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检索条件"任意字段=2nd International Conference on Data Storage and Data Engineering, DSDE 2019"

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dsde 2019 - Proceedings of the 2019 2nd international conference on data storage and data engineering

DSDE 2019 - Proceedings of the 2019 2nd International Confer...

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2nd international conference on data storage and data engineering, dsde 2019

ISBN: (纸本)9781450372169

The proceedings contain 12 papers. The topics discussed include: segmentation of cinema audiences: an empirical finding from Indonesia;compaction and compression techniques for file systems based on persistent memories;from knowledge to innovation: transformation of Thai academic libraries in times of digital disruption;predictability comparison of three kinds of robbery crime events using LSTM;relationship between rewards and employee creativity in advertising agencies: an empirical study;analysis on key factors of successful entrepreneurship in cultural enterprises;and innovation on the performance management index system of public hospital based on balanced scorecard: a case study of S Hospital.

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Unraveling Decentralized data storage: A Comparative Analysis of IPFS and BitTorrent Networks 2

Unraveling Decentralized Data Storage: A Comparative Analysi...

引用

2nd international conference on Electrical engineering and Automatic Control (ICEEAC)

作者： Mazri, Ammar Mehdi, Merouane Univ Blida Elect Dept Blida Algeria

ISBN: (纸本)9798350349740;9798350349757

Decentralized storage and distributor provides a way to reduce central control and control by one entity, resulting from cloud storage. This trend in storage has gained great popularity due to technological developments. In particular, BitTorrent and IPFS networks, belonging to two generations of peer-to-peer data networks. Therefore, in this paper, we will provide a technical overview of both the IPFS network and emphasize the new developments created by this technology and compare it specifically with the BitTorrent network. We identify basic common and overview comparison of quality. Through this view, we derive future challenges and research objectives related to data distribution networks.

关键词： IPFS BitTorrent P2P Detection IDS

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A Secure Approach to Avoid data Repetition in Cloud storage Systems 2

A Secure Approach to Avoid Data Repetition in Cloud Storage ...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Swedhaa, M.A. Sathya, V. Vanitha, M. Jothilakshmi, T. Kongunadu College of Engineering and Technology Artificial Intelligence and Data Science Trichy India Mohamed Sathak A.J. College of Engineering Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350349221

Cloud storage will be current data research and data management field in terms of security and elimination of repeated data-sets. In simple terms, this current research introduces a strong system called "Cloud-SecureDedupe(C-SD)". to make sure data is safe and cloud storage works well. The system helps with a common problem called data repetition, where the same information is stored more than once. "Cloud-SecureDedupe (SDP)"uses a safe method to solve this issue, making sure your data is reliable in cloud storage. It adds extra protection by using advanced techniques to keep important information secure. Additionally, it smartly identifies and removes any repeated data, not only making your data more secure but also saving space in the storage, so it's used more efficiently. The system has been carefully tested and proven to work well, ensuring that your experience with cloud storage is both safe and smooth. © 2024 IEEE.

关键词： Cloud storage

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Utilizing Hybrid Cloud Strategies to Enhance data storage and Security in E-Commerce Applications 2

Utilizing Hybrid Cloud Strategies to Enhance Data Storage an...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Gorantla, Venkata Ashok K Gude, Venkataramaiah Sriramulugari, Shiva Kumar Yuvaraj, N. Yadav, Piyush Sri Shakthi Institute of Engineering and Technology Department of Cse Coimbatore India Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9798350371055

This paper outlines a strategy for utilizing hybrid clouds to enhance data storage and security in e-commerce applications. Hybrid cloud deployments extend the security, flexibility, scalability, and cost-effectiveness of traditional cloud computing architectures, as well as makes use of the latest cloud security protocols to ensure data is kept safe. Hybrid cloud deployments are proven to provide better storage performance, more efficient data handling, and improved availability compared to traditional on-premise solutions. This paper will explore the challenges associated with building a secure, resilient, and scalable hybrid cloud architecture, and discuss key considerations for implementing such a solution. Additionally, it will discuss how organizations can protect data from unauthorized access, since hybrid cloud deployments often follow the shared responsibility model. Ultimately, this paper will discuss how the improved storage and security of a hybrid cloud can benefit e-commerce applications and increase customer confidence. © 2024 IEEE.

关键词： Cloud Strategies data Protection data Security data storage E-commerce Hybrid Cloud

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DNA data storage: A Solution to the Growing Digital data Dilemma 2

DNA Data Storage: A Solution to the Growing Digital Data Dil...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Manodee, Akarsh Pandey, Neeraj Kumar Mishra, Amit Kumar Kumar, Amit Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

In a time of data expansion our digital footprint, on a basis surpasses 2.5 quintillion bytes prompting the importance of considering backup and storage options. Conventional approaches encounter difficulties with the added risks posed by cyber threats and system failures. This study advocates for a shift towards DNA based data storage. DNA, known for its durability and efficiency has the capability to encode and store amounts of data in a space small, as a golf ball with ancient strands demonstrating its long-lasting nature. Given the repercussions of losing information in todays world this research emphasizes the crucial necessity of safeguarding digital data by suggesting DNA as an exceptional solution. It outlines technologies for synthesizing artificial DNA and encoding binary data into DNA strands. Overcoming obstacles like cost, speed, and uniformity is crucial for viable DNA data storage. The study emphasizes the necessity of industry-wide standards for scalability and acceptance, calling for increased cooperation. In a world where data's importance is escalating, DNA data storage emerges as a unique opportunity to safeguard digital information. Transcending unbeatable backups, it revolutionizes data preservation. As technology advances, DNA data storage could become the industry standard, ushering in a new era of data security and accessibility. © 2024 IEEE.

关键词： Artificial DNA synthesis Binary data encoding Cyber threats data preservation data storage DNA-based storage

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Research on Security storage and Access Control System of Big data in Universities Based on Blockchain Technology 2

Research on Security Storage and Access Control System of Bi...

引用

2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Yu, Xiao Wuhan Technology and Business University Hubei Wuhan430065 China Institute of Information and Intelligent Engineering Applications Hubei Wuhan430065 China

ISBN: (纸本)9798350373646

With the rise of the big data era, universities are encountering significant challenges in safeguarding data security and ensuring privacy protection. Blockchain technology, renowned for its decentralized, secure, and reliable distributed database features, offers a promising avenue for addressing the security and access control issues associated with big data in universities. Initially, this study examines the current state and prevalent challenges concerning the security storage and access control of big data within university environments. Subsequently, it provides an in-depth overview of blockchain technology and its potential application in enhancing data security within this context. Following this, a conceptual framework for a university-based big data security storage and access control system leveraging blockchain technology is proposed. This framework is elaborated upon, covering aspects such as data encryption, access control strategies, and consensus mechanisms. Finally, the efficacy and practicality of the proposed system in enhancing the security of big data storage and access control within universities are evaluated through experimentation, alongside an outlook on future research directions. This study aims to offer novel theoretical insights and practical guidelines for enhancing big data security storage and access control within university settings. © 2024 IEEE.

关键词： blockchain technology control system data encryption secure storage

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Research on the Optimization of storage Strategies for EMU On-Board data 2

Research on the Optimization of Storage Strategies for EMU O...

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2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： Yang, Chunhui Zhang, Weijiao Sun, Chaoyuan Zhao, Shiwei China Academy of Railway Sciences Corporation Limited Institute of Computing Technologies Beijing100081 China

ISBN: (纸本)9798350368208

This paper conducts an optimization study on the storage strategy of EMU on-board data, proposing a performance ratio-based weighted calculation method to address storage challenges in a big data environment. EMU on-board data is crucial for ensuring the safety and efficiency of train operations, yet the surge in data volume presents significant storage and processing difficulties. By evaluating two prominent big data storage solutions, Hbase and Hive, and analyzing the performance impact of snappy and gzip compression methods, the experiment reveals that Hive-snappy demonstrates superior query performance, while Hbase-gzip shows a clear advantage in space efficiency. The paper introduces time efficiency and space efficiency indicators and combines them with a comprehensive weighted performance ratio (WPR) to provide a scientific basis for data storage strategy in the railway industry. Future research will delve into data compression technology, storage strategy optimization, real-time data processing, data security and durability, and cross-domain applications to promote technological advancement in related industries. © 2024 IEEE.

关键词： storage efficiency

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Enhancing Enterprise data Management with Secure and Scalable Cloud storage Solutions 2

Enhancing Enterprise Data Management with Secure and Scalabl...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Kanna, Rajesh Lakshmi, D. Muneeshwari, P. Valantina, G. Mary Suguna, R. Department of Petroleum Engineering AMET Deemed to be University Chennai India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350383867

Today it has become extremely critical for the business to utilize and safeguard the data(Enterprise data) in a proper way in order to achieve the organizational success which is in accordance with the digital environment. The objective of this paper is to analyze the paradigm change in the patterns from relying mainly on secure and scalable cloud storage solutions to handle evolving challenges encountered in data management. The shift from old stands on site in console to a cloud based storage system gives enterprises unlimited capacity not only is that but they also can scale accordingly and get the best security features. No sentiment was spared by the introductory abstract, which discussed the advantages to the end users of cloud storage, from its flexibility and accessibility to being cheaper than preserving data in conventional storage modes. On the other hand, abstract attracts the security in cloud storage, including encryption, access controls, and compliance certificates that help protect the information from a theft and other risks. The other topic that has been touched upon is scalability, which demonstrates how the cloud makes enterprises to be very adaptable to current storage requirements and enabling growth of storage that is astronomical in magnitude. Consequently, the poem abstract illustrates the essential functions of cloud storage as the technology that particularly empowers innovation through data science and use of analytics and artificial intelligence. This article demonstrates the need for organizations to implement a cloud standard purpose made for the storage of data and the harm of business in the digital age as a strategic priority amidst data management activities. © 2024 IEEE.

关键词： Cloud storage

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Enhancing Efficiency in Large Scale data Processing: Optimizing Cluster Compute and storage Resources 2

Enhancing Efficiency in Large Scale Data Processing: Optimiz...

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2nd international conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Chennareddy, Venkateswarlu Koppula, Rama C. Patibandla, Pavan Kumar Technical Lead and IEEE Member Seattle United States Principal Software Engineer Salesforce United States Engineering Amplitude Inc United States

ISBN: (纸本)9798350371314

In an era dominated by Artificial Intelligence (AI) and Big data, managing and generating foundational data for large-scale data models presents significant challenges in terms of resources and costs. This paper introduces a novel method aimed at optimizing cluster compute and storage resources, critical for AI applications requiring extensive data processing. By utilizing a custom Stratified Sampling algorithm along with a fully automated, independent, and self-adaptive data comprehensive test suite module, this approach efficiently produces a minimized dataset while maintaining essential coverage of business scenarios. This method not only ensures the integrity and representativeness of the data required for business use cases, but also promises resource reduction, with an estimated 80-85% savings in cluster compute and storage resources. Addressing a pressing need in data processing, this method emerges as a crucial asset for entities seeking to enhance their data management capabilities while ensuring data quality and comprehensive in an AI-centric landscape. © 2024 IEEE.

关键词： Artificial intelligence

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data storage and Analysis of Power Supply Service Grid Cloud Telephone System in Big data Environment 2

Data Storage and Analysis of Power Supply Service Grid Cloud...

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2nd IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2024

作者： You, Chunling Guo, Yongjie Luo, Minyu Huang, Qinjian Liao, Qinwu Wu, Yuanlin State Grid Fujian Electric Power Co. Ltd. Marketing Service Center Fujian361006 China Fuzhou University Fujian350004 China State Grid Fuzhou Electric Power Supply Company Fujian350004 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fujian350001 China

ISBN: (纸本)9798350368208

In the big data environment, the data storage and analysis of the power supply service grid cloud telephone system face challenges of huge data volume and high processing speed requirements. In order to effectively address this issue, this study adopted cloud storage technology and big data analysis framework, combined with real-time data processing and batch data processing strategies, optimized the data storage structure, and achieved efficient querying and analysis of data. This study first adopts a distributed storage architecture and optimized data storage through the Hadoop Distributed File System (HDFS). Next, the article uses Apache Spark to achieve fast processing and analysis of data, improving the parallelism and efficiency of data processing. Finally, the article introduces Kafka to achieve real-time streaming processing of data, ensuring that data can be quickly analyzed and responded to while receiving real-time updates. The experimental results show that through the integration of this series of technologies, the storage speed of the system can reach up to 682 KB/s, and the data processing speed can also reach 800.0 KB/s, with an average error of only 1.2%. In the above data conclusions, combining distributed storage and real-time data processing technology can not only effectively solve the data storage and processing problems of power supply service grid cloud telephone systems in big data environments, but also improve the overall performance of the system. © 2024 IEEE.

关键词： Cloud storage

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