Database Management System Research Articles

ALGEBRAIC MODELS FOR DATA AND KNOWLEDGE REPRESENTATION IN MODERN DATABASE MANAGEMENT SYSTEMS

The article discusses algebraic data and knowledge representation models in modern database management systems. It is shown that despite the effectiveness of the relational model in storing large volumes of structured information, its capabilities are limited for expressing machine learning algorithms. In this regard, new approaches are proposed based on advanced algebraic models that allow formalizing the architecture and operations of neural networks in SQL. Methods of hybridization of SQL and GPU computations, application of specialized operators, combining data processing and analysis stages are considered. The results confirm the high efficiency of the developed solutions for intelligent analytics.

TRANSMISSION, STORAGE AND PREPROCESSING OF DATA RECEIVED FROM SENSORS USING LORA TECHNOLOGY

This study focuses on the use of Internet of Things technologies to monitor air quality parameters in the city. The research included the development of a system for collecting data from sensors of temperature, humidity, pressure, gases, time, GPS for various environmental parameters. The article discusses the benefits of using LoRaWAN for data transmission, highlighting its low power consumption and long-range capabilities. The presented architecture allows for system scalability, emphasizing the ability to integrate with additional devices and sensors for more complete and comprehensive environmental monitoring. The collected data is stored in a MySQL database management system and the importance of proper data structuring and normalization is emphasized to ensure accurate analysis. In addition, a data preprocessing process is described to solve the problem of heterogeneity of data obtained from different sensors. The result of the study is the obtained and preprocessed data. The work also highlights the importance of data management to ensure the successful implementation of such systems in the face of heterogeneity of information from different sensors. Thus, the article presents a comprehensive approach to air quality monitoring using IoT and highlights the importance of proper data management for subsequent accurate analysis.

Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment

Building a high-performance and scalable system has always been a challenge in tracking systems. At the root of this problem lies the excessive and real-time data overload. This paper aims to replace traditional approaches with big data approaches. In this study, a new big data ecosystem design for vehicle tracking system architecture is presented. The aim is to process real-time and extremely fast-generated location/tracking data very fast and increase the overall system performance. The process speed performance of the newly developed big data ecosystem is compared with the table query speed performance of a relational database. As a result of the comparison, the query speed of the big data ecosystem was found to be much faster than that of the relational database management system.

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.

Visual hazardous models: A hybrid approach to investigate road hazardous events

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009–2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 820 times more fatal/grievous injuries than simple injuries on expressways per 1000 crosswalk crashes (posted speed limit: 100 km per hour). Working pedestrian age group (25–64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

Database for Monitoring the Technical Condition of Structures as an Integral Part of Industrial Safety

Purpose. Development of a database designed to systematization and store information on the technical condition of industrial structures based on the results of periodic visual and instrumental surveys. Methodology. The information system includes a database, a database management system and an application programme. The most appropriate type of database for monitoring the technical condition of structures is a relational structure. The database model covers owners, buildings, structures, quantitative and qualitative parameters of defects and damage, strength indicators of building structures and tilt of industrial structures. Findings. Structural query language was used in the process of developing a database for monitoring the technical condition of industrial facilities. The created database "Monitoring" includes interconnected tables that provide holistic and efficient storage of information. The tables are linked using foreign keys, which provides easy access and data management. The tables are conditionally divided into those that users fill in and those that are filled in in accordance with legislative and regulatory documents at the stage of database creation. This approach to building a database ensures structured storage of information on the technical condition of buildings, taking into account regulations and legislation. The integration of tables filling in by users and pre-filled tables creates a system that increases the efficiency of monitoring the technical condition of industrial structures. Originality. The scientific novelty of the proposed database lies in an innovative approach to structuring and integrating various types of data related to the technical condition of industrial structures. The system offers an effective combination of data from different formats and sources into a single information space and will further provide a comprehensive analysis of the condition of building structures, which is one of the key factors of industrial safety. Practical value. It is intended to improve the efficiency of industrial structures maintenance and exploitation safety, and provides tools for storing the results of monitoring the condition of building structures.

Architecture and Development Framework for a Web-Based Risk Assessment and Management Platform Developed on WordPress to Address Opioid Overdose.

The number of overdose-related fatalities continues to reach historic levels across Canada, despite ongoing efforts by authorities. To reduce mortality, a clinical trajectory ranging from preventative measures to crisis intervention, skill training to treatment, and risk assessment to risk management needs to be supported. The web-based Risk Assessment and Management Platform (RAMP) was developed to realize this concept and to empower people who use drugs through an integrated tool that allows them to better understand and manage their risk of overdose. This paper outlines the architecture and development of RAMP, which is built on the WordPress platform. WordPress components are mapped onto a 3-tier architecture that consists of presentation, application, and database layers. The architecture facilitates the development of a modular software that includes several features that are independent in functionality but interact with each other in an integrated platform. The relatively low coupling and high coherence of the features may reduce the cost of maintenance and increase flexibility of future developments. RAMP's architecture comprises a user interface, conceptual framework, and backend layers. The RAMP front end effectively uses some of the WordPress' features such as HTML5, CSS, and JavaScript to create a mobile, friendly, and scalable user interface. The RAMP backend uses several standard and custom WordPress plug-ins to support risk assessment and monitoring, with the goal of mitigating the impacts and eliminating risks together. A rule-based decision support system has been hard-coded to suggest relevant modules and goals to complement each user's lifestyle and goals based on their risk assessment. Finally, the backend uses the MySQL database management system and communicates with the RAMP framework layer via the data access layer to facilitate a timely and secure handling of information. Overall, RAMP is a modular system developed to identify and manage the risk of opioid overdose in the population of people who use drugs. Its modular design uses the WordPress architecture to efficiently communicate between layers and provide a base for external plug-ins. There is potential for the current system to adopt and address other related fields such as suicide, anxiety, and trauma. Broader implementation will support this concept and lead to the next level of functionality.

Semiconductor Industry Innovations: Database Management in the Era of Wafer Manufacturing

The semiconductor industry has made significant advancements in wafer assembly, necessitating the evolution of Database Management Systems (DBMS) to handle increasingly complex and voluminous data. This paper examines the specializations of DBMS tailored for the semiconductor industry, showcasing innovations that integrate advanced data analytics with real-time processing on scalable storage solutions. These innovations enhance Effectiveness, Precision, and Efficiency (EAP) by 40%, reducing defect rates by one-fifth from 2021 to 2025. The study explores modern DBMS designs and their role in predictive maintenance, quality control, and process optimization. Leveraging big data and machine learning, these systems can swiftly analyze large datasets to identify patterns and outliers for improved decision-making. Additionally, modern DBMS offers robust data security features, such as anomaly detection and encryption, which have minimized breaches and increased compliance. However, challenges remain, including integrating new technologies with legacy systems and addressing the shortage of skilled professionals. Our analysis underscores the ongoing need for innovation in DBMS to scale with emerging memory technologies. This report serves as a comprehensive guide for those interested in entering the market, detailing the current requirements and future opportunities in database management for wafer manufacturing.

RESEARCH ON CONSTRUCTION OF NATURAL RESOURCES THREE-DIMENSIONAL SPATIO-TEMPORAL DATABASE SYSTEM

Abstract. The construction of an efficient and practical database management system based on large-scale three-dimensional spatiotemporal databases presents a novel challenge in the field of natural resource information technology. This research, with the objective of supporting the contemporary era of unified natural resource management and building upon the foundation of a national-level three-dimensional spatiotemporal natural resource database, proposes a comprehensive framework for a distributed architecture database management system. Subsequently, the research outlines the system's components, encompassing a service publishing system, online application system, professional management system, and operational supervision system, and designs core functionalities for the integrated management of natural resource elements within a three-dimensional spatiotemporal context. Building upon this framework, the research utilizes cloud-based database software and hardware environments, along with distributed technology pathways, to establish a national-level three-dimensional spatiotemporal database management system for natural resources. Furthermore, it conducts typical applications in the field of natural resources. The results demonstrate that the database management system effectively achieves the integration, representation, and analytical application of large-scale natural resource survey and monitoring data within a three-dimensional spatiotemporal context. It plays a crucial supportive role in the development of a national-level three-dimensional spatiotemporal natural resource database and holds valuable insights for the construction of provincial-level database management systems and other related systems in various domains.

Integrating Novel Machine Learning for Big Data Analytics and IoT Technology in Intelligent Database Management Systems

Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.

Improving web-oriented information systems efficiency using Redis caching mechanisms

The responsiveness of a web application with minimum latency time and maximum web pages loading speed is determined by its overall performance. When dealing with a large number of users and amount of data, the performance of web applications is significantly affected by ways of data processing, storage and access. This paper identifies the significance of data caching process to speed up access to relational database. The study examines approaches to improve the performance of web applications through the joint use of MySQL relational database management system (DBMS) and Redis NoSQL DBMS. The practical part of the study presents a description of a web application built based on Java and Spring Boot framework. The paper proposes the implementation of the caching strategies that take into account the principles of aspect-oriented programming. Made experiments on performance testing of the developed web application with and without caching are presented. The presented results of the study allowed us to conclude that it is possible to improve the performance of web applications by the optimal use of caching strategies when performing database queries.

Quantum Join Ordering by Splitting the Search Space of QUBO Problems

The join order has a huge impact on the execution time of a query, such that finding an optimal join order plays a crucial role in query optimization. However, join order optimization is known to be NP-hard. Hence, in this paper, we propose an approach for accelerating join order optimization by quantum computers. We extend our previous approach supporting bushy join trees by splitting the search space of possible join orders and solving each of these subspaces on currently available quantum computers to optimize the join of more relations than our previous approach. We have integrated our approach to quantum query optimization in the relational database management system PostgreSQL to conduct studies with real-world queries. In our experiments, we show that we can perform join order optimization up to 7 relations for real-world queries using quantum annealing and up to 8 relations for artificial queries using simulated annealing with a reasonable number of QUBO problems solved by D‑Wave’s Quantum Annealer. Furthermore, we show that our approach can be also used to perform join-order for queries joining five relations on circuit-based quantum computers running the quantum approximate optimization algorithm (QAOA) and variational quantum eigensolver (VQE) approaches.

Identifying the Root Causes of DBMS Suboptimality

The query optimization phase within a database management system (DBMS) ostensibly finds the fastest query execution plan from a potentially large set of enumerated plans, all of which correctly compute the same result of the specified query. Sometimes the cost-based optimizer selects a slower plan, for a variety of reasons. Previous work has focused on increasing the performance of specific components, often a single operator, within an individual DBMS. However, that does not address the fundamental question: from where does this suboptimality arise, across DBMSes generally? In particular, the contribution of each of many possible factors to DBMS suboptimality is currently unknown. To identify the root causes of DBMS suboptimality, we first introduce the notion of empirical suboptimality of a query plan chosen by the DBMS, indicated by the existence of a query plan that performs more efficiently than the chosen plan, for the same query. A crucial aspect is that this can be measured externally to the DBMS, and thus does not require access to its source code. We then propose a novel predictive model to explain the relationship between various factors in query optimization and empirical suboptimality. Our model associates suboptimality with the factors of complexity of the schema, of the underlying data on which the query is evaluated, of the query itself, and of the DBMS optimizer. The model also characterizes concomitant interactions among these factors. This model induces a number of specific hypotheses that were tested on multiple DBMSes. We performed a series of experiments that examined the plans for thousands of queries run on four popular DBMSes. We tested the model on over a million of these query executions, using correlational analysis, regression analysis, and causal analysis, specifically Structural Equation Modeling (SEM). We observed that the dependent construct of empirical suboptimality prevalence correlates positively with nine specific constructs characterizing four identified factors that explain in concert much of the variance of suboptimality of two extensive benchmarks, across these disparate DBMSes. This predictive model shows that it is the common aspects of these DBMSes that predict suboptimality, not the particulars embedded in the inordinate complexity of each of these DBMSes. This paper thus provides a new methodology to study mature query optimizers, identifies underlying DBMS-independent causes for the observed suboptimality, and quantifies the relative contribution of each of these causes to the observed suboptimality. This work thus provides a roadmap for fundamental improvements of cost-based query optimizers.

Bedrock Geologic Database of the Indiana Portions of the Princeton, Evansville, Mount Vernon, and West Frankfort 30- x 60-minute Quadrangles

Located in the eastern Illinois Basin, the Princeton 1:100,000-scale quadrangle comprises portions of Knox, Gibson, Pike, Dubois, Posey, Vanderburgh, Warrick, and Spencer Counties in Indiana as well as small portions of Edwards, Gallatin, Wabash, and White Counties in Illinois. The mapped area is situated in the rolling topography of the Wabash Lowland and dominated by Pennsylvanian-age coal-bearing strata. Pennsylvanian siliciclastics in the Princeton quadrangle host economically important coal seams. The most widespread coal beds occur in the Carbondale Group, but the Raccoon Creek Group hosts coal of superior quality (low sulfur, low ash) in Indiana. Surface and underground mining of these coals have been extensive, with expansive impacts on the landscape and subsequent reclamation. Bedrock outcrops are obscured mainly by glacial sediments and extensive fluvial deposits from the Wabash River that formed deep channels (paleo and active) filled with outwash and alluvium. The geologic map of Gray and others (1970), the Compendium of Paleozoic Rock-Unit Stratigraphy in Indiana (1986), and Indiana Geologic Names Information System (IGNIS, https://legacy.igws.indiana.edu/ignis/) are the fundamental compilation of previous geological knowledge from this area. Additional data come from previous 1:24,000-scale mapping through the National Cooperative Mapping Program STATEMAP component. However, most information for the Bedrock Geologic Database of the Indiana Portions of the Princeton, Evansville, Mount Vernon, and West Frankfort 30- x 60-minute Quadrangles and, more broadly, the western corridor, is derived from Indiana-scale coal seam mapping conducted between 2011 and 2017 based on the National Coal Resources Data System (NCRDS), Indiana Coal Quality Database (ICQD), and geophysical logs from oil and gas wells archived in the Petroleum Database Management System (PDMS). The majority of these data were previously presented in Esri-based Indiana Coal Atlas StoryMaps for the entire western corridor. This geologic mapping project was funded in part through the STATEMAP Program supported by the U.S. Geological Survey under Cooperative Agreement No. G22AC00424. The Bedrock Geologic Database of the Indiana Portions of the Princeton, Evansville, Mount Vernon, and West Frankfort 30- x 60-minute Quadrangles is a composite geodata set that conforms to "GeMS (Geologic Map Schema)--a standard format for the digital publication of geologic maps," available at http://ngmdb.usgs.gov/Info/standards/GeMS/. Metadata records associated with each element within the geodata set contain more detailed descriptions of their purposes, constituent entities, and attributes. An OPEN shapefile version of the data set is also available. It consists of shapefiles, DBF files, and delimited text files and retains all information in the native geodatabase, but some programming will likely be necessary to assemble these components into usable formats.

Refactoring a Complex Scientometric Software System to Support PostgreSQL Database Management System

The problem of software import substitution is under a great deal of attention in Russia since 2015. As a part of import substitution for a complex software system, database migration presents a considerable risk. Despite the SQL ISO standardization efforts, different implementations of SQL relational database management system are partially incompatible on the level of source codes for the schema, queries and stored procedures. Incompatibilities can manifest even at the level of query semantics. Some traits of complex scientometric software systems further complicate the migration process. Such traits include the complex data model and, consequently, the presence of non-trivial graph-traversing SQL queries which should complete fast enough to be used interactively for thousands of users simultaneously. In this paper an experience of refactoring a complex scientometric software system to support PostgreSQL database management system is presented. Our approach includes both usage and modification of the existing tools and development of the in-house software to support the refactoring process compounded with an extensive automated testing procedures.

Information modeling in Russia

According to a study by SearchInform for 2022, only 3% of systemically important enterprises and 1.7% of critical information infrastructure organizations replaced foreign operating systems (OS) and database management systems (DBMS) with domestic ones. This article examines the factors that may be the reason for such a low dynamics of import substitution at Russian enterprises, as well as the conditions under which the situation may change for the better.

Vector database management systems: Fundamental concepts, use-cases, and current challenges

Vector database management systems have emerged as an important component in modern data management, driven by the growing importance for the need to computationally describe rich data such as texts, images and video in various domains such as recommender systems, similarity search, and chatbots. These data descriptions are captured as numerical vectors that are computationally inexpensive to store and compare. However, the unique characteristics of vectorized data, including high dimensionality and sparsity, demand specialized solutions for efficient storage, retrieval, and processing. This narrative literature review provides an accessible introduction to the fundamental concepts, use-cases, and current challenges associated with vector database management systems, offering an overview for researchers and practitioners seeking to facilitate effective vector data management.

The global volcano monitoring infrastructure database (GVMID)

Monitoring volcanoes is of the most importance in volcano risk mitigation to safeguard lives and economies. Thanks to recent technological advances, both on-ground and in space, our understanding of volcanic processes has improved significantly. Though there is no one-system-fits-all, optimizing infrastructure for efficient monitoring stands as key objective. The impacts of volcanic hazards can span from local to global scales, affecting us both in the short and long term. This highlights the worldwide significance of improving volcano monitoring. Previously reliant on local ground-based instruments, today’s monitoring approach is enhanced by remote and space-based techniques such as satellite remote sensing, scanning-Differential Optical Absorption Spectroscopy (DOAS), and infrasound. Designing an effective monitoring infrastructure for volcano observatories involves careful consideration of various factors such as network coverage, type of sensors, data transmission, and power supply to ensure that the targeted parameters meet the specific needs of each volcano (e.g., type of activities, early warning systems). Additionally, fostering collaboration and information sharing within the global scientific community is essential for addressing the current challenges in volcanology. In line with this, we’ve established the Global Volcano Monitoring Infrastructure Database (GVMID) to compile data from volcano monitoring across the globe. Global Volcano Monitoring Infrastructure Database serves as an integral component of WOVOdat, the global volcano unrest database, aiming to enhance our understanding of eruptive processes and improve eruption forecasts. The database incorporates monitoring metadata comprising networks, stations, and instruments, all standardized and managed using a MySQL relational database management system. Accessed through a web-based interface (https://wovodat.org/gvmid/home.php), GVMID offers an informative snapshot and foundational overview of the techniques and instruments in place at diverse volcanoes. This interactive platform allows for queries, visualizations, and downloads, serving as a valuable resource for the volcano community. GVMID can assist observatories in various ways, by: (a) Facilitating the setup or enhancement of monitoring systems for specific volcanoes. (b) Providing insights into the latest monitoring technologies and instrumentation. (c) Identifying existing monitoring gaps that could be addressed through remote sensing infrastructure and future instrument deployments. We extend an invitation to the global volcano community to actively participate in the development and enrichment of GVMID. Our aim is for it to become a continually updated and indispensable resource that caters to diverse needs within the volcanology community.

Library management database design and application

Under the data-centric business environment, efficient database design is pivotal for efficient data management across various sectors. For libraries and bookstores, a well-tailored database is essential to categorize, index, and swiftly retrieve vast amounts of data, ensuring efficient operations. MySQL is an open-source database management system which facilitates efficient data manipulation. Based on MySQL, this essay delves into the realm of library database design and application, addressing the need for streamlined systems. This research addresses the need for adaptation in the evolving technology landscape and offers practical insights for librarians and researchers aiming to optimize information management in modern libraries. Employing the basic steps for database design, we explore one methodology for designing a library management database based on the design of database requirement, modeling, backend implementation, efficiency optimization and testing using MySQL. Through this approach, the study provides a potential solution that meets the basic requirements of libraries and enhances information management practices.

The design of library database management system based on MySQL

Database-manage-system has become a modern management among different enterprises. The enterprises data need managed by a well-performed system. Also, the database is an essential need for a modern library. In this case, more and more people start using different way to build the system which is suitable for the enterprises. The library system has become a famous one which need a large database system to encounter the huge amount of data. In this article, it shows the usage of database-manage-system in a local library and how does the system work. Also, the introduction of the function of MySQL usage in this application. Finally, after the test of the data and running the programme in MySQL, the database-manage-system realize the function of list the book, check of availability, make reservations and store different users status. In this essay, through the test of data and the code, a database-manage-system is designed. This database-manage-system could complete basic function which contain check status, make appointments and locate books.