Design Of Data Model Research Articles

Spatial Big Data and Analysis Strategies Supporting Geographic Information System for Transportation (GIS-T) in Conceptual Design, Modelling, and Decision-making: A Review

Abstract. Transportation is a key component in urban design for cities’ efficiency and residents’ life quality, and GIS has the capability of handling data management, model design, and scientific decision-making for transportation studies. This article reviews how spatial big data and analysis strategies help GIS-T studies from the phases of conceptual design, modelling, and decision-making. In this research, we firstly summarized categorizes of data objects and relevant information for real-world issues from transportation applications in the conceptual design phase. In the modelling phase, optimization strategies for transport planning and accessibility measures through network data were also summarized. Finally, we reviewed spatial analysis methods in supporting transport decision-making, and how spatial methods take advantage of geography features of transport variables in previous studies. Our research primarily focuses on geography research with transportation topics as applications, and this work can help transport experts to have a better understanding of GIS values for transportation modelling and planning.

Bolt loosening status classification via wave energy dissipation measurements with temperature-compensated deep learning by piezoelectric active sensing

Abstract In industrial applications, bolts, serving as crucial components, endure substantial loads and are susceptible to loosening problems exacerbated by intricate external environmental factors. The active sensing method based on wave energy dissipation exhibits pronounced sensitivity to axial load fluctuations in bolts and demonstrates extensive applicability, with its measurement indicators directly applicable for discerning the bolt’s status. However, environmental factors, notably temperature, can significantly influence signal energy measurements, and the oversight of temperature impact may result in erroneous state discrimination. To tackle this challenge, this paper introduces a cascaded model comprising a temperature compensation subnetwork and a bolt state discrimination subnetwork. The temperature compensation subnetwork takes temperature and signal energy as inputs and outputs the temperature-compensated signal energy, and conveys the outcomes to the bolt state discrimination subnetwork for state classification. In model design, we quantitatively analyzed the number of convolutional blocks and training epochs for the temperature compensation subnetwork with the aim of enhancing the model’s generalization ability, ultimately determining the model architecture. By comparing the experimental results between a single-task model and a model incorporating the temperature compensation subnetwork, we verified the effectiveness of the temperature compensation subnetwork. The experimental outcomes demonstrate that the proposed cascaded model achieved a state classification accuracy of 98.6% on over 1300 temperature-generalized data points. To comprehensively assess our approach, we conducted detailed comparisons with other bolt loosening monitoring methods, elucidating the effectiveness based on data trends and model design. Experimental results demonstrate that the proposed temperature compensation cascaded model accurately identifies bolt states amidst complex temperature variations.

Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments

Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential.

Implementation of Artificial Neural Network in Predicting CPO Prices Using Backpropagation

This research examines the development of a forecasting model for Crude Palm Oil (CPO) prices using artificial neural network algorithms, particularly the backpropagation algorithm. CPO, as Indonesia's main export commodity, has significant economic impacts and affects the income of oil palm farmers. Data on CPO prices taken from CIF Rotterdam from January 2019 to December 2023 were used in this study. The research method involved several stages, including data collection, pre-processing, model design, and model implementation using Python programming. The results of training the model using the backpropagation algorithm showed an error value of 0.537829578 after 1000 epochs, while evaluation using Mean Squared Error (MSE) showed an MSE value of 0.022709 during the training process and 0.017604 during the testing process. The model also produced predictions for CPO prices in the next three months: 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model can predict future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis

This paper explores the design and optimization of multidimensional data models to enhance the query performance and data analysis capabilities of OLAP (Online Analytical Processing) systems. It delves into three prominent dimensional modeling techniques: Star Schema, Snowflake Schema, and Galaxy Schema, analyzing their impact on query complexity, data redundancy, storage requirements, and ease of maintenance. Additionally, it examines three aggregation strategiesPre-Aggregation, Dynamic Aggregation, and Hybrid Aggregationfocusing on their effectiveness in balancing query response time, storage efficiency, flexibility, and computational cost. The study further investigates performance optimization techniques, including query optimization, partitioning, and materialized views, providing case studies and experimental data to illustrate their benefits and challenges. The findings underscore the importance of tailored optimization strategies in OLAP systems to meet varying business needs and query patterns, highlighting the trade-offs between performance gains, storage requirements, and implementation complexity

Design of a news recommendation model based on collaborative filtering and LSTM

This paper aims to design a news recommendation model based on user preferences to address the issues in recommendation systems under large datasets. Initially, four datasetsclick_history, news, news_embedding, and user_predictwere integrated into a single table, followed by data cleaning and feature engineering. Due to the large volume of data, this paper proposes necessary data filtering for the training and testing sets, utilizing temporal data to construct user feature vectors and news feature vectors. One challenge is how to effectively integrate user preferences and news features into the model to avoid overfitting or underfitting. In the model design and building phase, different methods were attempted to merge the information of users and news. Ultimately, the user preference features were processed using a fully connected layer, and the news embedding vectors were handled using an LSTM model. These two data parts were then combined into another fully connected layer, using ReLU as the activation function and CELoss as the loss function. Subsequently, the model's hyperparameters were adjusted and evaluated, achieving favorable model performance. The prediction accuracy for recommending news to users in user_predict was calculated as an evaluation criterion. Finally, this paper proposes directions for generalization and optimization in three aspects: data processing, model design, and experimental design. This includes data processing methods, potential improvements or mechanisms that could be incorporated into the model, and hyperparameter tuning. The paper primarily proposes data filtering to solve the problem of excessive data scale, which may aid in addressing recommendation system issues under large-scale datasets.

디지털트윈 전시형 콘텐츠 개발을 위한 엔티티 기반 3차원 예술작품 데이터모델링 개선방안 연구

Recently, a number of virtual reality exhibition-type content services have been produced using archive resources of visual art records as a means of promoting cultural policy-based public companies. However, it is by no means easy to accumulate 3D works of art as data. Looking at the current state of metadata in public institutions, there was no digitalization of resources when developing digital twins because it was built based on old international standards. It was found that data modeling evolution is inevitable to connect multidimensional data at a capacity and speed that exceeds the functions of existing systems. Therefore, the elements and concepts of data modeling design were first considered among previous studies. When developing virtual reality content, when it is designed for the migration of 3D modeling data, the previously created metadata was analyzed to improve the upper elements that must be added to 3D modeling. Furthermore, this study demonstrated the possibility by directly implementing the process of using newly created metadata in virtual reality content in accordance with the data modeling process. If this study is gradually developed in the future, metadata-based data modeling can become more meaningful in the use of public data than it is today.

An optimized relational database for querying structural patterns in proteins.

A database is an essential component in almost any software system, and its creation involves more than just data modeling and schema design. It also includes query optimization and tuning. This paper focuses on a web system called GSP4PDB, which is used for searching structural patterns in proteins. The system utilizes a normalized relational database, which has proven to be inefficient even for simple queries. This article discusses the optimization of the GSP4PDB database by implementing two techniques: denormalization and indexing. The empirical evaluation described in the article shows that combining these techniques enhances the efficiency of the database when querying both real and artificial graph-based structural patterns.

Modelling of spatial planning systems with LADM standard: the case in Turkish regulatory planning system

Spatial planning includes various decisions that guide the formulation of land policies, such as planned urbanisation, environment-nature-culture protection, and safe agriculture. Land Administration Systems (LASs) should also include spatial planning decisions as they play a role in facilitating the implementation of land policies. In this study, the Turkish spatial planning system's role in the land administration is discussed. The paper presents the necessity of designing and standardising the spatial planning system as data model. This data model design includes not only spatial plan decisions but also documents that guide spatial planning and land development decisions caused by planning. Therefore, this study aims to present a standardised model of the Turkish spatial planning system and the land use rights, restrictions and responsibilities established by spatial plans. Designing spatial planning system information as a conceptual data model within the Land Administration Domain Model is expected to support land administration system-based improvements.

Deep learning in digital health with chatgpt: a study on efficient code generation

Abstract Background/Introduction Artificial Intelligence (AI) plays a significant role in the healthcare industry by providing innovative solutions to many health-related problems. Deep learning (DL) is a critical component in the development of AI-powered solutions; however, extensive knowledge is required to implement DL processes such as data preparation, model design, model training, and model evaluation. This has limited the widespread adoption of AI in the medical field, particularly in scenarios where non-technical professionals possess limited coding proficiency. Purpose This study evaluates the feasibility of using ChatGPT, a large language model developed by OpenAI, as an assistive tool for non-technical professionals to develop DL applications. The objective is to reduce the time and effort required in source code creation, while simplifying the overall process of application development. Methods ChatGPT was utilised to generate relevant source code for the creation of a DL application for echocardiogram view classification. Natural language input prompts were fed through the ChatGPT interface to produce modular scripts that fulfilled the purposes of video frame extraction, image pre-processing, dataset splitting, model training, and model evaluation. Input prompts were specified as a series of logical steps which explained the desired functionality, along with the necessary software libraries to be integrated. Results Experiments on code generation involved iteratively modifying input prompts through the rewording of sentences, reordering of steps, and usage of different libraries, to obtain working source code from ChatGPT responses. Results demonstrated that ChatGPT can generate fully usable programming scripts from natural language inputs, without the need for any modifications, to accomplish the various tasks involved in the DL process flow. Experiments were also conducted on locally sourced dobutamine stress echocardiogram videos, to verify script functionality and output reliability. The scripts successfully extracted, pre-processed, and split the video frames into training, validation, and testing sets. These datasets were then employed in the training and evaluation of a convolutional neural network which achieved an accuracy of above 80% when classifying seven different echocardiogram views. Conclusion ChatGPT has the potential to significantly reduce the time and effort required in developing DL applications. The successful implementation of the programming scripts suggest that ChatGPT can function as an assisting tool for non-technical professionals. Its ability to generate operational source code and streamline the development process could facilitate greater adoption of AI in healthcare and other industries. Future works shall explore the usage of ChatGPT in generating solutions to more heart-related use cases.Code generation and DL process flow

RANCANG BANGUN SISTEM INFORMASI MANAJEMEN KARYAWAN

The employee management information system at Nazra Swim School (NSS) was created with the aim of improving the performance and service of employees at Nazra Swim School (NSS). Nazra Swim School has a lot of data, one of which is employee data that needs to be stored and processed. Employee data processing is currently still done manually. To facilitate the processing of employee data, an information system is needed to process employee data so that the resulting information is more accurate, efficient and precise. This employee management information system is done using the waterfall method. This information system design includes document flowcharts, system flowcharts, data flow diagrams, context diagrams, database design, conceptual data model (CDM), physical data model (PDM) and user interface design. The employee management information system is designed using the programming language PHP, Notepad++ and MySQL database. implementation of a web-based employee management information system can help administration and employees in managing employee data automatically.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction, low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm (DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling (LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the real-time intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

A New Approach to 3D Digital Evaluation and Analysis In Geological Engineering

The initial geological analysis of hydropower projects is greatly complicated by the existence of complex geological conditions. To adapt advances in informatization and digitalization to the needs of the engineering survey industry and to conduct geological analysis more intuitively, in the present study the development of “GeoBIM”, a geological 3D design platform based on the DSI modeling engine and a “graphics + data” architecture is rerported. GeoBIM realized the integration of 3D information in the whole process of geological work, from field data collection, model design, and analysis to approval. Taking the siting and construction of a hydropower station as an example, the field data collection and cataloging APP (boreholes, caverns, slopes) and web-side exploration monitoring platform of GeoBIM were used to build a 3D geological fine visualization model. The principle of dissipated energy was used to analyze the quality of the rock mass, which showed the spatial distribution characteristics of local geological conditions and rock mass quality, improving the efficiency and intuitiveness of engineering rock mass quality classification. GeoBIM was compared with traditional 2D geological analysis methods and actual conditions to demonstrate the rationale and intuitive use of the 3D digital evaluation method in geological engineering. Bangladesh J. Bot. 52(2): 679-688, 2023 (June) Special

Sandpile-simulation-based graph data model for MVD generative design of shield tunnel lining using information entropy

BIM standard development is central to the performance and behavior of BIM model application across transmission, visualization, and information management perspectives. Tremendous effort has been made to ease the implementation of IFC data model in practice. Yet, the complexity of IFC data model hurdles the implementation of the import and export functionality by software vendors. To overcome this, buildingSMART introduced the concept of Model View Definitions to define which parts of an IFC data model need to be implemented for a specific data exchange scenario. With such, the certification of compatibility for software products with the IFC standard is formed. The Model View Definition is use case orientated to determine whether the specific information should be included in an IFC partial model. With the creation of ad-hoc, project-specific Exchange Requirements increasing, associated MVD development requires much more work to incorporate standard development. To resolve this issue, this paper attempts to exploit the potential of information entropy which has proven itself extremely crucial in many other industries in terms of information management, and then integrates it with sandpile simulation to propose a Top-down hierarchy to structure as well as interpret IFC partial model via Model View Definition. The proposed information entropy shifted MVD development approach would manage to unify the MVD development process that enables the reduction on confusion for various end users, specific organization, or project needs. Moreover, to better translate the BIM standard topology into sandpile simulations, a new notion system is proposed. Sandpile simulations are further implemented to prove their applicability, during the simulation, self-organized criticality is identified, and the existence of chaos is observed.

Open HD map service model: an interoperable high-Definition map data model for autonomous driving

ABSTRACT With the growth in the vehicle industry, autonomous driving has become a hot topic worldwide and has attracted increasing attention from both industrial and academic sectors. Maps, as pivotal geospatial information carriers, play a vital role in route planning and navigation service. Compared with conventional maps, high-definition (HD) maps possesses higher precision, richer information, and various services and are regarded as critical infrastructure for autonomous driving. However, heterogeneous HD map data standards and models have different characteristics and advantages, and thus they rarely meet all autonomous driving requirements for different driving objectives. This research presents an interoperable map data model, the Open HD Map Service Model (OHDMSM), to provide a reference for HD map development. The designed OHDMSM, which contains three data layers and a set of corresponding interfaces, demonstrates high interoperability for HD map data fusion and application. As a proof of concept, an HD map data system is implemented with all functions following the designed data model and interfaces of OHDMSM. The design and development of OHDMSM data structures, interfaces and systems will benefit data requesting, updating, and interoperation for HD map data worldwide, which can be helpful for developing autonomous driving and intelligent transportation in the Digital Earth.

Integrating Computational Thinking and Data Science: The Case of Modding Classification Games

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games’ content, leading them to develop a critical stance towards the game data model and representations.

A non-functional requirements-based ontology for supporting the development of industrial energy management systems

Non-functional requirements (NFRs) are essential in the development of industrial energy management systems (IEMS). This paper presents a comprehensive ontology model for NFRs in IEMS, derived from an extensive survey of NFRs in both the software industry and the energy domain. The proposed ontology encompasses four critical factors influencing the quality attributes of IEMS: technologies, stakeholders, markets, and regulations. Implemented using the OWL 2 DL standard, the ontology model aims to provide a clear and consistent understanding of NFRs and their relationship to the energy domain, as well as identify which factors have a significant impact on the environmental performance of IEMS. The ontology is evaluated through various methods, such as technical validation, user evaluations, its applications in RDF data management, and application-based evaluation, including software architecture, knowledge base data model design, and regulatory framework design. By understanding the link between software quality requirements and the characterizing factors of the energy domain, as provided by our ontology model, this information can be used to inform life cycle assessments and quantify potential reductions in energy consumption, emissions, waste generation, and other environmental impacts associated with implementing cleaner and more sustainable solutions. The results demonstrate that the proposed ontology effectively supports IEMS development and serves as a foundation for developing reusable and adaptable software systems across different industrial domains.

Investigation of the Effectiveness of Usage of Graph Databases for Big Data Analysis

The purpose of research. The purpose of this work is to study graph models of databases and develop a methodology for comparative analysis of database models. The theoretical and methodological basis of the study was the fundamental scientific works of domestic and foreign authors in the field of basic problems of database theory, algorithm theory, graph theory, data processing structures and methods.Methods. The paper uses methods of structural, comparative and content analysis, as well as statistical methods of information processing and methods of graph theory. As a result of the conducted research, the authors justified the features, advantages and disadvantages of using a graph data model.Results. The relevance of this study is due to the intensive development of information technologies intended for the economic development of the country, the pandemic and the geopolitical situation in the world. These prerequisites orient researchers to use new methods of data processing and analysis. However, it is possible to optimize big data processing processes not only with the help of powerful new algorithms, but also with the use of fundamentally different data structures and models other than relational.The paper presents applied examples of using the graph model of databases in various subject areas. A method of comparative analysis of data models in relation to big data analysis has been developed. The main points of data model design are highlighted: system scaling, compliance with requirements and standards, the ability to change data model structures, language complexity, performance and data processing speed. The proposed technique made it possible to numerically evaluate the effectiveness of graph models. Conclusion. The theoretical significance of the research consists in the development of methodological and technological approaches to the analysis of big data and the formation of structures and databases. The practical results of the study can be useful to large IT companies, as well as to the financial, logistics and commercial sectors, where the problem of big data analysis and research is most acute.

Breast Cancer Screening Should Embrace Precision Medicine: Evidence by Reviewing Economic Evaluations in China.

The cost-effectiveness of conventional population-based breast cancer screening strategies (e.g. mammography) has been found controversial, while evidence shows that genetic testing for early detection of pathogenic variants is cost-effective. We aimed to review the economic evaluations of breast cancer screening in China to provide an information summary for future research on this topic. We searched the literature to identify the economic evaluations that examined breast cancer screening and testing in China, supplemented by hand-searching the reference lists of the included studies. We finally included five studies satisfying our inclusion criteria. Four articles examined mammography while the rest investigated multigene testing. The existing breast cancer screening programmes were found to be cost-effective among urban Chinese women, but one study concluded that they might cause harm to women in rural areas. Contextual factors, such as data absence, urban-rural disparity, willingness-to-pay threshold, and model design, imposed barriers to cost-effectiveness analysis. Multigene testing was found to be cost-effective and has a promising population impact among all women with breast cancer in China. Future research should investigate the cost-effectiveness of screening and identifying breast cancer through precision medicine technologies, including genetic testing, genome sequencing, cascade testing, and the return of secondary findings.