Optimize Data Storage Research Articles

Optimization Analysis of Tennis Player Training Mode Based on 3D Simulation Model

This paper is based on 3D simulation technology and deeply analyzes the optimization strategy of tennis player training mode. The research focuses on two core systems: one is a remote motion monitoring system based on architecture, which captures and analyzes athletes’ motion data in real time, providing coaches with accurate motion feedback and training effectiveness evaluation; The second is a 3D-based safety monitoring system, which uses 3D simulation technology to simulate the training environment, monitor and warn potential safety risks in real time, and ensure the safety of athletes during training. These two systems are designed for different demand scenarios in tennis training, with complementary functions, and together constitute an intelligent solution for optimizing the training mode of tennis players. By continuously improving algorithms, optimizing data storage and computation methods, and improving the accuracy of data analysis and mapping, this study aims to enhance the scientific, efficient, and safe training of tennis players, providing strong support for the comprehensive improvement of their skills and physical fitness.

Advancements in Network Attached Storage (NAS) for Mobile Devices: Technological Developments and Performance Assessment

Network-attached storage (NAS) technology has emerged as a vital solution in the evolving landscape of data management and storage. With the exponential growth of data and the increasing demand for efficient, scalable storage solutions, NAS systems offer a centralized, cost-effective, and flexible alternative to traditional storage methods. This paper explores the application and advantages of NAS technology in various scenarios, from home and small business environments to large enterprises. It highlights the key strengths of NAS systems in optimizing data storage, ensuring data security, and facilitating cross-device sharing. The study delves into the architectural components of NAS, including file systems, RAID configurations, and network protocols, to demonstrate how these technologies enhance storage performance and reliability. Technical details on NAS system design, such as the integration of advanced file systems and user-level networking technologies, are discussed to showcase their impact on data transfer efficiency and overall system performance. Experimental results validate the effectiveness of NAS in different environments, illustrating its ability to provide high availability, data redundancy, and low-latency data access. The findings underscore NAS technology’s potential as a versatile and scalable storage solution, with recommendations for future research focused on developing new file systems, optimizing network protocols, and exploring edge computing integrations to further enhance NAS capabilities.

Research on the Architectural Design and Performance Optimization of Large Scale Real-Time Chat Systems

With the rapid development of internet technology, large-scale real-time chat systems have become an essential tool for connecting users worldwide. These systems not only support the core functions of social communication but also serve as key platforms for business, education, and remote collaboration. However, the surge in user numbers and the explosive growth in message volume pose unprecedented technical challenges to real-time chat systems, including high concurrency processing, low latency communication, data consistency assurance, and system scalability. This study aims to explore and implement an efficient, stable, and scalable architecture for large-scale real-time chat systems and propose corresponding performance optimization strategies. The research employs literature review, system analysis, prototype design, and performance evaluation methods. It first analyzes the design limitations and performance bottlenecks of existing real-time chat systems. Based on this analysis, a microservices-based system design scheme is proposed, which enhances system performance and reliability through componentized services, message queues, load balancing, and caching mechanisms. In terms of performance optimization, this paper focuses on key technologies such as load balancing algorithms, asynchronous processing mechanisms of message queues, caching strategies, and data storage optimization. Experimental evaluations have confirmed the significant effects of the proposed scheme in handling high-concurrency requests, reducing system latency, and improving data throughput.

Social Media and Cloud Computing Impact on Human Resource Management in Multinational Enterprises

To address the issues of high storage costs for human resource data, resource management of human resource systems, and employee social interaction in human resource management, this study proposes an adaptive intelligent HR data placement strategy based on genetic algorithms. Additionally, a fitness model and an employee social interaction model are presented. The fitness model utilizes genetic algorithms to optimize data storage strategy and server load balancing, resulting in reduced overall costs and efficient resource utilization. This is achieved by comprehensively considering data transmission latency and storage costs. The employee social interaction model analyzes social media blog post topics and employee ranks, and employs a co-training method to predict employee interactions. The experimental results indicated that the placement strategy can improve the HR data transfer by nearly 50% and effectively reduce the standard deviation of server load by about 10-15%. The accuracy, recall, F1 value, and precision of the employee social interaction model were 77.89%, 63.97%, 70.32%, and 71.78%, respectively. The proposed strategy and model demonstrated higher accuracy and robustness in predicting and analyzing employee social interactions, thus providing more reliable decision support for human resource management.

Enhanced Machine Learning for Cloud Computing Electricity Cost Prediction

Cloud computing is becoming popular in the tech industry as it offers convenient computing without the need to buy physical hardware. Instead, companies provide these services using their own computers and servers powered by electricity. However, running these data centers consumes a lot of energy, and with rising electricity prices, minimizing energy usage has become a big challenge. One way to tackle this is by efficiently managing data storage and scheduling tasks. In this article, we suggest using an advanced machine learning model called Extreme Gradient Boosting (XGBoost) to predict electricity prices and optimize data storage, helping to reduce energy costs in data centers

Sentiment-driven Movie Recommendation System: A Machine Learning Approach

Pioneering method for enhancing movie recommendation systems through the integration of sentiment analysis of micro-blogging data. Leveraging natural language processing (NLP) techniques, the proposed system extracts sentiment from tweets and other micro-blogging sources pertinent to movies systematic approach encompassing the collection, preprocessing, and analysis of movie review data tailored for sentiment analysis tasks. Key aspects covered include the acquisition of publicly available datasets, methodologies for web scraping, preprocessing techniques, strategies for sentiment labeling, methods for data augmentation, procedures for data splitting, optimal data storage formats, and ethical considerations inherent in data collection and utilization. By offering a comprehensive guide, furnish both researchers and practitioners with the necessary tools to proficiently manage movie review data while navigating the ethical intricacies associated with its acquisition and application.

BLOOM : CLOUD OPTIMIZATION TOOL

Bloom: Cloud Optimization tool" endeavors to harness the power of the Ant Colony Optimization (ACO) algorithm to address the challenge of organizing unstructured input data in cloud storage systems. The primary objective is to optimize cloud storage utilization and minimize associated billing expenses. Through the utilization of ACO, the solution aims to transform unorganized and unstructured data into a structured format, facilitating efficient storage allocation and enhancing data management practices. By leveraging predictive insights derived from the structured data, the solution empowers both cloud service providers and users to make informed decisions regarding data storage optimization. Ultimately, this approach not only streamlines data organization processes but also aids in maximizing cost-effectiveness by ensuring that cloud resources are allocated optimally based on usage patterns and predicted future demands. The integration of ACO into the solution offers a promising avenue for addressing the complexities inherent in managing vast amounts of unstructured data in cloud environments, thereby paving the way for improved efficiency and cost savings.

Optimized Data Storage Method for Sharding-Based Blockchain

Optimized Data Storage Method for Sharding-Based Blockchain

Blockchain-based Scalable IoT Cloud for Electronic Health Record using Horizontal and Vertical Mining

The twin technology of Blockchain with IoT(BIoT) deployed in healthcare systems has fetched out the mutative potentiality for accessing and managing Electronic Health Records(EHRs). However, this divergence also has considerable challenges, primarily in terms of the protection of data, scalability, and interoperability. To overcome these challenges, this research paper presents a new approach, leveraging Blockchain technology and mining mechanisms, to launch a secure, scalable, and proficient IoT cloud ecosystem for the administration of Electronic Health Records. This research study includes Blockchain scalable IoT cloud architecture intended to address the scalability problem of storing and managing large amounts of EHR records produced through IoT sensors. The proposed mechanism implements both horizontal and vertical mining concepts for the optimization of data storage as well as the retrieval processes. Horizontal mining concentrates on allocating and distributing Electronic Health Records data to more than one node, which can ultimately sink the overload on each node in the network and increase the scalability. Vertical mining, on the other flip of the coin, prioritizes the transactions, and optimizing the data with each transaction can drastically improve the acceleration speed of retrieving the data access competency. Blockchain technology constructs the core technology of BEHRHVM, assuring data integrity, privacy, and transparency. All the EHR record transactions are authorized as mutable blocks on the Blockchain framework. This mechanism improved patient privacy while accessing the secure distribution of EHRs among authorized health providers. In addition, the proposed system is built to integrate with current healthcare systems and the Internet of Things.

Simulating CDT quantum gravity

We provide a hands-on introduction to Monte Carlo simulations in nonperturbative lattice quantum gravity, formulated in terms of Causal Dynamical Triangulations (CDT). We describe explicitly the implementation of Monte Carlo moves and the associated detailed-balance equations in two and three spacetime dimensions. We discuss how to optimize data storage and retrieval, which are nontrivial due to the dynamical nature of the lattices, and how to reconstruct the full geometry from selected stored data. Various aspects of the simulation, including tuning, thermalization and the measurement of observables are also treated. An associated open-source C++ implementation code is freely available online.

Comparison of methods for optimizing the speed of reading/writing drives

The objects of this study are data storage devices of various types and levels of complexity, as well as the principles of their operation. They are complex technical systems that include many components and are characterized by a high degree of integration. The subject of the research is to study the main characteristics of hard drives and solid-state drives. Their structure, functional features, principles of operation and ways of optimization are important. The purpose of the study is to determine the most effective methods for optimizing the operation of these devices. This includes aspects such as memory management, load balancing, power management, and others. The results of this research can be used to improve data efficiency, improve the performance of data storage systems and create new technologies in this area. This study examines the performance of various disk storage solutions through a series of tests aimed at understanding speed and dependence on external factors. The main conclusions of the study reflect the importance of the integrated use of optimization approaches to improve the speed of reading and writing data. Optimizing the processes of reading and writing data is critically important for modern high-performance computing systems, as well as for applications that require quick access to large amounts of information. The improved techniques used in the course of the study contribute to a significant increase in the performance of data storage devices. They take into account the specifics of various types of storage devices, including hard drives and solid-state drives, and offer optimization approaches that take into account their unique characteristics. Overall, the results of this study provide valuable insights into the principles of optimizing data storage, and they can serve as a basis for developing new strategies and solutions in this important area of information technology. This study represents a significant contribution to the scientific understanding of optimizing data reading and writing processes, and its findings may have long-term implications for the development of data storage technologies.

Directions for using big data analytics in logistics management

Logistics operations are becoming increasingly complex and require accurate data for effective management. The use of big data in logistics management is a relevant issue due to the growing volume of data and the need to optimize delivery and inventory management processes to meet market demands. The purpose of the study was to develop ways to optimize the management of big data analysis in logistics. To achieve this goal, the methods of analysis, experimentation, and comparison were used. As a result of the study, strategies for optimizing logistics management of big data analysis were developed and successfully applied. The Python programming language based programme effectively optimizes delivery routes using a clustering algorithm and visualizes the results of this process. Additionally, an informative diagram has been drawn up to illustrate the key stages of the developed strategies. The study also developed and presented a table describing the use of big data analysis methods in various logistics companies. The companies were compared in terms of functionality, data, results, and field of activity. It is established that the use of machine learning methods and optimization of data storage and processing significantly increases the efficiency of logistics operations. The results of this study can be used by logistics companies of any size, as well as enterprises engaged in supply chain management. In addition, the recommendations and strategies developed in this study may be useful for information technology and data analytics professionals involved in the development of software solutions and systems to optimize logistics processes

IoT 엣지디바이스를 이용한 경량화 클라우드 컴퓨팅환경 구축 및 검증

This study validated a cloud computing environment using edge devices for Internet of Things (IoT) sensor data collection and communication. Lightweight cloud infrastructure enhanced resource efficiency, minimized latency, and optimized data storage and processing. Technical achievements encompass standardized data exchange, improved device scalability and flexibility through software technologies, real-time data updates via synchronization, and the creation of an API-based user interface for an intuitive dashboard. The study substantiates the efficient establishment and validation of a cloud computing environment using edge devices, providing practical solutions for IoT data management and processing.

Digitalization of accounting: advantages and challenges in the era of digital technologies

Introduction. In the conditions of the development of society and the transition of enterprises to the information type of economy, there is a need to generate ideas, and creative thinking of personnel, release innovative products, increase the company's intangible assets, and create a positive image as a basis for ensuring the development of entrepreneurial skills. Therefore, the issue of the formation and development of entrepreneurial competencies in the modern conditions of digitalization of the economy is gaining special importance and relevance. The driving force of the digital economy is the demand for innovative digital technologies, which today are used in completely different spheres of social and economic life. Digital transformation of a business, which is a transformation through the revision of its business strategy, models, operations, goals, marketing approaches, and products through the adoption of digital technologies, is impossible without the implementation of digital technologies in the development of the accounting system. However, the digitalization of accounting opens not only new opportunities for enterprises but also creates additional risks. The purpose of the paper is to specify the advantages and disadvantages of using digital technologies in accounting. Results. The conducted research has allowed defining the essence of digitization and outlining the possibilities of its application for the development of accounting in modern Ukrainian enterprises. The state of utilization of contemporary digital technologies in the context of accounting has been diagnosed. The opportunities provided by the implementation of modern digital technologies in the field of accounting have been specified, and the risks associated with the use of such technologies have been identified. The obtained results can be useful for enterprise managers to substantiate the possible directions of digitization of accounting, taking into account the advantages and disadvantages outlined in the paper. Conclusions. The key conclusion from the study is a generalization of the advantages and disadvantages of digitization of accounting. Accordingly, the automation of manual work and optimization of data storage are the main advantages, and the high cost of implementation and provision of technical support is a disadvantage.

Secure data access using blockchain technology through IoT cloud and fabric environment

AbstractIn the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.

Analysis for RS-coding and fountain-code for erasure coding

This research provides an in-depth analysis of RS-coding and fountain-coding for erasure cod-ing, focusing on the different circumstances that data storage may face during daily usage. The essay outlines the fundamental information of RS-code and fountain-code, and com-pares their effectiveness in addressing varying degrees of erasure in the data disk. The research includes case studies and content analysis to identify appropriate circumstances for using dif-ferent coding methods. Based on these findings, the research concludes that RS-coding is more effective when fixing erasures up to a certain limit, beyond which fountain-coding is a better option. Finally, the research presents a research example to demonstrate the practical usage of different coding methods in specific circumstances, highlighting the advantages and disad-vantages between the two coding systems. Overall, this research provides valuable insights into erasure coding and can aid in optimizing data storage systems for efficient and reliable per-formance.

Optimizing data regeneration and storage with data dependency for cloud scientific workflow systems

The characters of large volume, complex dependency and frequent reuse of the data in data-intensive scientific workflow systems make the data management more and more complex but crucial. Automating the data management is an urgent need for scientific workflow systems and, requires accurate models and efficient methods for data storage and request response. This paper deals with the problems of data regeneration and storage optimization by using data dependency for cloud scientific workflow systems. Through representing the data storage strategies as 0-1 strings, a global discrete model for data storage optimization is constructed, which has a complexity of O(2n) (n is the dataset number). In terms of data regeneration, we develop a deterministic method to determine optimal regeneration strategies for requested datasets with minimal computation under any data storage strategy, such that the systems can satisfy the data requests automatically and evaluate the computation cost accurately. To solve this storage optimization problem, the enumeration method and elitist canonical genetic algorithm are constructed by shortening the representation of storage strategies to avoid redundant computation. At last, experiments are conducted to test the proposed methods. Experimental results and comparisons show the positiveness and effectiveness of the proposed methods for data management in cloud scientific workflow systems.

A New Blockchain-Based Authentication Framework for Secure IoT Networks

Most current research on decentralized IoT applications focuses on a specific vulnerability. However, for IoT applications, only a limited number of techniques are dedicated to handling privacy and trust concerns. To address that, blockchain-based solutions that improve the quality of IoT networks are becoming increasingly used. In the context of IoT security, a blockchain-based authentication framework could be used to store and verify the identities of devices in a decentralized manner, allowing them to communicate with each other and with external systems in a secure and trust-less manner. The main issues in the existing blockchain-based IoT system are the complexity and storage overhead. To solve these research issues, we have proposed a unique approach for a massive IoT system based on a permissions-based blockchain that provides data storage optimization and a lightweight authentication mechanism to the users. The proposed method can provide a solution to most of the applications which rely on blockchain technology, especially in assisting with scalability and optimized storage. Additionally, for the first time, we have integrated homomorphic encryption to encrypt the IoT data at the user’s end and upload it to the cloud. The proposed method is compared with other benchmark frameworks based on extensive simulation results. Our research contributes by designing a novel IoT approach based on a trust-aware security approach that increases security and privacy while connecting outstanding IoT services.

Accelerating Random Forest on Memory-Constrained Devices Through Data Storage Optimization

Random forests is a widely used classification algorithm. It consists of a set of decision trees each of which is a classifier built on the basis of a random subset of the training data-set. In an environment where the memory work-space is low in comparison to the data-set size, when training a decision tree, a large proportion of the execution time is related to I/O operations. These are caused by data blocks transfers between the storage device and the memory work-space (in both directions). Our analysis of random forests training algorithms showed that there are two major issues : <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</b> Block Under-utilization: data blocks are poorly used when loaded into memory and have to be reloaded multiple times, meaning that the algorithm exhibits a poor spatial locality; <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</b> Data Over-read: the data-set is supposed to be fully loaded in memory whereas a large proportion of data are not effectively useful when building a decision tree. Our proposed solution is structured to address these two issues. First, we propose to reorganize the data-set in such a way to enhance spatial locality and secondly, to remove the assumption that the data-set is entirely loaded into memory and access data only when effectively needed. Our experiments show that this method made it possible to reduce random forest building time by 51 to 95% in comparison to a state-of-the-art method.

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity.

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.