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Design and Implementation of Log data Analysis Management System Based on Hadoop

Journal of Information Hiding and Privacy Protection 2020年第2期2卷 59-65页

作者： Dunhong Yao Yu Chen School of Computer Science and Engineering Huaihua UniversityHuaihua418000China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China School of Computer Science and Engineering Yulin Normal UniversityYulin537000China

With the rapid development of the Internet,many enterprises have launched their network *** users browse,search,and click the products of these platforms,most platforms will keep records of these network behaviors,these records are often heterogeneous,and it is called log *** effectively to analyze and manage these heterogeneous log data,so that enterprises can grasp the behavior characteristics of their platform users in time,to realize targeted recommendation of users,increase the sales volume of enterprises’products,and accelerate the development of ***,we follow the process of big data collection,storage,analysis,and visualization to design the system,then,we adopt HDFS storage technology,Yarn resource management technology,and gink load balancing technology to build a Hadoop cluster to process the log data,and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the ***,the obtained results are displayed visually,and a log data analysis system is successfully *** has been proved by practice that the system effectively realizes the collection,analysis and visualization of log data,and can accurately realize the recommendation of products by *** system is stable and effective.

关键词： Log data Hadoop data analysis data visualization

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Edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers

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Cleaner Engineering and Technology 2025年 27卷

作者： Ma, Shuaiyin Cao, Yichun Liu, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an 710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Department of Management and Engineering Linköping University Linköping SE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu 90570 Finland

Within the context of cleaner production, enhancing energy efficiency and sustainability has emerged as a central focus in supercomputing center development. To address the challenges in predicting energy consumption, this study proposes an architecture of edge computing-enabled energy efficiency prediction of immersion cooling system for supercomputing centers. This architecture combines time-series generative adversarial network (TimeGAN) for data augmentation with the neural basis expansion analysis for time series (N-BEATS) for prediction, providing a robust solution for accurate energy consumption prediction. TimeGAN enhances the training dataset by generating high-quality synthetic time-series data, effectively mitigating issues of data sparsity and imbalance. N-BEATS, with its modular architecture and strong adaptability to temporal data, ensures precise predictions by capturing both global trends and local variations in energy usage. Experimental results demonstrate the effectiveness of the proposed architecture, exhibiting superior performance across key metrics when compared to traditional models. Specifically, the RMSE of TimeGAN-N-BEATS reduced by more than 8 %, the MSE decreased by over 18 %, and the R2 reached 97.31 %, outperforming baseline models such as long short-term memory and gated recurrent unit, and their attention-enhanced variants. This study highlights the potential of integrating generative and predictive models to optimize energy efficiency in liquid cooling systems, offering valuable insights for sustainable supercomputing operations. © 2025 The Authors

关键词： Edge computing Liquid cooling system N-BEATS Supercomputing centers TimeGAN

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Joint multi-task cascade for instance segmentation

Joint multi-task cascade for instance segmentation

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作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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Research on the Assistant Decision-making of Gas Regulator Based on Case-based Reasoning

Research on the Assistant Decision-making of Gas Regulator B...

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第32届中国控制与决策会议

作者： Sijie Li Yahui Wang Yinxing Dong School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing North Hua chuang Vacuum Technology Co Ltd

In order to improve the efficiency of fault decision-making of gas surge tank, this paper proposes a casebased reasoning(CBR) for assistant decision-making of gas surge tank. On the basis of the real case of surge tank fault, the characteristic attributes of the case are extracted, and the case is represented by the frame representation method. Through analytic hierarchy process, local similarity calculation model and global similarity calculation model, the assistant decision-making method of gas surge tank based on case-based reasoning is established. Finally, the source case and target case are analyzed, and the results are proved. The application of this method in assistant decision-making of gas surge tank fault has practical significance.

关键词： gas surge tank assistant decision-making case-based reasoning similarity calculation analytic hierarchy process frame representation

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Protection of Large Sparse Network Through Optimal Cropped Depth-First Traversal Tree

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IEEE Transactions on Network Science and Engineering 2025年

作者： Wei, Wei Hu, Qiuyuan Zhang, Qinghui Li, Peng Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Zhengzhou450001 China Henan University of Technology Henan Grain Big Data Analysis and Application Engineering Research Center Zhengzhou450001 China Beijing Institute of Technology No. 5 Zhongguancun South Street Haidian District Beijing100086 China

The robustness of the network is its ability to withstand unexpected failures. There is an urgent need to enhance the robustness of sparse networks as they are prone to breakage. Among the various ways to enhance network robustness measured by the edge connectivity of the entire network, shielding candidate edges is a popular method when redundant edges are not affordable. However, existing tree-cutting favors dense graphs with exponential cropped-tree growth of search space, but the search space only scales linearly in sparse graphs. We explore sparse-graph limitations by extending cropping to maximum depth while maintaining sublinear overhead, leveraging ancestor-node constraints and cropping-chain theorems for optimal efficiency. Compared with the optimal algorithm, the results in representative network types show that the robustness measurement of small-scale graphs can be enhanced to 100%, and the ratio of acceleration can be greater than 105. In large-scale graphs with millions of nodes, robustness can be improved to over 99%, while the time can be as low as tens of seconds. © 2013 IEEE.

关键词： Trees (mathematics)

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How to fast verify collatz conjecture by automata 21

How to fast verify collatz conjecture by automata

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Ren, Wei Xiao, Ruiyang School of Computer Science China University of Geoseciences Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou China

ISBN: (纸本)9781728120584

To verify Collatz conjecture for a given integer, we need to justify whether or not the given starting number goes to 1 finally. Current method computes 3∗x+1 and x/2 one by one. In this paper, we propose an automata-based algorithm to compute the process in a batch with m steps - (3∗x+1)2 or x/2. We also propose reduced Collatz conjecture and we prove it is equivalent to Collazt conjecture. We also point out that original dynamics is the combination of reduced dynamics. (Original dynamics is a serial of occurred 3∗x+1 and x/2 computation in the process from a starting number to 1;Reduced dynamics is a serial of occurred 3∗x+1 and x/2 computation in the process from a starting number to the first transformed number that is less than the starting number.) We study the properties of reduced dynamics and discover that reduced dynamics for some starting numbers can be determined directly without conducting concrete 3∗x+1 or x/2 computations. We build a binary tree called Collatz tree and use it as a visual tool for automata design. We find that each partition of starting number residue class decides the further computation is whether 3∗x+1 or x/2. The proposed automata can output code segment with m+2 computations in terms of (3∗x+1)/2 or x/2 by taking as input x=4t+3, where t is in residue class i module 2m and m is a natural number. We finally propose a conjecture whose proof implies the proof of Collatz conjecture by using proposed Collatz tree. © 2019 IEEE.

关键词： Number theory

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Energy Consumption of IT System in Cloud data Center: Architecture, Factors and Prediction 16th

Energy Consumption of IT System in Cloud Data Center: Archit...

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16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Lin, Haowei Xu, Xiaolong Wang, Xinheng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China University of West London LondonW55RF United Kingdom

ISBN: (纸本)9783030307080

In recent years, as cloud data center has grown constantly in size and quantity, the energy consumption of cloud data center has increased dramatically. Therefore, it is of great significance to study the energy-saving issues of cloud data centers in depth. Therefore, this paper analyzes the architecture of energy consumption of IT system in cloud data centers and proposes a new framework for collecting energy consumption. Based on this framework, the factors affecting energy consumption are studied, and various parameters closely related to energy consumption are selected. Finally, the RBF neural network is used to model and predict the energy consumption of the cloud data centers, which is aim to prove the accuracy of the framework for collecting energy consumption and influencing factors. The experimental results show that these parameters under the framework for collecting energy consumption have better accuracy and adaptability to the prediction of energy consumption in cloud data centers than the previous model of energy consumption prediction. © 2019, IFIP International Federation for Information Processing.

关键词： Forecasting

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On Optimal Finite-length Binary Codes of Four Codewords for Binary Symmetric Channels

On Optimal Finite-length Binary Codes of Four Codewords for ...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Yanyan Dong Shenghao Yang School of Science and Engineering and the Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9784885523304

ISBN: (纸本)9781728128559

Finite-length binary codes of four codewords are studied for memoryless binary symmetric channels (BSCs) with the maximum likelihood decoding. For any block-length, best linear codes of four codewords have been explicitly characterized, but whether linear codes are better than nonlinear codes or not is unknown in general. In this paper, we show that for any block-length, there exists an optimal code of four codewords that is either linear or in a subset of nonlinear codes, called Class-I codes. Based on the analysis of Class-I codes, we derive sufficient conditions such that linear codes are optimal. For block-length less than or equal to 8, our analytical results show that linear codes are optimal. For block-length up to 300, numerical evaluations show that linear codes are optimal.

关键词： Sufficient conditions Binary codes Linear codes Maximum likelihood decoding Information theory

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A spatiotemporal attention-based ResC3D model for large-scale gesture recognition

A spatiotemporal attention-based ResC3D model for large-scal...

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作者： Li, Yunan Miao, Qiguang Qi, Xiangda Ma, Zhenxin Ouyang, Wanli School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xi’an China School of Electrical and Information Engineering The University of Sydney Sydney Australia

Abnormal gesture recognition has many applications in the fields of visual surveillance, crowd behavior analysis, and sensitive video content detection. However, the recognition of dynamic gestures with large-scale videos remains a challenging task due to the barriers of gesture-irrelevant factors like the variations in illumination, movement path, and background. In this paper, we propose a spatiotemporal attention-based ResC3D model for abnormal gesture recognition with large-scale videos. One key idea is to find a compact and effective representation of the gesture in both spatial and temporal contexts. To eliminate the influence of gesture-irrelevant factors, we first employ the enhancement techniques such as Retinex and hybrid median filer to improve the quality of RGB and depth inputs. Then, we design a spatiotemporal attention scheme to focus on the most valuable cues related to the moving parts for the gesture. Upon these representations, a ResC3D network, which leverages the advantages of both residual network and C3D model, is developed to extract features, together with a canonical correlation analysis-based fusion scheme for blending features from different modalities. The performance of our method is evaluated on the Chalearn IsoGD dataset. Experiments demonstrate the effectiveness of each module of our method and show the ultimate accuracy reaches 68.14%, which outperforms other state-of-the-art methods, including our basic work in 2017 Chalearn Looking at People Workshop of ICCV. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Gesture recognition

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Extraction of transitional relations in healthcare processes from Chinese medical text based on deep learning 2019

Extraction of transitional relations in healthcare processes...

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4th International Conference on Mathematics and Artificial Intelligence, ICMAI 2019

作者： Ren, Fangwen Yuan, Song Gao, Feng School of Computer Science and Technology Key Laboratory of Intelligent Information Processing and Real-time Industrial System in Hubei Province Institute of Big Data Science and Engineering Wuhan University of Science and Technology China

ISBN: (纸本)9781450362580

Knowledge extraction in healthcare has become a recent research hotspot due to the rapid development of AI technology as well as the urgent demand in the medicine research and practice. Traditional methods focus on extracting terms and relations using pattern matching and machine learning, but they depend on the construction of rules and the accuracy of feature extraction. In contrast, deep learning methods have a stronger ability to process data with high dimensions. Moreover, current study neglects the fact that the key knowledge in medicine is the process of diagnosing and treating patients, i.e., the relations that causes the status change in a healthcare workflow. This paper first identifies the relations relevant for medical processes, and adopts the method of relationship extraction based on Bidirection Gated Recurrent Unit (bigRU) model and attention mechanism (bigRU-ATT), in order to retrieve these relations from Chinese medical text. The experimental results show that regarding Chinese medical entity relationship extraction, we can achieve better accuracy rate and recall rate than using convolutional neural network(CNN). © 2019 Association for Computing Machinery.

关键词： Extraction

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