There are many techniques used in database design. One of them is Entity Relationship Diagram (ERD). ERD is one of the main diagrams representing the conceptual data model that reflects the user data requirements in the database system. ERD is the first stage in database design. There are several things that must be considered in making ERD for database design. Each database must have entities that are interconnected and each entity must have attributes consisting of a primary key as the entity's characteristics and descriptive attributes. Many users fail to create databases due to errors in ERD creation. Conceptual errors, procedural errors, or technical errors. This article was created using the SLR method, namely reviewing various kinds of references that discuss database design, then drawing conclusions to make it easier for users to overcome problems that occur when making ERD and implementing them.

DESIGN OF CASH SALES ACCOUNTING INFORMATION SYSTEM USING RESOURCE EVENT AGENT MODEL AS TOOL IN EDDY MOTOR OFFICIAL WORKSHOP

The role of information systems for accounting is very large because it changes from manual recording to computerized records, so the need for a good database design is essential. A database that meets the normalization rules is needed to support a computerized Accounting Information System (AIS). The Entity Relationship Model (E-R Model) is commonly used to design databases. However, drawing diagrams’ rules are unclear, making it difficult for the data designer to form a database that meets the normalization rules. The REA model is a development of the E-R Model. The REA model was first conceptualized as a framework for building accounting systems in a shared data environment both within companies and between companies. The core feature of the REA model is an object pattern consisting of two mirror images representing the semantics of a business process's input and output components (give-to-get), making it easier to build a data model.

PRIVACY AND ONLINE SOCIAL NETWORK

The popularization of Information and Communication Technologies allowed new forms of interaction, including Online Social Network (OSN) services. These services could share personal data with third-party organizations through Application Programming Interfaces (API), making it a complex task to observe the data flow. This research proposes a model to identify levels and elements available in the flow of personal data via API between organizations holding OSN services and external agents. It converged the recurrent mechanisms into providing data sets from the OSN services to external agents from a systematized reading of the technical documentation to establish a database model to fetch unitedly the possibility of storing information about the levels in which API provided personal data to external agents, applying the Entity-Relationship Model. Subsequently, it presents a Data Mart as a proof of concept for the proposed database model, intending to compare the ways of accessing the personal data attributes stored and, following, shared by the OSN services to external agents. It showed that the model could clarify the elements inherent in the data flow, allowing a more structured analysis, including the possibility of monitoring changes in the API by organizations over time.

PRIVACY AND ONLINE SOCIAL NETWORK

The popularization of Information and Communication Technologies allowed new forms of interaction, including Online Social Network (OSN) services. These services could share personal data with third-party organizations through Application Programming Interfaces (API), making it a complex task to observe the data flow. This research proposes a model to identify levels and elements available in the flow of personal data via API between organizations holding OSN services and external agents. It converged the recurrent mechanisms into providing data sets from the OSN services to external agents from a systematized reading of the technical documentation to establish a database model to fetch unitedly the possibility of storing information about the levels in which API provided personal data to external agents, applying the Entity-Relationship Model. Subsequently, it presents a Data Mart as a proof of concept for the proposed database model, intending to compare the ways of accessing the personal data attributes stored and, following, shared by the OSN services to external agents. It showed that the model could clarify the elements inherent in the data flow, allowing a more structured analysis, including the possibility of monitoring changes in the API by organizations over time.

ER+: A Conceptual Model for Distributed Multilayer Systems

Distributed databases and data transformation mechanisms are highly relevant to Business Intelligence and Data Analytics. Most enterprise systems today have multiple and distributed databases, and the growing dissemination of Edge-Oriented architectures is driving this trend across many industries and business domains. The Entity-Relationship (ER) model is fundamental to modeling complex enterprise systems, but it has shortcomings. In particular, the ER model cannot represent data transport between different locations (or databases) of a system, nor can it conceptually express data transformation operations, such as aggregate and line functions, that are standard in data analytics. Therefore, we propose ER+, an extension of the ER model, where data distribution, data transport, data transformation, and information generation for distributed operational and analytical systems can be visually identified. The new ER+ representation has another important benefit, it provides the basis for transforming conceptual models into physical implementations of distributed data-oriented systems. This work introduces new concepts in ER modeling and illustrates their application. The TPC-H use case is also used to demonstrate the the practicality of the ER+ model.

Creation of intelligent information decision support systems

The use of intelligent information decision support systems implies considering the problem area's specifics. The object of study is characterized by the following set of features: - quality and efficiency of decision-making; - vagueness of goals and institutional boundaries; - the plurality of subjects involved in solving the problem; - randomness; - a plurality of mutually influencing factors; - weak formalizability, uniqueness of situations; - latency, concealment, the implicitness of information. For the efficient and reliable functioning of agricultural facilities and enterprises, it is necessary to create and implement intelligent information systems. Over the past quarter of a century, domestic information systems have undergone a progressive evolution, both in terms of developing the theoretical principles of their construction and implementing these systems. The restructuring of agriculture, the market conditions for the functioning of objects, and agriculture enterprises have their characteristics and problems. Building the structure of intelligent decision support information systems is primarily associated with building a system model, in which both traditional elements of the control system and knowledge processing models should be defined. To solve these problems, methods of system analysis were used. The key research method is the optimization of data representation structures of databases and knowledge. The following relational data representation structures have been identified: relations, attributes, and values. In the relational model, structures are not specially allocated to represent data about entity relationships. Semantic networks use a three-level representation of data on entities and a four-level representation of data on entity relationships. The conducted studies have shown that in data representation structures, entity-relationship models are a generalization and development of the structures of all traditional data models since only in this data model there are 4-level data representations of both entities and relationships. All other traditional models are some special cases of the most general entity-relationship model.

A SERM based framework to optimize the identification of mandatory relationships

The SERM (Structured Entity Relationship Diagram) data model is an extension of the classic ER (Entity Relationship) model. Its transparent, hierarchical structure allows you to control and analyze the order in which information resources are entered into the database. Assuming attention to referential integrity, it has a significant impact on the verification of relationship complexity. Uncertainty in the identification of obligatory relations, confirmed by numerous corrections, demonstrates the need to develop an approach to support this task. The aim of this article is to present a solution in the form of a framework based on the SERM model. Its main aspects are optimization of the identification of obligatory relations and automatic handling of their protections. The proposed solution supports intelligent design of relational databases beyond conceptual design. It extends the scope to the design of database programming objects, such as procedures that generate triggers to handle obligatory relationships. This is an important aspect for securing database integrity.

Simulation of Database Interactions for Early Validation of Digitized Enterprise Processes

Digitized enterprise processes often encompass interaction with relational databases. Describing and simulating large-scale and complex processes on different abstraction levels lead to the use of tools and methods of Model-based Systems Engineering. In practice, current entity-relationship modeling approaches solely enable modeling relational database structure without simulation of database interactions at an early development stage. However, in general, it is known that early validation improves common understanding and communication in the development team and reduces the risk of design flaws. This paper presents an approach for model-based enterprise process digitization and a previously developed and now enhanced broker-based SysML Toolbox for integrating real relational databases into SysML simulations. The approach comprises status quo documentation concerning enterprise processes, development of digitized processes and required relational database structures as well as validation of digitized processes using the SysML Toolbox.

Consistent Design of Relational Databases using EERCASE

This article introduces EERCASE, a Computer Aided Software Engineering tool that is based on the best practices of the Model Driven Development paradigm to provide a consistent environment for relational database design. EERCASE follows the graphical notation of the Enhanced Entity–Relationship model according to Elmasri and Navathe, implements the EERMM metamodel to avoid syntactically invalid constructs, shows and describes static semantic errors, and generates data definition code that takes into account advanced structural validations. The theoretical and technical framework used for the implementation of EERCASE is discussed, with emphasis on the restrictive and informative validations performed by it. In addition, considering feedbacks on modeling errors and code generation, EERCASE is also presented as a computational environment that favors active learning.

Database Conceptual Diagram Using ERD and Data Dictionary Metadata for SME Construction Business

The data of construction business such as customers, projects, payments, workers, materials, and suppliers are very essential to the contractors especially to the Small and Medium-sized Enterprises (SME) construction companies. The customers’ request and confirmation for constructions projects, which workers involves in which project and what are the materials selected for the project are important to handle but still been manage manual. Some of the information requested, discussed or ordered are missing, misplaced or overlooked, especially when there are many concurrent constructions projects which to be completed at the same time. Therefore, the project aims to design a database of construction business for accelerating the information flow and transaction between each construction phase. The conceptual diagram has been designed and constructed using Entity relationship modelling and continue referred to the Database Life Cycle (DBLC) as methodology approach. The SME Construction management of Entity Relationship Diagrams (ERD), consist of the Customer, Project, Payment, Supplier and Material entities which constructed based on SME Construction Business rules and process validation. The findings can assist the researchers, students, system designers and developers to understand more on designing the database conceptual diagram and identify the data elements in the data dictionary which required for construction business database. Furthermore, the findings from this research project can be used to develop a SME construction management system database and SME information system in the future.

Formalization of Ontology Conceptualizations Using Model Transformation

Conceptual models are built with concepts and relationships between them to reach a unified view of domain problems. There are many kinds of conceptual models developed in different modeling languages, such as class diagrams and entity-relationship models. In this paper, the authors have developed a specific meta-model following the Ecore standard to define conceptual models. These domain-specific conceptual models can be automatically formalized as domain ontologies using model transformation with the technique of triple graph grammars into ontology formal descriptions in accordance with the defined Ecore meta-model of the language OWL (web ontology language). For ontology deployment, its OWL code may be generated from OWL models using model-to-code transformation guided by Xpand templates. A performance evaluation is realized using a benchmark from the university domain with very large conceptual models. Through the experiments, they validate the performance and we prove the exactness and the scalability of the automatic transformation process of conceptual models.