Database Management System Research Articles

Local calibration of JPCP transverse cracking and IRI models using maximum likelihood estimation

The calibration of transfer functions is essential for accurate pavement performance predictions in the Pavement-ME design. Several studies have used the least square approach to calibrate these transfer functions. Least square is a widely used simplistic approach based on certain assumptions. Literature shows that these least square approach assumptions may not apply to the non-normal distributions. This study introduces a new methodology for calibrating the transverse cracking and international roughness index (IRI) models in rigid pavements using maximum likelihood estimation (MLE). Synthetic data for transverse cracking, with and without variability, are generated to illustrate the applicability of MLE using different known probability distributions (exponential, gamma, log-normal, and negative binomial). The approach uses measured data from the Michigan Department of Transportation's (MDOT) pavement management system (PMS) database for 70 jointed plain concrete pavement (JPCP) sections to calibrate and validate transfer functions. The MLE approach is combined with resampling techniques to improve the robustness of calibration coefficients. The results show that the MLE transverse cracking model using the gamma distribution consistently outperforms the least square for synthetic and observed data. For observed data, MLE estimates of parameters produced lower SSE and bias than least squares (e.g., for the transverse cracking model, the SSE values are 3.98 vs 4.02, and the bias values are 0.00 and -0.41). Although negative binomial distribution is the most suitable fit for the IRI model for MLE, the least square results are slightly better than MLE. The bias values are -0.312 and 0.000 for the MLE and least square methods. Overall, the findings indicate that MLE is a robust method for calibration, especially for non-normally distributed data such as transverse cracking.

Impacts of data consistency levels in cloud-based NoSQL for data-intensive applications

When using database management systems (DBMSs), it is common to distribute instance replicas across multiple locations for disaster recovery and scaling purposes. To efficiently geo-replicate data, it is crucial to ensure the data and its replicas remain consistent with the same and the most up-to-date data. However, DBMSs’ inner characteristics and external factors, such as the replication strategy and network latency, can affect system performance when dealing with data replication, especially when the replicas are deployed far apart from the others. Thus, it is essential to comprehend how achieving high data consistency levels in geo-replicated systems can impact systems performance. This work analyzes various data consistency settings for the widely used NoSQL DBMSs, namely MongoDB, Redis, and Cassandra. The analysis is based on real-world experiments in which DBMS nodes are deployed on cloud platforms in different locations, considering single and multiple region deployments. Based on the results of the experiments, we provide a comprehensive analysis regarding the system throughput and response time when executing reading and writing operations, pointing out scenarios where each DBMS could be better employed. Some of our findings include, for instance, that opting for strong data consistency significantly impacts Cassandra’s reading operations in the single-region deployment, while MongoDB writing operations are most affected in a multi-region scenario. Additionally, all of these DBMSs exhibit statistically significant variations across all scenarios in the multi-region setup when the data consistency is switched from weak to stronger level.

Scaled Sensitivity of Pavement–Mechanistic-Empirical Transfer Function Coefficients for Flexible and Rigid Pavements

The American Association of State Highway and Transportation Officials (AASHTO)Ware Pavement–Mechanistic-Empirical (ME) transfer functions need local calibration for reliable performance predictions. It is often not viable to calibrate all coefficients at the same time. Therefore, it is crucial to identify the most sensitive transfer function coefficients. Moreover, the sensitivity also indicates the impact of each coefficient on the performance prediction. Several studies have shown the sensitivity of the Pavement-ME design inputs, but limited research is available on the sensitivity of transfer function coefficients. Typically, the sensitivity is obtained using a normalized sensitivity index (NSI). This paper estimated the sensitivity of the Pavement-ME transfer function coefficients using scaled sensitivity coefficients (SSCs). NSI values are compared with the SSCs. Ten sections, each from flexible and rigid pavements, were used to calculate the NSI values. These are existing pavement sections from the Michigan Department of Transportation (MDOT) pavement management system (PMS) database, designed using the 1993 AASHTO design guide. Results show that SSCs provide a more reliable sensitivity on a range of independent variables rather than a point estimate, unlike NSI. NSI values showed variability among different sections and magnitudes of predicted performance. Calculation of SSCs is a forward problem and does not require any input data. A mathematical model (transfer function) is needed; therefore, SSCs can be calculated on any range of independent variables. It also shows the potential correlation between different coefficients and accuracy in estimation.

Efficient Data Management Strategies for NoSQL and Relational Databases in Cloud Environments:An In-Depth Analysis

As data volumes grow exponentially, managing and storing this information efficiently becomes increasingly critical. NoSQL databases have emerged as a preferred solution for handling unstructured data, outperforming traditional relational database management systems (RDBMS) in scalability and flexibility. Techniques such as deduplication (removing duplicate data) and compression, combined with powerful tools like Hadoop and MongoDB, facilitate optimized storage and reduced network usage. However, the challenge of schema evolution in both NoSQL and SQL systems introduces risks of downtime and data inconsistency. This paper reviews current methods, highlights gaps in existing solutions, and proposes an integrated approach to ensure efficient data management across diverse database environments while minimizing downtime and enhancing data integrity.

GENERALIZED DATASET OF GEOLOGICAL AND GEOPHYSICAL INFORMATION ON THE EASTERN SECTOR OF THE RUSSIAN ARCTIC FOR MACHINE LEARNING – BASED ANALYSIS

The article presents a practical approach to the geological and geophysical spatial data collection and preliminary processing to use in machine learning models for geophysical applications. According to the established principles for estimating efforts in data analysis, which are confirmed by the results of surveys among specialists, this stage is viewed as major time and resource-consuming, amounting up to 80% in total volume of data analysis for a hypothesis testing project. The paper focuses on creating a consistent data set that integrates geological and geophysical information on a given region. We consider problems of different sources in the geodata representation to be related to their format (vector/raster), scale, type of attribute information (quantitative/qualitative) and their availability. The algorithm formalization and synthesis for combining geospatial data and converting them into quantitative vectors is a critical aspect. Combining various data draws on the concept of neighborhood fitting in with the data selection techniques and data consolidation strategy. The paper presents the general architecture of the software and hardware complex which includes a module for data collection and transformation in Python using the Pandas library, a data storage system based on the PostgreSQL DBMS (Database Management System) with the PostGIS extension. It is shown that for the considered class of problems in geophysics, it is sufficient to use a relational DBMS for data storing and processing. If the problem dimension increases, it is proposed to use the Big Data technology based on Apache Hadoop for scaling the system. A practical application of the proposed approach is demonstrated as results of data collection for the Caucasus region and eastern sector of the Russian Arctic. Based on the prepared data, experiments were carried out using machine learning models for recognition of locations of potential strong earthquakes and for sensitivity estimation of several geophysical features of these regions. The article presents the experimental results and evaluation of their efficiency.

Open Source Adoption for Digital Transformation and Data Management During the COVID-19 Crisis

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

USING THE METHOD OF WEIGHTED RESIDUALS IN THE FEM FORM FOR THE APPROXIMATION OF TWO-DIMENSIONAL DISTRIBUTIONS

The paper is devoted to the description of the method of two-dimensional approximation of data using algorithms of the method of weighted residuals with piecewise specified basis functions in the implementation of the Finite Elements Method. The mathematical formulation of the problem and the main relations of the method for two-dimensional regions are provided. A triangular finite element is involved in the simulation. Information is given about the program developed according to the algorithms of this method, the result of which is the selection of regions of a two-dimensional figure with the specified values of the analyzed parameters. By use of randomly distributed coordinates, the verification of the results for different data sets was performed and a satisfactory correspondence of the approximated and input data was obtained. Additional software, including programs written in Python and a web application created using the Laravel framework in PHP, has been developed to create a database that is scheduled to be processed by approximation. The PyQt5, OSMnx, requests, aiohttp, BeautifulSoup libraries and the PostgreSQL database management system were used to implement the project tasks. Algorithms of the developed software are described. An example of the reproduction of the locations of cities on the map of Ukraine is provided. The developed program of two-dimensional approximation was applied to the analysis of spatio-temporal dynamics of the emergence of cities on the territory of Ukraine. As an example, data on the founding of cities in the period from 800 to 1200 AD were considered and the main urbanized regions at that time were determined. The results of this Finite Element approximation were obtained for the purpose of preparing data for further modeling of sociodynamic processes in the terrain of Ukraine. The obtained results in the form of coordinates of the borders of regions that had a high level of urbanization can be used in various scientific studies, primarily in historical sciences.

The pattern of antibiotics prescription for endodontic infections in Malaysia: Cross sectional survey study.

This study aimed to assess the antibiotic prescribing pattern for endodontic infections among general dental practitioners (GDPs) and endodontic specialists in Malaysia. A 22-questions survey on demographic and general information on antibiotic prescribing patterns for endodontic infection was delivered to the email addresses of general dentists and specialists via the Dental Practitifoner Information Management System database. Collected data were analysed using multivariate logistic regression tests at the significance level of 0.05. A total of 372 responses were collected. 94% of respondents were GDPs, and 6% endodontists. Clinical experience and frequency of endodontic treatment performance were the significant factors in prescribing antibiotics (p < 0.05). The common reasons for antibiotic prescription were to control the spread of infection and fever (88%). Endodontists have better knowledge of antibiotic prescription than GDPs. The majority of GDPs had inappropriately prescribed antibiotics. Improving the knowledge of GDPs in Malaysia regarding antibiotics-indications in endodontics is mandatory.

Using Time-Series Databases for Energy Data Infrastructures

The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.

NoSQL Technologies in Big Data Ecosystems: a Comprehensive Review of Architectural Paradigms and Performance Metrics

The exponential growth of data in volume, velocity, and variety has challenged traditional relational database management systems, leading to the emergence and widespread adoption of NoSQL and other innovative database technologies. This article provides a comprehensive review of NoSQL databases and their role in modern Big Data infrastructure stacks. Through a systematic analysis of current literature and industry case studies, we explore the architectural paradigms, performance metrics, and use cases of various NoSQL database types, including document-based, key-value, column-family, and graph databases. Our findings indicate that NoSQL solutions offer significant advantages in scalability, flexibility, and real-time processing capabilities, particularly for unstructured and semi-structured data. However, challenges persist in areas such as data consistency, security, and interoperability with existing systems. We also examine emerging trends, including NewSQL, time-series databases, and the integration of artificial intelligence in database management. This article contributes to the understanding of how organizations can leverage NoSQL technologies to optimize their Big Data infrastructure, highlighting both the opportunities and considerations for implementation. Our conclusions underscore the transformative impact of NoSQL on data management practices and provide directions for future research in this rapidly evolving field.

Optimisasi Pengelolaan Karyawan dan Proyek Melalui Sistem Berbasis Web di PT Raja Perkasa

PT Raja Perkasa, a company operating in the construction sector, faces challenges in managingoperational activities and employee performance. This research aims to develop a web-basedmanagement system as a solution to address these issues. The developed application is designed topresent the company’s portfolio, manage partner data, handle partnership proposals, overseeproject data, manage employee records, facilitate attendance tracking, manage leave requests,resignation submissions, and issue employee warnings. The application development utilizes theLaravel framework and MySQL as the database management system, with the goal of improvingthe efficiency and effectiveness of company information management. The Waterfall method isused for the application development, encompassing the stages of requirements analysis, design,coding, testing, deployment, and maintenance. The application, after successful testing in thedeveloper’s local environment, was then hosted for direct user access. In the maintenance phase,bug fixes were implemented, and user satisfaction data was gathered through questionnairescompleted by 23 respondents representing seven user roles: admin, project manager, owner, HR, employees, clients, and visitors. The Likert scale analysis resulted in an average satisfaction levelof 87.14%. The research findings indicate that the application provides a positive user experience.However, further development is needed to add features such as employee recruitment, financialmanagement, and project scheduling, with a focus on sensitive data security. Therefore, thisapplication is expected to strengthen PT Raja Perkasa’s position in the construction industry andenhance project management and employee productivity

BIM/IFC as input for registering apartment rights in a 3D Land Administration Systems – A prototype webservice

The need for 3D Land Administration Systems (LAS) is growing. In this respect, research is carried out in the field of 3D LAS with respect to data sources, registration of 3D Rights, Restrictions and Responsibilities and dissemination services. Within this context, BIM/IFC models are considered promising sources for 3D LAS, even though the reuse of such models from practice has not yet been examined adequately. Evaluating BIM/IFC-models from practice is crucial, since they are created for different purposes, fulfilling various design criteria. This paper investigates the technical challenges encountered when using real-world BIM/IFC-models for apartment rights’ registration in a 3D LAS. It addresses the validation of five Dutch real-world BIM/IFC-models against four technical criteria, namely: existence of IfcSpace; geometric validity; no overlap and georeferencing. The results of the validation show that the collected BIM/IFC-models lack georeference, IfcSpace and a reference to attributes related to the respective legal units in the Dutch 3D LAS. After validation the models are stored in a 3D LAS Database management system (DBMS), in which the legal spaces are enriched with information of the Rights, Restrictions and Responsibilities (RRR’s) in line with the Land Administration Domain Model (LADM - ISO 19152:2012). The contents of the 3D LAS DBMS are visualised in a web viewer. Additionally, the design for a webservice is introduced, aiming to automate the process of validation, conversion and visualisation. The paper concludes with recommendations and guidelines for creators of BIM/IFC-models based on the outcome of the validation, as well as challenges and recommendations for implementing a validation webservice.

Effect of Management Information System on Growth of Agro-Firms in Anambra State

The study examined the effect of management information systems on the growth of agro-firms in Anambra State. The objectives were to: determine the effect of decision-making information systems on the Return on Investment (ROI) of agro-businesses in Anambra State, Nigeria, and ascertain the extent to which database management systems affect the ROI of agro-businesses in Anambra State, Nigeria. The study adopted a survey research design, with data generated from both primary and secondary sources. The data collection method was a questionnaire, which was randomly administered to staff of selected agro-firms. The population of the study was 6,727, while the sample size was 1,293, out of which 929 questionnaires were retrieved. The hypotheses were tested using the multiple regression analysis method at a 0.05% level of significance. The findings revealed that decision-making information systems had a significant effect on the ROI of agro-businesses in Anambra State, with a t-statistic of 7.743 and a probability value of 0.000. Additionally, database information systems had a significant effect on the ROI of agro-businesses in Anambra State, with a t-statistic of 2.476 and a probability value of 0.013. The study concluded that management information systems have a significant positive effect on agro-firms in Anambra State. It recommended that agricultural firms should invest in advanced decision support tools to aid data-driven decisions that maximize ROI. Furthermore, agro-business managers should focus on optimizing their database information systems to improve data organization and accessibility. Regular maintenance and updates of the database systems are crucial for ensuring accurate and timely information, which can positively impact ROI.

Database Construction and Application of Large-scale Geospatial Data

Abstract. Geospatial data refers to data that related to spatial location. It is the abstract representation of objects, events, or other characteristic information located on or near the surface of the Earth. It characterizes attribute features, spatial features, and associated information of geospatial features. Geospatial data commonly include various data types such as point, polyline, polygon, and so on. Geospatial database is the basic method to store and manage geospatial data. It is also the important component of geographic information systems (GIS). Geospatial database generally stores data in a specific structure in a medium, which can achieve efficient management of geospatial data. Users can add, modify, and delete data efficiently. Furthermore, query retrieval and statistical analysis can be quickly performed based on geospatial database. In this paper, distributed geospatial database is constructed by using database partition tables storage technology. Meanwhile, database construction software is developed to achieve the construction, management, and application of large-scale geospatial data. It can improve data import, query and retrieval efficiency significantly. The database application service system can directly connect to the database for displaying statistical analysis results. At the same time, geospatial data are processed to vector tiles, which stored in the tile database. Furthermore, standard online service is then published, which can be used for database management and application service system. It can be used for natural resource management work such as basic surveying, survey and monitoring, and so on.

Risk Profile of Stalking in South Korea: Analyzing the First Year of Court Decisions Following Initial Legal Responses.

South Korea implemented specific legislation titled the Act on Punishment of Crimes of Stalking (APCS) on October 21, 2021, to address the issue of stalking. This study evaluated the risks associated with stalking incidents in South Korea by reviewing and analyzing legal cases with finalized judgments within the first year of the APCS. Data were collected through a systematic search of the Korean Supreme Court's Written Judgment Management System database. We identified 193 stalking-related cases between October 21, 2021, and October 14, 2022. These cases were analyzed based on four risk profile criteria: profiles of stalkers and victims, the nature of stalker-victim relationships, motivations behind stalking, and patterns of stalking behavior. Further, we reviewed cases to which the APCS and the Criminal Act applied by analyzing stalker-victim relationships and the stalkers' underlying motivations to assess the risks associated with stalking. Approximately 16.6% of stalkers were diagnosed with mental disorders, the most common disorder being psychosis. The average age of the victims was 42.84 years, with a higher prevalence of female victims compared to male ones. Predominantly, stalkers were former intimate partners of the victims, with most cases motivated by rejection. Direct contact methods, such as physical approaches, have been observed more frequently than indirect methods, such as repeated unwanted messages, or non-contact behaviors, including surveillance and loitering. The findings provide an up-to-date overview of the under-examined criminal stalking issue in South Korea.

A Decade of the Database Systems Research with the Lens of PODS, SIGMOD, TODS, and TKDE: A Bibliometric Perspective

Database management systems are an essential research field in data engineering, data mining, and computer science. The development and research in this field have evolved from manual bookkeeping to automatic data storage, rapidly growing with time. In this bibliometric study, the articles from four premier venues of publication in the field of database systems have been collected for analysis, including ACM Special Interest Group on Management of Data SIGMOD, a well-reputed symposium PODS (Principles of Database Systems) and two journals of the domain namely: Transactions on Database Systems (TODS) by ACM, and Transactions on Knowledge and Data Engineering (TKDE). This study includes the articles published between 2010 and 2019 in the selected venues to perform bibliometric analysis. Both TKDE and TODS are highly reputed journals in the field of database systems but publish quite different research. The SIGMOD conference began in 1975 and specializes in the research area of databases and data management problems, whereas PODS is the research conference that specializes in research related to database theory. This study presents the analysis of the collected articles from three major aspects: 1) metadata analysis, 2) content-based analysis, and 3) citation-based analysis. Furthermore, it also looked at the leading institutes, countries, and authorities based on article citation and publication count. As a whole, this study proposes a methodology for performing the extensive bibliometric analysis for the database management system and conducts a bibliometric analysis for the aforementioned top publication venues in this area of research.

Analysis of Local Innovation Productivity Agents Within Micro and Small Businesses in The Trade, Industry and Services Segments of The Federal District

Objective: To analyze the effects of the Local Innovation Agent – ​​Productivity program on the adoption of innovative practices and its impacts on the productivity of small businesses monitored in the Federal District in 2023. Theoretical Framework: Innovation is a central element for economic development, a strategy to make companies efficient and competitive in the markets in which they operate, through various types of changes in management and production processes, with results that impact productivity and commercial performance. Method: This research adopted a quantitative-exploratory approach based on the ALI Management System database, which uses the Innovation Radar tool to measure the impact on SMEs and bibliographic research focused on the culture of innovation in organizations. Results and Discussion: The results obtained revealed that the companies monitored during the program generally improved their scores in the dimensions analyzed. The results demonstrate that the commerce, industry and services sectors have different impacts and that innovation is still a topic that requires support and access for small businesses. Research Implications: The data collected in this study show the effectiveness of the Local Innovation Agent Program - Productivity in inducing the adoption of innovative practices by small businesses. It is a fact that by adopting measures to monitor entrepreneurs, it is possible to encourage reflection on the part of managers of these businesses so that they prioritize innovative measures that assist and result in business management capable of facing the adversities of market competition. Originality/Value: This study is valuable for analyzing the effectiveness of public innovation policies, with a focus on small businesses. It is clear that other studies are needed to compare results from different territories to better understand the needs of small businesses.

SIRS (Student Involvement Report System) Case Study – Studies on QOS (Quality of Service) Perimeters, Daily Error Types and Activities Within the Server

Relational Database Management System (RDBMS) is commonly applied in Small, Medium, and Enterprise businesses. In this research, we investigated Quality of Service (QoS) perimeters over day-to-day operations in RDBMS and how the running process able to be expedited with the adaptation and combination of autonomic computing approach using MAPE-K (Monitor, Analyse, Plan, Execute, and Knowledge Base) and Fuzzy rules. The core function of the system is to monitor student attendance, assessment, and lastly escalation of identified student issues to the department of academic. In a nutshell, it is called the Student Involvement Report (Sir). Two segments have been analysed and focused on errors in request time out, bandwidth, hits, and user activities. The outcomes of this are significant on activities that related to the peak of academic activity, due to the submission of student reports and escalation among departments. The MAPE-K and Fuzzy rules can capture the scenarios and identify rules that are significantly important to reduce and enhance the Request Time Out and Hits activities.

BIM framework for efficient material procurement planning

Inefficient procurement processes can lead to increased costs and project delays. Addressing information management inefficiencies is a significant but largely unexplored area within construction procurement strategies, despite potential for automation through Database Management Systems (DBMS) and Industry Foundation Classes (IFC). Subjective approaches constrain procurement planning, hindering optimal solutions. This paper addresses the gap by developing a comprehensive semi-automated procurement planning framework. The framework offers flexibility through a two-phased optimization employing Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), integrated with a Building Information Modeling (BIM)-driven database platform compatible with various modeling software. It enhances decision-making by considering indirect costs and allowing installment payments while generating a 4D schedule for improved supply chain stakeholder visualization and decision-making (e.g., project managers), demonstrating improvements over traditional procurement plans in a real-world case study. The developed framework enables future research on integrating real-time data, predictive analytics, and smart contracts to further enhance procurement management.

Web-based human-robot collaboration digital twin management and control system

Human-robot collaboration presents a promising application in customized production. The utilization of digital twin technology, an advanced form of virtual reality interaction, is pivotal in augmenting the capabilities of human-robot collaboration by enhancing perception and interaction dynamics. Human-robot collaboration digital twins have been proposed to create collaboration strategies, simulate collaborative processes, and guarantee individual safety. Nevertheless, there remain obstacles in the implementation of digital twins within human-robot collaboration. One notable challenge involves developing a user-friendly and easily accessible digital twin management and control system that is well-suited for human-robot collaboration. The human-robot collaboration digital twin is a sophisticated system that encompasses both physical and virtual environments. Effectively managing diverse resources within this system, devising collaboration strategies, and overseeing the collaboration process is essential for the standardized utilization of digital twins in human-robot collaboration. In response to the specific requirements of digital twins in this context, a web-based system has been conceptualized and implemented for managing and controlling human-robot collaboration digital twins. The system is structured based on the browser/server architecture model. Its functional design is implemented through a modular approach. The server-side database management system chosen is MySQL, while the programming and development of the control system are carried out using the Microsoft.Net Framework 4.8 on the Visual Studio 2022 platform. The system’s functional modules are exemplified through a case study on human-robot collaborative assembly. The findings of this study offer insights that can guide the standardized management and application of digital twins within the realm of human-robot collaboration.