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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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BMDP:Blockchain-Based Multi-cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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Multi-View Clustering Via Multi-Manifold Regularized Concept Factorization

SSRN

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SSRN 2024年

作者： Ban, Yong Su, Shaosheng Cai, Yongming Huang, Zhanpeng College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou510006 China Information department of Zhongshan City People’s Hospital Zhongshan528403 China Guangdong Provincial Traditional Chinese Medicine Precision Medicine Big Data Engineering Technology Research Center Guangzhou510006 China Cloud-Based Computing Precision Medical Big Data Engineering Technology Research Center of Guangdong Universities Guangzhou510006 China

Matrix factorization stands as a crucial technology for deriving latent representations of data by uncovering underlying structures. Non-negative matrix factorization (NMF)-based multi-view clustering algorithms have demonstrated their competitive edge in this *** order to eliminate the non-negativity constraint, concept factorization (CF) based multi-view clustering methods have been widely used. However, concept factorization fails to preserve the local geometric structure local geometric structure and form a consensus matrix in a unified framework. In this paper, a multimanifold regularized concept factorization for multi-view clustering (MVMMCF) is proposed, which can preserve the local geometric structure of the feature and data manifold. MVMMCF uses kernelbased approaches to refine the latent data representation. Dual-graph regularization is incorporated to form the geometrical framework of the feature and data manifolds, and representation matrix is regularized toward a common consensus matrix. And an iterative alternating method is used to achieve the consensus representation matrix for multi-view data. Finally, extensive experiments are carried out on five *** result shows that our method outperform several popular non-negative matrix factorization and concept factorization based multi-view clustering algorithms. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Discovering All-chain Set with Direction and Graduality Characteristics over Streaming Time Series

Discovering All-chain Set with Direction and Graduality Char...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Shaopeng Wang Chunkai Feng Department of Software Engineering Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Inner Mongolia discipline inspection and supervision big data laboratory Inner Mongolia University Hohhot China

Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. The basic ideas of these two strategies are both incremental computing. The first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. The second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN.

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Exploiting the Medical data Storage Implementation and Privacy Protection with Consortium Blockchain and IPFS 3rd

Exploiting the Medical Data Storage Implementation and Priva...

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3rd International Conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Liu, Shaojie Wen, Bin Wang, Zexu School of Information Science and Technology Hainan Normal University Haikou China Cloud Computing and Big Data Research Center Hainan Normal University Haikou China Key Laboratory of Data Science and Intelligence Education of Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789811679926

Due to the limitations of technologies, the traditional electronic medical record system has the risk of data being tampered with and attacked, which makes personal privacy easy to be leaked. Combined with the consortium blockchain development platform – FISCO BCOS, a new electronic medical record storage model is proposed. The model controls the access of medical records through blockchain and smart contract, strengthens the privacy protection of patients, improves the convenience of medical data sharing, and provides reliable data support for medical research. Finally, the proposed electronic medical record storage model is fully realized, and its effectiveness and practicability are proved. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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M-A-R: A Dynamic Symbol Execution Detection Method for Smart Contract Reentry Vulnerability 3rd

M-A-R: A Dynamic Symbol Execution Detection Method for Smart...

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3rd International Conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Wang, Zexu Wen, Bin Luo, Ziqiang Liu, Shaojie School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China Key Laboratory of Data Science and Intelligence Education of Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789811679926

The original intention of smart contract design is to execute every transaction in the blockchain spontaneously, efficiently and fairly, meanwhile, smart contract plays an important role in the blockchain activities. With the development of blockchain, the vulnerability of smart contract becomes more and more obvious. The security vulnerability detection of smart contract is very important. This paper proposes M-A-R, a dynamic symbol execution method focusing on efficient detection for reentry vulnerability, realizes the security detection of the source code of smart contract, optimizes the design of its implementation method, then compares it with the existing related tools. The results show that M-A-R approach can detect the reentry vulnerability efficiently and has good universality and scalability. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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Energy Efficiency for HDFS 9

Energy Efficiency for HDFS

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9th IEEE International Conference on cloud computing in Emerging Markets, CCEM 2020

作者： Das, Abhishek Snv, Bhargav Sanketh Reddy, N. Phalachandra, H.L. Pes University Dept. of Computer Science and Engineering Bangalore India Center for Cloud Computing and Big Data Pes University Bangalore India

ISBN: (纸本)9781728195766

The rapidly increasing growth of big data is leading to increased usage of distributed computing and clusters as this can enable tasks to be split and processed in parallel resulting in higher computational efficiency. Environments like Apache Hadoop use Distributed file systems like the HDFS to support large clusters of typically commodity hardware to achieve the same. Commodity hardware present in HDFS consumes significant energy even when not in an active state, and considering our fragile ecosystem, reduction of the carbon footprint and conservation of energy in these environments is the focus of our work. We have evolved on a dynamic approach to Hadoop Distributed File Systems (HDFS) that provides an energy efficient paradigm for Hadoop clusters by segregating the servers in the cluster to two 'Zones', a 'Hot-Zone', and a 'Cold-Zone'. We use energy efficient storage devices (SSDs), to store data that is frequently used for the hot zone. The Cold-Zone would have lesser energy efficient devices (HDDs) and would be used to store data that is not frequently used. The Hot-Zone would always be in an active state to compute while the cold zone would have the HDDs to be in a low power consumption state. We have also enhanced the block placement policy for HDFS to support dynamic block transfer among the two zones. © 2020 IEEE.

关键词： Energy efficiency

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A Blockchain-Based Multi-cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on big data and cloud computing (Bdcloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： cloud computing data centers Costs Blockchains Behavioral sciences

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Prediction method of geographical and spatial distribution of traffic accidents based on traffic flow big data

Advances in Transportation Studies

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Advances in Transportation Studies 2023年第Special issue期2卷 113-124页

作者： Liu, Y. Zhang, Z.A. Shang, Z.L. Wang, Z. Cloud Computing and Big Data Institute of Henan University of Economics and Law Henan Zhengzhou450046 China The University of New South Wales SydneyNSW2032 Australia Engineering Training Center Zhengzhou University of Light Industry Zhengzhou450001 China College of Electrical Engineering and Information Engineering Lanzhou University of Technology Lanzhou730050 China

Accurate prediction of the geographical spatial distribution location of traffic accidents can provide drivers with richer traffic geographic information, thereby reducing the rate of traffic accidents. However, the existing methods for predicting the geographical spatial distribution location of traffic accidents have the problems of large error and long time. Therefore, this paper proposes a method for predicting the geographical spatial distribution location of traffic accidents based on the traffic flow gig data. First, collect traffic flow big data during the accident through highway detectors. Secondly, the collected data is processed for exception deletion, missing completion and geocode conversion. Finally, according to the geographical spatial distribution characteristics, the geographical spatial distribution of traffic accidents is predicted through Kalman filter. The experimental results show that this method predicts the geographical spatial distribution of traffic accidents based on actual test results, with good prediction effect and high prediction efficiency. © 2023, Aracne Editrice. All rights reserved.

关键词： Spatial distribution

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