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Research on Distributed database Stability Testing Platform based on Chaos Engineering 22

Research on Distributed Database Stability Testing Platform ...

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wang, Chaolun Yang, Jiaxing Han, Xiaolu Ma, Jianrui Liu, Siyuan Ma, Pengwei China Acad Informat & Commun Technol Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

With the fast development of information technology, the databases used as the fundamental storage and computing component of information system have to deal with much more complicated scenarios with high frequency and concurrency. Distributed databases are becoming more and more common in data-intensive industries such as banking and telecommunications. The selection of distributed database products requires comprehensively consider the function, performance, security, ease of use and stability of the product. The stability of distributed database is known to be difficult in testing and product selection. The method widely used in database testing is TPC-DS (transaction processing performance council-decision support) benchmark, which is only suitable for functional and performance testing. To deal with this drawback, a distributed database stability testing platform based on chaos engineering methodology is developed Through the perturbation injection by using the stability testing platform, the performance fluctuation of the database under pressure condition can be observed, and the stability of the database can then be evaluated

关键词： database distributed database database testing database product selection big data chaos engineering

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Big data Classification Model and Algorithm Based on Double Quantum Particle Swarm Optimization

Big Data Classification Model and Algorithm Based on Double ...

引用

2023 IEEE international conference on Control, Electronics and Computer Technology, ICCECT 2023

作者： Jingbo, Duan Guangdong Business and Technology University Guangdong526060 China Philippine Christian University Center for International Education Manila1004 Philippines

ISBN: (纸本)9798350399028

For the traditional big data classification models and algorithms that have the drawback of long processing time, the use of intelligent algorithms to model identification of multivariable systems has become a mainstream research direction. Discussion This paper proposes an efficient solution for data-parallel optimized computing. The problems in the identification of multi-variable systems that integrate historical big data and intelligent algorithms are analyzed, and the impact of input variables on output is quantified. Build a big data classification model, mine valid data points, arrange data combination methods, divide data local connection methods, obtain local node micro-cluster data, calculate redundant data and invalid data increment values in node data groups, and reconstruct central node sample algorithm, adjust the integrated data classification strategy, optimize and update the data integration classification method. Design the experiment, simulate the experimental environment, and verify that the proposed model calculation method can shorten the data processing time in practical application, which has practical research value. Through the design of the overall process of the algorithm, data classification to form a data set, and then describe the steps of the algorithm, and finally verify the feasibility of the algorithm through simulation experiments, which can not only greatly reduce the communication cost between network nodes, but also greatly improve the global mining accuracy and improve the query efficiency in distributed data, It has good utilization value in the analysis of massive data information. © 2023 IEEE.

关键词： Big data

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Analysis Model of Efficiency and Accuracy in Big data Based on Clustering Algorithm K-means

Analysis Model of Efficiency and Accuracy in Big Data Based ...

引用

2023 international conference on Internet of Things, Robotics and Distributed computing, ICIRDC 2023

作者： Lian, Yaohao Wang, Peng Guo, Gaiyan Hebei University of Economics and Business Hebei Shijiazhuang050000 China

ISBN: (纸本)9798350375275

With the continuous growth of big data, traditional data processing and analysis methods have faced great challenges. The K-means algorithm, as a classic representative of clustering technology, is of great significance for processing these large-scale datasets. This article delves into the efficiency and accuracy of K-means based clustering algorithms in big data environments. Firstly, we introduced the core principles and mathematical foundations of the K-means algorithm. Then, the main challenges of the algorithm in the big data environment were discussed in detail, such as computational complexity, randomness of initial center point selection, and convergence speed. To address these challenges, this article proposes a series of optimization strategies and verifies their effectiveness through experimental evaluation. The research results show that the improved algorithm has relatively stable final results and higher accuracy than traditional methods, with accuracy rates of 86.21% and 73.09%, respectively. This confirms that the improved algorithm is effective, with a more stable level of results and better results. These studies provide strong theoretical support and practical guidance for the further application of K-means in big data analysis. © 2023 IEEE.

关键词： Efficiency

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Decoding Climatic Variability: A data-Driven Approach to Assess Drought Impacts on Plant Communities in Hulunbuir

Decoding Climatic Variability: A Data-Driven Approach to Ass...

引用

algorithms, computing and data processing (acdp), international conference on

作者： Yu Liu Zhi Liang Shengming Liu Shuhang Cheng Zhangcheer Wang Department of Information Engineering Wuhan Institute of City Wuhan China

This research delves into the resilience of plant communities, with a focal emphasis on their adaptation mechanisms towards drought, with an overarching objective to broaden our comprehension of ecosystem dynamics and bolster environmental sustainability. We executed multiple regression analyses encompassing variables such as temperature, precipitation, and species richness within the Hulun-beier Baikal grassland situated in Inner Mongolia. The Standardized Precipitation Evapotranspiration Index (SPEI) was incorporated as a salient indicator of drought conditions. In parallel, a differential equation model was utilized to approximate aboveground biomass, coupled with the application of a particle swarm optimization algorithm aimed at accurately determining maximum biomass and optimal species count. A sensitivity anal-ysis further elucidated a linear correlation between monthly SPEI values and species numbers within the studied region. Key discoveries encompass: 1. Extreme climatic perturbations such as droughts or heavy rainfalls significantly undermine the sta-bility of plant communities, with particularly pro-nounced effects on those already encumbered by additional stressors such as pollution or habitat loss. 2. Rampant intraspecific competition within plant communities precipitates a decline in their numbers, a process that is further exacerbated by prevailing environmental stressors. 3. For a cogent prediction of the impacts of future drought events on ecosystems, it is paramount to integrate comprehensive models that account for an array of factors such as soil quality, nutrient cycling, biological interactions, and anthropogenic activities. This research presents critical insights pertinent to the conservation of plant biodiversity, the promotion of environmental sustainability, and the augmentation of our understanding of natural systems.

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Towards Scalable Process Mining Pipelines

Towards Scalable Process Mining Pipelines

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Mohamed, Belal Elhelw, Mohamed Awad, Ahmed Nile University Giza Egypt The British University in Dubai Dubai United Arab Emirates Cairo University Giza Egypt

ISBN: (纸本)9798350304602

Over the past two decades, process mining has proven to be a valuable approach to gain insights into organizations' performance. The major sub-fields of discovery, conformance, and improvement have witnessed substantial development. Contributions have covered the spectrum of better algorithms, richer comparison metrics, and movement towards online analysis for process data. Mostly, these contributions were addressing process mining guidelines from the process mining manifesto. In this paper, we address the sixth guideline in the process mining manifesto. That is, process mining should be a continuous process. For this, we propose a pipelining approach that is: configurable, scalable, modular, and automated. We realize our proposal using Dask and evaluate it with different architectures, process discovery, and evaluation metrics. © 2023 IEEE.

关键词： data mining

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An Efficient Fault Tolerance Strategy for Multi-task MapReduce Models Using Coded Distributed computing 1

引用

23rd international conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Xie, Zaipeng Zhang, Jianan Zhang, Yida Xu, Chenghong Chen, Peng Qu, Zhihao Song, WenZhan Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing Peoples R China Hohai Univ Coll Comp & Informat Nanjing Peoples R China Univ Georgia Ctr Cyber Phys Syst Athens GA 30602 USA

ISBN: (数字)9789819708628

ISBN: (纸本)9789819708611;9789819708628

MapReduce is a programming framework designed for processing and analyzing large volumes of data in a distributed computing environment. Despite its capabilities, it faces challenges due to silent data corruption during task execution, which can yield inaccurate results. Ensuring fault tolerance in the MapReduce framework while minimizing communication overhead presents considerable challenges. This study presents CDCFT (Coded Distributed computing Fault Tolerance), a novel approach to fault tolerance within the MapReduce paradigm, combining the strengths of TMR (Triple Modular Redundancy) and CDC (Coded Distributed computing). By leveraging task-level TMR and voting mechanisms, CDCFT robustly defends against silent data corruption. To further optimize, CDCFT employs intra-group broadcasts for relaying intermediate messages and has a finely-tuned node grouping combined with a strategic data and task allocation procedure. Through rigorous theoretical analysis, we establish that CDCFT's communication overhead during the Shuffle Stage is notably less than traditional CDC methods that rely on triple modular redundancy. Experimental results showcase the efficacy of CDCFT, signifying a substantial reduction in the overall communication overhead and execution time compared to the conventional fault-tolerant methods.

关键词： MapReduce framework Silent data corruption Fault tolerance Coding distributed computing Communication load

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A High Capacity Combined Carrier Steganography Without Embedding Image

A High Capacity Combined Carrier Steganography Without Embed...

引用

作者： Zhang, Yangwen Chen, Yuling Dou, Hui Luo, Yun Tan, Chaoyue Xiong, Maolin College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350304602

The technique of image steganography is the embedding of secret information into the carrier image by means of algorithms that are widely used in the field of information transmission, but steganalysis tools built with traditional steganographic algorithms can easily identify them. The technique of steganography without embedding (SWE) is the mapping of the secret information onto the noise and the generation of an image by mapping the noise of the secret information. This SWE technique is difficult to find without directly using secret information. But the capacity of secret information is less and the extraction accuracy is low when the SWE is currently hidden. Therefore, a steganography without embedding technique for carrier secret information is proposed in this paper based on the above problems. Initially, the secret information is mapped into the carrier noise, which can increase the secret information capacity of steganography and improve the accuracy of extracting information. Finally, the effectiveness and generality of the design method will be verified by experiments. © 2023 IEEE.

关键词： Embeddings

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Classification Learning Model of Environmental Protection data Based on Intelligent Optimization Algorithm 2

Classification Learning Model of Environmental Protection Da...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Zhao, Yingjie Hebei Baoding University Hebei Province Baoding City China

ISBN: (纸本)9798350347456

Environmental issues are becoming increasingly prominent, and people are paying more and more attention to environmental protection. Intelligent optimization algorithms have good global search capabilities, high accuracy, and real-time performance. Therefore, with the help of intelligent optimization algorithms, this paper established a classification learning model for environmental protection data, and analyzed the data from aspects such as water, solid waste, ecological governance, and ESG. The accuracy of the data obtained by the intelligent algorithm in the analysis results is less than 82%. However, it can still improve the processing capacity of relevant environmental data. This article mainly uses experimental comparison and algorithm analysis methods to analyze the results of environmental protection data classification learning. At the same time, the accuracy and stability of the entire algorithm were tested, and the accuracy of the average K, LWF, and IAE during the incremental learning process of MNIST data was compared. Experimental data show that the data accuracy rate obtained by the improved intelligent optimization algorithm exceeds 85%. Therefore, in data mining, existing environmental protection data can be used for statistical analysis and prediction of these classification objects. © 2023 IEEE.

关键词： Environmental protection

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Evaluation of Machine Learning algorithms for Text Classification Tasks 3

Evaluation of Machine Learning Algorithms for Text Classific...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Aravindan, M.K. Raajini, X. Mercilin Ingle, Yashwant S. Jangid, Pradeep Kumar Katyal, Ashu Naval, Preeti Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Assessing machine-learning algorithms for texttype responsibilities is critical to advancing the world of herbal language processing (NLP). As using applications that use NLP grows, so does the want to pick the maximum accurate algorithms out of a pool of to-be-had solutions. This paper reviews the current United States of America research concerning assessing system reading algorithms for textual content-type obligations. Numerous evaluation metrics, consisting of accuracy, precision, consider, and f-degree, are discussed, in addition to the strategies used to examine algorithms and determine the foremost solutions. The diverse challenges confronted in this venture are highlighted, giving readers an outline of the challenges related to assessing the tool and getting to know algorithms for text-kind responsibilities. © 2023 IEEE.

关键词： Natural language processing systems

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Based on the Application of Machine Learning algorithms in Business data Analysis 3

Based on the Application of Machine Learning Algorithms in B...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Yiwen The University of Auckland Auckland Cbd Auckland1010 New Zealand

ISBN: (纸本)9798350319125

With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machine learning algorithm has been widely used in the field of business data analysis. This paper will introduce the application of machine learning algorithm in business data analysis, including classification, clustering, prediction and association rule mining. © 2023 IEEE.

关键词： artificial intelligence Business data analysis commercial market machine learning algorithms

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