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Journal articles on the topic 'Data Storage Optimization'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Koka, Naveen. "Data Storage Codification for Enhanced Optimization." International Journal of Science and Research (IJSR) 10, no. 6 (2021): 1813–16. http://dx.doi.org/10.21275/sr24608151607.

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Ekta, Mrinal. "Secured Cloud Data Sharing: Privacy-Preserving Storage Optimization with Data Confidentiality." International Journal of Research Publication and Reviews 4, no. 8 (2023): 2957–66. http://dx.doi.org/10.55248/gengpi.4.823.51935.

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Yolchuyev, Agil, and Janos Levendovszky. "Data Chunks Placement Optimization for Hybrid Storage Systems." Future Internet 13, no. 7 (2021): 181. http://dx.doi.org/10.3390/fi13070181.

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“Hybrid Cloud Storage” (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of “Public Cloud Storage” as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This

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Naveen, Koka. "Data Storage Codification for Enhanced Optimization." Journal of Scientific and Engineering Research 8, no. 6 (2021): 142–46. https://doi.org/10.5281/zenodo.12805341.

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In today's digital landscape, the integration of mobile applications with server systems presents both opportunities and challenges in data management. This symbiotic relationship necessitates seamless synchronization between mobile devices and servers, particularly in scenarios where internet connectivity fluctuates. Strategies such as Data Storage Codification for Enhanced Optimization offer a solution, enabling robust data storage and retrieval processes that remain functional even in offline settings. By leveraging mechanisms such as cloud document servers and unique identifiers, organizat

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Ur Rahaman, Shafeeq. "Data - Driven Warehouse Automation and Route Optimization in Cold Storage Logistics." International Journal of Science and Research (IJSR) 9, no. 3 (2020): 1718–25. http://dx.doi.org/10.21275/sr200310112008.

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Talakh, M. V., V. V. Dvorzhak, and Yu O. Ushenko. "Denormalization techniques for IOT data warehouses: balancing query performance and data redundancy." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 72–81. https://doi.org/10.31649/1681-7893-2025-49-1-72-81.

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This article explores the impact of denormalization techniques on query performance in IoT data warehouses while maintaining acceptable data redundancy. It analyzes normalized and denormalized approaches in a smart home IoT environment using Azure Synapse. Empirical testing (10,000–5 million records) shows that strategic denormalization combined with columnar storage optimization improves performance by up to 94%. Evaluating four key optimization techniques (Join Reduction, Columnar Storage, Query Complexity Optimization, Temporal Scaling Optimization), we find that denormalization initially i

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Feser, John, Sam Madden, Nan Tang, and Armando Solar-Lezama. "Deductive optimization of relational data storage." Proceedings of the ACM on Programming Languages 4, OOPSLA (2020): 1–30. http://dx.doi.org/10.1145/3428238.

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Butil, John Carlo M., Ma Lei Frances Magsisi, John Hart Pua, Prince Kevin Se, and Ria Sagum. "The Application of Genetic Algorithm in Motion Detection for Data Storage Optimization." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 199–202. http://dx.doi.org/10.7763/ijcce.2014.v3.319.

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Alekseev, A., A. Kiryanov, A. Klimentov, et al. "Data Handling Optimization in Russian Data Lake Prototype." Journal of Physics: Conference Series 2438, no. 1 (2023): 012021. http://dx.doi.org/10.1088/1742-6596/2438/1/012021.

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Abstract CERN experiments are preparing for the HL-LHC era, which will bring an unprecedented volume of scientific data. These data will need to be stored and processed by thousands of physicists, but expected resource growth is nowhere near the extrapolated requirements of existing models, in terms of both storage volume and compute power. Opportunistic CPU resources such as HPCs and university clusters can provide extra CPU cycles, but there is no opportunistic storage. In this article, we will present the main architectural ideas, deployment details, and test results, with emphasis on our r

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Yuan, Zhu, Xueqiang Lv, Yunchao Gong, Boshan Liu, Haixiang Yang, and Xindong You. "Text Semantics-Driven Data Classification Storage Optimization." Applied Sciences 14, no. 3 (2024): 1159. http://dx.doi.org/10.3390/app14031159.

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Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to ext

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Baladari, Venkata. "Intelligent Tier-Based Data Management: A Predictive Approach to Cloud Storage Cost Optimization." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2583–86. https://doi.org/10.21275/sr23089114850.

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12

S., Sharmila Selvi. "STORAGE OPTIMIZATION OF REPOSITORIES USING DATA MINING AND BIG DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 5 (2019): 516–18. https://doi.org/10.5281/zenodo.3234982.

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Software Configuration Management (SCM) deals with various changes and evolution in the software. Each software comprises of thousands of versions. Individual versions need to be stored again and again. Every software keeps on evolving so we need to keep track on each evolution. Software engineer uses mining techniques to store and retrieve these kinds of data’s. This research paper deals with the design, and implementation of an efficient storage management for SCM repositories that facilitates a developer’s to store revisions of software changes using Map reduce Techniques. The m

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Li, Xu, Qi Shen, and Tiancheng Yang. "Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis." Applied and Computational Engineering 69, no. 1 (2024): 168–73. http://dx.doi.org/10.54254/2755-2721/69/20241503.

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This paper explores the design and optimization of multidimensional data models to enhance the query performance and data analysis capabilities of OLAP (Online Analytical Processing) systems. It delves into three prominent dimensional modeling techniques: Star Schema, Snowflake Schema, and Galaxy Schema, analyzing their impact on query complexity, data redundancy, storage requirements, and ease of maintenance. Additionally, it examines three aggregation strategiesPre-Aggregation, Dynamic Aggregation, and Hybrid Aggregationfocusing on their effectiveness in balancing query response time, storag

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V. Powar, Ranjeet, and B. Arunkumar. "Massive Volume of Unstructured Data and Storage Space Optimization- a Review." International Journal of Engineering & Technology 7, no. 3.27 (2018): 252. http://dx.doi.org/10.14419/ijet.v7i3.27.17888.

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Nowadays the volume of digital data generated and used by enterprises is increasing at an enormous rate. The survey says that more than 80% of data that were generated in the last two years are unstructured in nature. Hence storage space requirement for storing this big volume of unstructured data is very high. It has gained attention to large-scale storage systems. Deduplication is a space efficient method mainly used to solve storage space optimization problem. This paper focuses on the effect of massive volume of unstructured data and review various storage optimization techniques and surve

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Bhattacharya, Hindol, Samiran Chattopadhyay, Matangini Chattopadhyay, and Avishek Banerjee. "Storage and Bandwidth Optimized Reliable Distributed Data Allocation Algorithm." International Journal of Ambient Computing and Intelligence 10, no. 1 (2019): 78–95. http://dx.doi.org/10.4018/ijaci.2019010105.

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Distributed storage allocation problems are an important optimization problem in reliable distributed storage, which aims to minimize storage cost while maximizing error recovery probability by optimal storage of data in distributed storage nodes. A key characteristic of distributed storage is that data is stored in remote servers across a network. Thus, network resources especially communication links are an expensive and non-trivial resource which should be optimized as well. In this article, the authors present a simulation-based study of the network characteristics of a distributed storage

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Zhang, Jiayi, and Lin Shi. "Optimization of Network Furniture Management System Based on Big Data." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/9698853.

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In order to make the traditional household equipment can be remotely controlled, wireless networking technology is introduced into the traditional furniture equipment to achieve the effect of access and remote control. In view of the problem that the traditional cloud storage system lacks flexibility, opacity, weak robustness and cannot effectively store, manage, and maintain big data in data storage, a big data oriented cloud storage system is designed and implemented to intelligently process the business requests of the furniture system through the logic control module. The user access modul

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Vikas, S., and S. N. Thimmaraju. "Data Optimization using Apache Flink." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 137–42. https://doi.org/10.35940/ijeat.B3081.129219.

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Map Reduce, Flink, and Spark, also become more popular in the processing of big data lately. Flink will be an open platform Big Data processing system for Apache-powered batch storage and streaming of data. Flink's query optimizer is constructed for historical information processing (batch) based on parallel storage systems approaches. Flink query query optimizer interprets the questions into jobs of different tasks that are regularly sent. Therefore, taking advantage of task similarities should prevent redundant computation. In this article, the multi-demand optimization model for Flink,

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Vitthal Nil, Krishna. "Data Leakage Optimization in Multi-cloud Storage Services." International Journal of Computer Applications 175, no. 16 (2020): 43–47. http://dx.doi.org/10.5120/ijca2020920668.

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Bessem, J. M., and H. R. E. van Maanen. "Optimization of digital storage of random analogue data." Measurement Science and Technology 5, no. 11 (1994): 1331–38. http://dx.doi.org/10.1088/0957-0233/5/11/002.

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Kim, Jaehun, and Soo-Mook Moon. "Storage Trie Optimization Based on Ethereum Transaction Data." Journal of KIISE 51, no. 2 (2024): 110–14. http://dx.doi.org/10.5626/jok.2024.51.2.110.

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Chandrakanth, Lekkala. "Cloud-Based Data Warehousing Optimization Techniques." Journal of Scientific and Engineering Research 9, no. 5 (2022): 114–18. https://doi.org/10.5281/zenodo.12789974.

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This article delves into enhancing cloud-based data warehousing's efficiency with the accompanying expert on Snowflake and Amazon Web Services (AWS). Companies are relying more and more on cloud systems for storing and analyzing data, and optimizing data warehousing now seems to be a really important part for performing well during queries, cutting down data storage costs, and managing data in a good way. This study delivers a case study that describes the optimization methods utilized in a Snowflake installation on AWS. This approach leads to performance improvements and cost savings. The opt

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Hongtao Liu. "Optimization and performance improvement of distributed data storage in hybrid storage systems." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 459–67. http://dx.doi.org/10.30574/wjaets.2024.13.1.0443.

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With the rapid development of information technology, the storage and processing of massive data has become one of the important challenges facing the current computing field. Traditional centralized storage systems have been unable to meet the needs of big data applications, while distributed storage systems have become the infrastructure of modern data centers because of their high scalability and high availability. However, in practical applications, a single distributed storage model is often difficult to balance cost-effectiveness and performance requirements. Therefore, this paper propos

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Saukani, Imam, Eko Nuraini, Slamet Nurhadi, et al. "Format Methods on Storage Media (Hard Disk) for Optimization Data Storage Capacity." Asian Journal Science and Engineering 2, no. 2 (2024): 126. http://dx.doi.org/10.51278/ajse.v2i2.1018.

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This research is to determine how much storage capacity in the File Allocation Table 16 (FAT16), File Allocation Table 32 (FAT 32) and New Technology File System (NTFS), the use of the hard drive is currently the of the maximum capacity will not be able to use when not using the appropriate partition, because it can affect the amount of storage capacity available after the hard disk in the partition. This type of research is reviewed based on its purpose of use, so the research to be conducted is applied research because the products of this research can be used by all computer users. Ultimate

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24

Atri, Preyaa. "Cloud Storage Optimization Through Data Compression: Analyzing the Compress-csv-files-gcs-bucket Library." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 3 (2023): 498–500. https://doi.org/10.51219/JAIMLD/preyaa-atri/134.

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This paper examines the hypothetical Compress-csv-files-gcs-bucket library, analyzing its potential role in optimizing Google Cloud Storage (GCS) by compressing files within buckets. We discuss the problem of storage inefficiency in cloud environments and present compression as a solution. The paper then explores potential use cases, implementation considerations, and the impact this library could have on data management and cost reduction. Finally, we address limitations and propose areas for further research.

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Qin, Yang, Weihong Yang, Xiao Ai, and Lingjian Chen. "Fault tolerant storage and data access optimization in data center networks." Journal of Network and Computer Applications 113 (July 2018): 109–18. http://dx.doi.org/10.1016/j.jnca.2018.04.001.

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Joshi, Ms Indira. "BLOOM : CLOUD OPTIMIZATION TOOL." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32255.

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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

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Pogorelov, A., Ye Pogoryelov, and A. Zhuravlev. "Optimization of data storage density and data media quality monitoring in film storage thermal recording devices." Journal of Magnetism and Magnetic Materials 249, no. 3 (2002): 428–30. http://dx.doi.org/10.1016/s0304-8853(02)00463-8.

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28

Noon, Nan, Janusz Getta, and Tianbing Xia. "Optimization of Parallel Data Transfers in Multi-Tiered Persistent Storage." American Journal of Information Science and Technology 8, no. 3 (2024): 84–97. http://dx.doi.org/10.11648/j.ajist.20240803.14.

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A logical model of multi-tiered persistent storage provides a view of data where all available storage resources are distributed over a number of levels depending on the data transfer parameters and capacities. The efficient parallelization of data transfers in multi-tiered persistent storage is a significant challenge for a pipelined data processing model. This work examines a category of database applications implemented as sequences of operations that transfer data between the levels of multi-tiered persistent storage. The concept of EPN: Extended Petri Nets represents how database applicat

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29

Maghfiroh, Lutfi Rahmatuti, and Ramadhan Azizulhakim Yusuf. "Study of Search Algorithm Optimization from Multi-Version Data Warehouse using NoSQL Non-relational Database." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (2022): 173–84. http://dx.doi.org/10.34123/icdsos.v2021i1.74.

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Statistics Indonesia, which produces large-scale data, requires effective and optimal storage. Research related to Multi-Version Data Warehouse (MVDW), which utilizes document-based NoSQL itself, has attempted to be developed for the sake of BPS data storage and proposed an algorithm to store and search data. This paper is made to examine algorithm optimization methods to reduce the time used in the process of storing and searching data when needed. The algorithm proposed in this paper focuses on the data storage process by suggesting a storage model that generalizes the coding of variables in

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Vempati, Sekhar. "Whale Optimized Distributed Computing Data Lake for Energy Storage." Journal of Computer Allied Intelligence 2, no. 5 (2024): 17–30. http://dx.doi.org/10.69996/jcai.2024022.

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This paper presents the Whale Seahorse Optimization Distributed Computing (WSODS) algorithm, a novel approach that combines the Whale Optimization Algorithm (WOA) and Seahorse Optimization Algorithm (SOA) within a distributed computing framework. WSODS aims to address complex optimization challenges across various domains, including power storage systems and data lake architectures. The algorithm's performance was evaluated based on key metrics such as data processing time, system throughput, resource utilization, and scalability. The evaluation results indicate that WSODS significantly enhanc

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Rathod, Subhash Gulabrao, Mangesh D. Salunke, Hemantkumar B. Jadhav, Deepika Amol Ajalkar, Dinesh Banurao Satre, and Devyani Bonde. "Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 316–23. http://dx.doi.org/10.17762/ijritcc.v11i7s.7005.

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The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can de

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Tosi, S., and T. Conway. "Detector target response optimization for multitrack digital data storage." IEEE Transactions on Magnetics 42, no. 7 (2006): 1926–28. http://dx.doi.org/10.1109/tmag.2006.874097.

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33

Hossain, Kaium, Mizanur Rahman, and Shanto Roy. "IoT Data Compression and Optimization Techniques in Cloud Storage." International Journal of Cloud Applications and Computing 9, no. 2 (2019): 43–59. http://dx.doi.org/10.4018/ijcac.2019040103.

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This article presents a detailed survey on different data compression and storage optimization techniques in the cloud, their implications, and discussion over future directions. The development of the smart city or smart home systems lies in the development of the Internet of Things (IoT). With the increasing number of IoT devices, the tremendous volume of data is being generated every single day. Therefore, it is necessary to optimize the system's performance by managing, compressing and mining IoT data for smart decision support systems. In this article, the authors surveyed recent approach

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Gagliardi, Marco, and Cosimo Spera. "Optimization models for computer data storage design: An application." Computers & Industrial Engineering 26, no. 4 (1994): 743–56. http://dx.doi.org/10.1016/0360-8352(94)90009-4.

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HAYASHI, SHINICHI, and NORIHISA KOMODA. "Data Location Optimization for Improvement of Tiered Storage Performance." Electronics and Communications in Japan 98, no. 10 (2015): 1–11. http://dx.doi.org/10.1002/ecj.11696.

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36

Dubey, Piyush. "The Data Lakehouse: An Evolving Paradigm in Data Architecture." International Journal of Computing and Engineering 7, no. 10 (2025): 30–47. https://doi.org/10.47941/ijce.2958.

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The data lakehouse architecture represents a transformative evolution in data management, addressing critical limitations in traditional big data architectures. This paradigm combines data lake flexibility with data warehouse capabilities, creating a unified platform that eliminates redundant data copies and streamlines processing workflows. By implementing a layered structure—encompassing storage, metadata, catalog, semantic and query optimization components—the lakehouse provides comprehensive support for diverse analytical workloads while maintaining centralized governance. The architecture

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Vasuinthira, N. "Efficiently Data Analysis and Transmission for consumer using grid computing." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 643–50. https://doi.org/10.31142/ijtsrd10991.

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Research focuses on the issue of wireless big data computing in smart grid. Smart grid means electrical network architecture is purposed for generating, distributing and administering efficiently the power consumption to end users. First investigate the consistency between the characteristics of big data and smart grid data. A propose a hybrid approach for storage planning, which consists of an outer optimization based on genetic and an inner optimization algorithm for energy scheduling. Propose a big data computing architecture for smart grid, consisting of four main levels data sources, data

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Chen, Zhu Min. "Node Storage Optimization in Cloud." Advanced Materials Research 811 (September 2013): 520–24. http://dx.doi.org/10.4028/www.scientific.net/amr.811.520.

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With the growing demand for mass data storage, cloud storage has become an inevitable trend of the development of the storage. In order to improve the efficiency and stability of cloud storage system, this paper presents an optimization algorithm based on cloud storage. Node idle zone and node resource usage view is divided, while integrating node and global scheduling method, which can improve the cost-effective and stability of cloud storage system.

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Preyaa, Atri. "Efficient Data Transformation on Google Cloud Storage: A Python Library for Converting CSV to Parquet." European Journal of Advances in Engineering and Technology 8, no. 3 (2021): 59–62. https://doi.org/10.5281/zenodo.11408142.

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The ever-growing volume of data in various formats poses significant challenges for storage optimization and efficient analytics in cloud environments. Parquet, a columnar data format, offers substantial advantages over traditional CSV (Comma-Separated Values) files in terms of storage efficiency, query performance, and data compression. This paper explores a Python library, gcs_convert_csv_to_parquet, designed to seamlessly convert CSV files stored in Google Cloud Storage (GCS) buckets to Parquet format. We analyze the library's functionalities, potential use cases, and its impact on data eng

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Ms., Gloriya Kardile, and Vaibhavi Channe Ms. "AWS S3 Classes: A Comparative Analysis and Optimization Strategies for Cost Efficiency and Performance Optimization for Organizations." International Journal of Advance and Applied Research S6, no. 22 (2025): 287–95. https://doi.org/10.5281/zenodo.15501607.

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<em>Cloud storage services, particularly Amazon Simple Storage Service (S3), have become fundamental components for organizations seeking scalable and cost-effective solutions for data storage. With Amazon S3 offering multiple storage classes tailored to different performance, Durability, and cost requirements, selecting the appropriate storage class has become a critical decision for organizations aiming to optimize both cost efficiency and performance. This paper presents a comprehensive comparative analysis of the various storage classes available in Amazon S3, including Standard, Standard-

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Xin, Gang, and Hui Yan. "Study on the Optimization of Data Mining in Big Data." Advanced Materials Research 989-994 (July 2014): 1837–40. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1837.

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This paper proposes an analysis measure for Big Data by optimizing traditional data mining, base on Weka data analyzing platform ,K-means algorithm is employed through the interface programs in Weka system, so that optimized data mining techniques can be applied in cloud storage, cloud computing of Big Data by clustering analysis base on Big Data pre-processing and real-time monitoring of memory.

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Zhang, Zhi Tong. "Optimization of History Tree in 3DR-Tree Index Structure." Applied Mechanics and Materials 347-350 (August 2013): 2521–23. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2521.

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Many optimizations have been done to 3DR-tree index structure and many opinions have been proposed. Modification by splitting mechanism is one of them. There are two index trees in 3DR-tree index structure after modification: one is a history tree for past data storage and the other is an active tree for current data storage. In this article, optimization of history tree is firstly done and is proved theoretically. Then a correspondent insert algorithm is designed.

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Lin, Weiwei, Zhiqiang Xu, Qin Li, Huifang Zhang, Yong Wang, and Yun He. "Research on reliable data communication optimization strategy for energy storage power stations considering link-service importance." Journal of Physics: Conference Series 2849, no. 1 (2024): 012123. http://dx.doi.org/10.1088/1742-6596/2849/1/012123.

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Abstract Aiming at the reliability of large-capacity data transmission of energy storage power stations, an optimization strategy for reliable data communication of energy storage power stations considering the importance of link service is proposed. Firstly, a link risk degree equilibrium optimization model is constructed based on the data communication network of energy storage power stations, and business importance classification is considered. Then, based on considering the link risk degree of the importance of the service, a data communication optimization method is proposed, which takes

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Mei, Chao, Peiming Chen, Ying Li, et al. "Operation optimization of data center based on multi-station integration." ITM Web of Conferences 45 (2022): 01001. http://dx.doi.org/10.1051/itmconf/20224501001.

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With the rapid development and extensive application of Internet technology, the data information grows exponentially. The proposal of the multi-station integration mode not only provides effective assistance for the development of the big data industry, but also put forward a new direction for the energy-saving operation of data centers. This paper presents a cooperative operation architecture of the fusion station covering substation, data center and energy storage power station, and establishes a mixed integer nonlinear programming model which aims at minimizing the annual total cost. The a

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Wang, Yajun, Shengming Cheng, Xinchen Zhang, Junyu Leng, and Jun Liu. "Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform." Mathematical Problems in Engineering 2021 (December 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/3839800.

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The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution

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He, Dandan, Lijuan Wang, and Can Wang. "The Analysis of RDF Semantic Data Storage Optimization in Large Data Era." IOP Conference Series: Earth and Environmental Science 128 (March 2018): 012151. http://dx.doi.org/10.1088/1755-1315/128/1/012151.

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Lohia, Dr Harsh. "Secure Data Storage Optimization Over Cloud using Encrypted Cloud Data Deduplication Technique." Journal of Science and Technology 9, no. 1 (2024): 131–38. http://dx.doi.org/10.46243/jst.2024.v9.i01.pp131-138.

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Sreenu, Maddipudi. "SAP HANA Database Data storage management using Native Storage Extension and Near Line Storage." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 4 (2023): 1–6. https://doi.org/10.5281/zenodo.14381599.

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SAP HANA provides powerful data storage and management capabilities, addressing the challenges of high data volumes across different temperature data—hot, warm, and cold. This paper explores SAP HANA’s Native Storage Extension (NSE) and Near-Line Storage (NLS) as effective solutions for managing multi-temperature data, enhancing performance, and optimizing storage costs. By implementing these technologies, businesses can ensure that mission-critical data is stored in high-performance memory (hot data), while less critical data is moved to lower-cost storage (warm and cold data) wit

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Yin, Xiao Hui, Peng Dong Gao, Chu Qiu, and Yong Quan Lu. "Parallel Processing and Performance Optimization of Meteorological Satellite Mass-Data Program." Applied Mechanics and Materials 263-266 (December 2012): 192–97. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.192.

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We implemented parallel processing of polar-orbiting meteorological satellite MERSI data aerosol retrieval program. The parallel implementation was based on Linux cluster architecture. The master-slave parallel programming mode on MPI parallel environment was applied. Performance optimizations are made in load balance, communication overhead, storage and system I/O according to the specific environment. In addition, parallel speed-up ratio and efficiency were analyzed to evaluate the experiment results. Experimental results demonstrate that the parallel techniques and the performance optimizat

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Globa, Larysa, and Anton Kartashov. "OPTIMIZING DISTRIBUTED DATA STORAGE IN MULTI-CLOUD ENVIRONMENTS: ALGORITHMIC APPROACH." Information and Telecommunication Sciences, no. 2 (December 23, 2024): 4–12. https://doi.org/10.20535/2411-2976.22024.4-12.

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Background. Multi-cloud environments present complex challenges in optimal resource allocation and provider selection. Previous research has established a comprehensive ontological model and evaluation criteria for distributed data storage, however efficient provider selection remains a significant challenge due to the dynamic nature of cloud services and the multitude of interdependent factors affecting performance and cost-effectiveness. Objective. The purpose of the paper is to develop and validate a sophisticated optimization function for cloud provider selection in multi-cloud environment

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