Data Access Time Research Articles

DEVELOPMENT AND DEPLOYMENT OF A SECURITY INFORMATION MANAGEMENT SYSTEM FOR ENHANCED ORGANIZATIONAL SAFETY AND EFFICIENCY

The study focuses on the development and deployment of a Security Information Management System (SIMS) to enhance organizational safety and efficiency. It addresses the critical need for robust data security frameworks in modern organizations, emphasizing the protection of information integrity, availability, and confidentiality. Rooted in historical and evolving security paradigms, the research highlights the transition from traditional military-focused strategies to comprehensive approaches encompassing economic, environmental, and technological dimensions. Using the Object-Oriented System Development Methodology (OOSDM), the study designed and implemented a real-time SIMS integrating authentication and authorization processes. The proposed system features modular architecture comprising Web, Application, and Data tiers to ensure scalability, security, and efficiency. Advanced tools, such as encryption, intrusion detection systems, and login tracking, safeguard against cyber threats, including hacking and denial-of-service attacks. Rigorous testing of the system demonstrated its effectiveness in mitigating security risks, supporting decision-making with timely data access, and enhancing operational efficiency. The findings underscore the potential of SIMS to secure sensitive organizational data and recommend its adoption, training for security personnel, and regular updates for sustained functionality. This research contributes to advancing information security practices, particularly in dynamic operational environments.

HealthChain: A blockchain‐based framework for secure and interoperable electronic health records (EHRs)

AbstractCurrently, there is no unified Electronic Health Record (EHR) system connecting major healthcare organizations such as hospitals, medical centers, and specialists. Blockchain technology, with its unique features, provides an ideal platform for developing a large‐scale electronic health record system. In this article, the authors introduce HealthChain, a novel blockchain‐based secure EHR system that integrates advanced encryption techniques, a robust consent management system, cross‐platform interoperability, and enhanced scalability. Unlike existing EHR systems, HealthChain allows patients to have comprehensive control over their health data, ensuring that access is strictly regulated according to their preferences. The experimental results demonstrate several significant improvements over traditional EHR systems. HealthChain reduces data access times by 30%, and its interoperability rate with various healthcare systems is 40% higher than that of other blockchain‐based EHR solutions. Security is greatly enhanced, with HealthChain experiencing 50% fewer data breaches due to its advanced encryption and smart contract‐based access controls. Moreover, patient satisfaction has increased by 35% as a result of better control and access to their health records. These findings highlight HealthChain as not only a feasible and effective solution for managing health records but also a significant advancement over existing systems.

Data caching technologies in modern microprocessors

Objective. The study presented in the paper is aimed at studying the methods for improving the efficiency of software in modern computing systems with a hierarchical memory structure. Method. The study is based on data caching technologies in microprocessors. Result. The article presents the results of the analysis of various approaches to the development of efficient software taking into account the characteristics of the memory subsystem of the computing system, which made it possible to prove the importance of cache memory in improving the performance and interaction of computer components. Conclusion. Cache memory is a critical element in the architecture of microprocessors, playing a key role in determining the performance of the computing system. Optimizing the use of cache can significantly improve data access time and, as a result, overall system performance. Software developers need to pay special attention to the characteristics of the memory subsystem when designing and implementing solutions.

Improving the Efficiency of X-ray Data Management through Database-Based Web Application at RSUD I Lagaligo

The development of information technology in the health sector encourages BLUD RSUD I Lagaligo East Luwu Regency to improve the efficiency of X-ray data management. This research aims to develop a web-based x-ray result database application to speed up access and increase user satisfaction. The methods used include requirements analysis, system design, application development with Laravel and MySQL, as well as functionality testing and User Acceptance Testing (UAT). The results showed that data access time was reduced from 7 minutes to 1 minute and user satisfaction increased from 3.4 to 4.8. This application is proven effective in improving the efficiency and quality of radiology services. Suggestions for further research are integration with broader hospital information systems and development of advanced analytic features.

Analysis of the parasitic capacitance effects on the layout of latch-based sense amplifiers for improving SRAM performance

Static random-access memory (SRAM) technology is utilized in designing cache memory to enhance the processing performance of computer systems. The sense amplifier (SA) circuit, a crucial component of memory design, significantly impacts data access time and power consumption. In comparison to conventional differential sense amplifiers (DSA) designs, latch-based sense amplifiers (LSA) used in memory-based computing platforms have specific requirements, including robust noise resistance in harsh working environments and low power consumption, particularly for internet of thing (IoT) embedded computing applications. However, the performance can be degraded due to various factors that arise during the layout, such as conductor resistance or the development of parasitic capacitance. Therefore, this study employs low-voltage 22 nm UMC CMOS technology for LSA design layout and analyzes the factors influencing design performance post-layout process. Layout design optimization techniques are applied to mitigate the impact of parasitic capacitance on important signal lines such as data line/data line bar (DLL/DLLB). Based on the performance analysis results, it is possible to achieve a reduction in power consumption of up to 15% and a 5% decrease in read delay time by implementing circuit layout LSA design optimization techniques.

Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing

ABSTRACT Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537.

Blockchain-based data sharing algorithm in distributed network data storage

This paper proposes a sharing algorithm based on blockchain principles to address the issues of data sharing, low efficiency, and performance in traditional systems. The algorithm is integrated with the domain name system to develop a data storage system based on blockchain. The performance of the sharing algorithm is evaluated, and the data storage system is tested. This demonstrates that the sharing algorithm’s average latency is 436 ms and average throughput is 5439 tps. Furthermore, it outperforms the other comparison algorithms. Additionally, the study conducts performance experiments to compare the data storage system. The data storage system proposed in this study demonstrates a higher average throughput of 6.42*108 tps and a faster data access time of 0.15 s than the other comparison systems. The comprehensive results show that the proposed sharing algorithm and data storage system outperform the comparison algorithm and system in terms of latency, throughput, and data access performance. The constructed model exhibits good centralized and distributed storage crawling performance, which can achieve more secure, efficient, and trustworthy data sharing in distributed network data storage.

A hybrid chaos-based algorithm for data object replication in distributed systems

One of the primary challenges in distributed systems, such as cloud computing, lies in ensuring that data objects are accessible within a reasonable timeframe. To address this challenge, the data objects are replicated across multiple servers. Estimating the minimum quantity of data replicas and their optimal placement is considered an NP-complete optimization problem. The primary objectives of the current research include minimizing data processing costs, reducing the quantity of replicas, and maximizing the applied algorithms’ reliability in replica placement. This paper introduces a hybrid chaos-based swarm approach using the modified shuffle-frog leaping algorithm with a new local search strategy for replicating data in distributed systems. Taking into account the algorithm’s performance in static settings, the introduced method reduces the expenses associated with replica placement. The results of the experiment conducted on a standard data set indicate that the proposed approach can decrease data access time by about 33% when using approximately seven replicas. When executed several times, the suggested method yields a standard deviation of approximately 0.012 for the results, which is lower than the result existing algorithms produce. Additionally, the new approach’s success rate is higher in comparison with existing algorithms used in addressing the problem of replica placement.

A Data Management System for Smart Cities Leveraging Artificial Intelligence Modeling Techniques to Enhance Privacy and Security

Smart cities are metropolitan areas that use sophisticated technology to increase efficiency, sustainability, and overall quality of life. The potential for transformation is tremendous, with applications ranging from Internet of Things (IoT)-driven infrastructure to data-driven governance. Effectively handling the abundant data produced in smart cities requires stringent security and privacy protocols. This research aims to tackle these difficulties by introducing the suggested Artificial Intelligence-based Data Management System (AI-DMS) for Smart Cities. AI-DMS seeks to optimize the data processing pipeline, guaranteeing effectiveness throughout the process, from data extraction to publication. Implementing a Multi-Level Sensitive Model is a notable addition, as it classifies data into three categories: sensitive, quasi-sensitive, and public. This allows for more nuanced sharing of data. Privacy preservation is accomplished using Principal Component Analysis (PCA), a comprehensive technique encompassing feature mapping, selection, normalization, and transformation. The simulation results demonstrate that AI-DMS outperforms other methods. It achieves a Data Quality Score of 95.12% (training) and 93.76% (testing), a Privacy Preservation Rate of 85.23% (training) and 82.76% (testing), a Processing Efficiency of 90.54% (training) and 88.76% (testing), a Sensitivity Model Accuracy of 80.12% (training) and 78.45% (testing), and a Data Access Time of 22.76 ms (training) and 21.32 ms (testing). The results highlight AI-DMS as a reliable and effective system, guaranteeing superior smart city data management that is secure and precise. This contribution aligns with the changing urban scene, offering improvements in decision-making based on data while still ensuring privacy and security.

A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations.

REVIEW OF TECHNOLOGIES FOR STORING AND ANALYZING DATA FROM SATELLITE IMAGES AND AERIAL PHOTOGRAPHY

An overview of current technologies for storing and analyzing data from satellite imagery and aerial photography is provided. Currently, the processing and aggregation industry is actively developing: new analytical methods are being added, and fundamentally new approaches are being created. To evaluate existing technologies, the article briefly describes the main products on the market, such as EOSDA, Maxar, Google Earth Engine, ArcGIS, and others. To evaluate the strengths and weaknesses, a comparative table was compiled taking into account the factors of autonomy and openness to modifications. The autonomy of geospatial data aggregation and processing is important not only in terms of dependence on third-party computing resources, but is also a key factor for implementation in critical industries. In addition, independence and the ability to be deployed on client hardware require analytical algorithms to use computing resources more economically, which in turn correlates with the quality of data aggregation approaches. Therefore, a key direction on the way to system autonomy is the ability to get rid of unnecessary information before it is processed by more demanding algorithms for searching and identifying objects or spatial patterns. That is why the authors proposed an approach to optimize processing and storage, which will simplify access to analytics results and overall performance. This approach can significantly reduce data storage and access time, while ensuring high accuracy and relevance of information. A method for aggregating aerial photography data into semi-automated panoramic images that remove overlapping neighboring frames using homographic transformations based on special points is proposed. A method for comparing (and searching for coincidences) aerial imagery and similar satellite imagery that approximates data from different sources is also proposed. Given the result, the introduction of machine learning and artificial intelligence methods in geospatial data analysis can be the key to the development of highly automated systems that can adapt and respond to changes in large data streams.

An effective data placement strategy for IIoT applications

SummaryWith the rapid development of the Internet of things (IoT) and mobile communication technology, the amount of data related to industrial Internet of things (IIoT) applications has shown a trend of explosive growth, and hence edge‐cloud collaborative environment becomes one of the most popular paradigms to place the IIoT applications data. However, edge servers are often heterogeneous and capacity limited while having lower access delay, so there is a contradiction between capacity and latency while using edge storage. Additionally, when IIoT applications deployed crossing edge regions, the impact of data replication and data privacy should not be ignored. These factors often pose challenges to proposing an effective data placement strategy to take full advantage of edge storage. To address these challenges, an effective data placement strategy for IIoT applications is designed in this article. We first analyze the data access time and data placement cost in an edge‐cloud collaborative environment, with the consideration of data replication and data privacy. Then, we design a data placement strategy based on ‐constraint and Lagrangian relaxation, to reduce the data access time and meanwhile limit the data placement cost to an ideal level. As a result, our proposed data placement strategy can effectively reduce data access time and control data placement costs. Simulation and comparative analysis results have demonstrated the validity of our proposed strategy.

On hierarchical clustering-based approach for RDDBS design

Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to develop effective solutions for DDBS design problems. But the great majority of them barely consider the RDDBS's initial design. Therefore, this work aims at proposing a clustering-based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design. To ensure that each operation flows into the next without any increase in complexity, fragmentation and allocation are done simultaneously. With this approach, the main goals are to minimize communication expenses, response time, and irrelevant data access. Most importantly, it has been observed that the proposed approach may effectively expand RDDBS performance by simultaneously fragmenting and assigning various relations. Through simulations and experiments on synthetic and real databases, we demonstrate the viability of our strategy and how it considerably lowers communication costs for typical access patterns at both the early and late stages of design.

DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments.

Research on the Application and Performance Optimization of GPU Parallel Computing in Concrete Temperature Control Simulation

With the development of engineering technology, engineering has higher requirements for the accuracy and the scale of simulation calculation. The computational efficiency of traditional serial programs cannot meet the requirements of engineering. Therefore, reducing the calculation time of the temperature control simulation program has important engineering significance for real-time simulation of temperature field and stress field, and then adopting more reasonable temperature control and crack prevention measures. GPU parallel computing is introduced into the temperature control simulation program of massive concrete to solve this problem and the optimization is carried out. Considering factors such as GPU clock rate, number of cores, parallel overhead and Parallel Region, the improved GPU parallel algorithm analysis indicator formula is proposed. It makes up for the shortcomings of traditional formulas that focus only on time. According to this formula, when there are enough threads, the parallel effect is limited by the size of the parallel domain, and when the parallel domain is large enough, the efficiency is limited by the parallel overhead and the clock rate. This paper studies the optimal Kernel execution configuration. Shared memory is utilized to improve memory access efficiency by 155%. After solving the problem of bank conflicts, an accelerate rate of 437.5× was realized in the subroutine of the matrix transpose of the solver. The asynchronous parallel of data access and logical operation is realized on GPU by using CUDA Stream, which can overlap part of the data access time. On the basis of GPU parallelism, asynchronous parallelism can double the computing efficiency. Compared with the serial program, the accelerate rate of inner product matrix multiplication of the GPU asynchronous parallel program is 61.42×. This study further proposed a theoretical formula of data access overlap rate to guide the selection of the number of CUDA streams to achieve the optimal computing conditions. The GPU parallel program compiled and optimized by the CUDA Fortran platform can effectively improve the computational efficiency of the simulation program for concrete temperature control, and better serve engineering computing.

Compute-in-Memory SRAM Cell Using Multistate Spatial Wavefunction Switched (SWS)-Quantum Dot Channel (QDC) FET

This paper presents multistate spatial wavefunction switched (SWS)-quantum dot channel (QDC) field-effect transistor (FET) static random access memory (SRAM)-based Compute-in-Memory (CIM) cell. The SWS-QDC FETs have two or more vertically stacked coupled quantum dot channels, and the spatial location of carriers within these channels is governed by the applied gate voltage. The location of the carriers can be utilized to encode multiple logic levels within a single device. The utilization of SWS-QDC FETs in CIM cell increases the data storage and energy-efficient computation in the memory. CIM reduces the data access time and improves performance for energy-efficient artificial intelligence (AI) edge devices.

Shaping the Future of Animation towards Role of 3D Simulation Technology in Animation Film and Television

The application of 3D simulation technology has revolutionized the field of animation film and television art, providing new possibilities and creative opportunities for visual storytelling. This research aims to explore the various aspects of applying 3D simulation technology in animation film and television art. It examines how 3D simulation technology enhances the creation of realistic characters, environments, and special effects, contributing to immersive and captivating storytelling experiences. The research also investigates the technical aspects of integrating 3D cloud simulation technology into the animation production pipeline, including modeling, texturing, rigging, and animation techniques. This paper explores the application of these optimization algorithms in the context of cloud-based 3D environments, focusing on enhancing the efficiency and performance of 3D simulations. Black Widow and Spider Monkey Optimization can be used to optimize the placement and distribution of 3D assets in cloud storage systems, improving data access and retrieval times. The algorithms can also optimize the scheduling of rendering tasks in cloud-based rendering pipelines, leading to more efficient and cost-effective rendering processes. The integration of 3D cloud environments and optimization algorithms enables real-time optimization and adaptation of 3D simulations. This allows for dynamic adjustments of simulation parameters based on changing conditions, resulting in improved accuracy and responsiveness. Moreover, it explores the impact of 3D cloud simulation technology on the artistic process, examining how it influences the artistic vision, aesthetics, and narrative possibilities in animation film and television. The research findings highlight the advantages and challenges of using 3D simulation technology in animation, shedding light on its potential future developments and its role in shaping the future of animation film and television art.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

PERBANDINGAN EFISIENSI WAKTU PROSES PENGAKSESAN DATA ANTARA QUERY BERBENTUK JOIN DENGAN SUBSELECT

Queries in a database application system, have a great influence on the success of application development. Frequently used query forms include subselect and join. Choosing the right query form will affect the performance of the database application. Database applications are sometimes slow to respond because of slowness in their databases. The selection of the form of the query to be used is the first step of forming a query, because the resulting query will affect access from the database. The purpose of this study is to determine the form of SQL with a faster data access time, whether join or subselect. The subject of this study is queries made in the form of joins and subselects. The methods that will be used in this study are literature and experimental methods. The result of this study is a comparison table of access time between join and subselect queries. From the results of the query comparison, it can be concluded that the join form query is a faster SQL form compared to subselect. A faster query is determined from testing done by creating a simple application using both forms of the query.

Building Resident Quality Improvement Knowledge and Engagement Through a Longitudinal, Mentored, and Experiential Learning-Based Quality Improvement Curriculum.

Quality improvement (QI) training is an essential component of resident medical education and a part of the ACGME core competencies. We present our residency's evidence-based QI curriculum, which outlines key components identified in the literature for successful QI education. Our curriculum included a mandatory five-part longitudinal educational series during ambulatory education sessions for second-year residents. Modeled after the Institute for Healthcare Improvement model for improvement and taught by a chief resident, our curriculum introduced residents to key QI concepts through case-based, just-in-time didactics and applied experiential learning via concurrent resident-led longitudinal QI projects. Residents received structured, multilayer mentorship from a faculty mentor in their field of interest and the chief resident of quality and patient safety. Their work-in-progress projects were presented to faculty QI experts and institutional leadership for additional feedback and mentorship. Since 2016, a total of 234 internal medicine residents have completed our QI curriculum and developed 67 QI projects, which have been presented at various local, regional, and national conferences. In the 2 most recent academic years, Quality Improvement Knowledge Application Tool Revised (QIKAT-R) scores significantly increased from 4.6 precurriculum to 6.3 postcurriculum (p < .001). A longitudinal, experiential, and mentored QI curriculum teaches residents QI skill sets through incorporating mechanisms associated with successful educational initiatives and adult learning theory. Our QIKAT-R results and project output show that our curriculum is associated with improved trainee QI knowledge and systems-level improvements.