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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Research on 3D Visualization of Geographic Information data in Big data Environment

Research on 3D Visualization of Geographic Information Data ...

引用

2023 international conference on Applied Physics and computing, ICAPC 2023

作者： Zhang, Jiantao Lai, Weiping Huang, Yanshan Wu, Jianbin Lin, Xiaojun State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fujian Fuzhou350000 China

ISBN: (纸本)9798350359244

This paper introduces a system framework for rapid construction and rendering of 3D virtual environment using 2D GIS data. A large-scale GIS data 3D visualization system is designed and implemented. ArcSDE and SQL Server are used to store data. After extraction, processing and registration of multi-band images, the images are stored in the database in Shape form. SURF feature points were extracted and images were matched. DEM is combined with preprocessed multi-band remote sensing images. It was 3D visualized by AcrScene software. The system can well realize correlation analysis, two-dimensional and three-dimensional attribute correlation query, and big data rendering. After testing, this design has good smoothness, fast response and good picture quality. It has nice interactive features. The system can automatically load and splice terrain data. © 2023 IEEE.

关键词： Big data

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Research on an Intelligent Structure for Push Control of Multi-Center Cloud data Pool 5

Research on an Intelligent Structure for Push Control of Mul...

引用

5th international conference on Intelligent Control, Measurement and Signal processing, ICMSP 2023

作者： Ying, Huang Armed Police Engineering University Department of Information Engineering Xi'an710086 China

ISBN: (纸本)9798350336030

The intelligent control of complex system must first solve the problem of data calculation, application and real-time support, which is not only the difficulty and focus of control system design, but also the key to artificial intelligence and intelligent manufacturing. It is found that the essence of control system are both processing data and controlling the direction of data flow. So, the connotation of intelligence lies in an open data processing system to increase the control of data flow, thus forming the basis of real-time data processing. Therefore, this paper proposes an intelligent push control structure of multi center cloud data pool. The structure takes multi-layer cloud data pool as the center, vertical cloud data pool processes global parameters, and uses block chain technology to keep data synchronization. Horizontal cloud data pool collects status data by different function, and parameters required by control loop are pushed by cloud data pool. So, cloud computing ensures the separation of data computing and control, and multi-layer cloud data pool can form sensor networks. The structure realizes the intelligent control target of complex large-scale system that can not only implement big data calculation, but also control the flow direction of data. © 2023 IEEE.

关键词： Lakes

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Intelligent computing in Communication Networks: Challenges, Solutions, and Research Prospects

Intelligent Computing in Communication Networks: Challenges,...

引用

2023 IEEE international conference on computing, ICOCO 2023

作者： Fakhrul Safitra, Muhammad Prastika Putri, Deyana Lubis, Muharman Fakhrurroja, Hanif Saidi Lubis, Fahdi Satria, Villy School of Industrial and System Engineering Telkom University Information System Department Bandung Indonesia School of Industrial and System Engineering Telkom University Industrial Engineering Department Bandung Indonesia School of Applied Science Telkom University Application Software Engineering Department Bandung Indonesia

ISBN: (纸本)9798350302684

Communication networks can be transformed through the implementation of computing, which can improve their overall performance, security, and energy efficiency. Nevertheless, several obstacles must be overcome before computing can be fully integrated into communication networks. This paper examines the challenges and research opportunities in the field of computing in communication networks. To take full advantage of computing, new algorithms and techniques must be developed, and machine learning must be explored. Big data analytics and the integration of edge and fog computing with computing techniques are also promising areas for research. The challenges that must be addressed include ethical and privacy concerns, energy consumption, integration with 5G networks, IoT, and cloud computing, and the development of resilient computing systems that can identify and respond to cyber threats. Overcoming these challenges will ensure that computing remains an essential tool for designing and optimizing communication networks. © 2023 IEEE.

关键词： data Analytics

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Light pollution measurement and strategy modelling based on multidimensional data

Light pollution measurement and strategy modelling based on ...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Cheng, Jiting Peng, Changzhi He, Yao School of Computing National University of Singapore Singapore119077 Singapore College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China School of Microelectronics Southeast University Jiangsu Wuxi214000 China

ISBN: (纸本)9781510671881

Artificial light sources have intensified light pollution, diminishing visible stars and threatening biodiversity and ecological balance. It is crucial to establish an evaluation index of light pollution and put forward effective treatment strategies. This study establishes an evaluation model for light pollution, considering extensive data and principal component analysis, and the clustering algorithm is used for analysis. Then we came up with three solutions to the light pollution issue and spoke about how each might affect its prevention and management. In addition, we applied these three strategies in two different regions and came up with the most effective intervention strategies. Finally, two typical regions are selected to illustrate the pathways through which different strategies affect the final results. © 2023 SPIE. All rights reserved.

关键词： Biodiversity

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IoT-Based Power Metering Automation for Improving data Integrity and Reliability in Power Marketing Meters

IoT-Based Power Metering Automation for Improving data Integ...

引用

2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Samreen, Sumaiya Chanti, Yerrolla Mounika, P. Mandava, Manjusha Kruthika, C.G. Malla Reddy Engineering College for Women It Department Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

The goal of this paper is to provide an internet of things (IoT)-based approach for automated electricity metering. When measuring power using standard methods, inaccuracies and erroneous readings are possible, putting the data's integrity and authenticity at risk. The suggested method employs sensors linked to the internet of things (IoT) and machine learning algorithms to detect abnormalities and inconsistencies in real-time energy use statistics. This was evident during the testing phase. The proposed method offers several benefits over the present standard of power metering systems in use. The testing findings demonstrated that the proposed system is more reliable and effective in terms of 0.957 success rate, 0.943 accuracy, 0.967 recall, 0.956 F1 score and 0.982 AUC. © 2023 IEEE.

关键词： Anomaly detection Cloud-based data storage and analysis platform Internet of Things (IoT) Machine learning algorithm Power metering Real-time data analysis User interface

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Profiling Hyperscale Big data processing 23

Profiling Hyperscale Big Data Processing

引用

50th Annual international Symposium on Computer Architecture (ISCA)

作者： Gonzalez, Abraham Kolli, Aasheesh Khan, Samira Liu, Sihang Dadu, Vidushi Karandikar, Sagar Chang, Jichuan Asanovic, Krste Ranganathan, Parthasarathy Univ Calif Berkeley Berkeley CA 94720 USA Google Mountain View CA 94043 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798400700958

computing demand continues to grow exponentially, largely driven by "big data" processing on hyperscale data stores. At the same time, the slowdown in Moore's law is leading the industry to embrace custom computing in large-scale systems. Taken together, these trends motivate the need to characterize live production traffic on these large data processing platforms and understand the opportunity of acceleration at scale. This paper addresses this key need. We characterize three important production distributed database and data analytics platforms at Google to identify key hardware acceleration opportunities and perform a comprehensive limits study to understand the trade-offs among various hardware acceleration strategies. We observe that hyperscale data processing platforms spend significant time on distributed storage and other remote work across distributedworkers. Therefore, optimizing storage and remotework in addition to compute acceleration is critical for these platforms. We present a detailed breakdown of the compute-intensive functions in these platforms and identify dominant key data operations related to datacenter and systems taxes. We observe that no single accelerator can provide a significant benefit but collectively, a sea of accelerators, can accelerate many of these smaller platform-specific functions. We demonstrate the potential gains of the sea of accelerators proposal in a limits study and analytical model. We perform a comprehensive study to understand the trade-offs between accelerator location (on-chip/off-chip) and invocation model (synchronous/asynchronous). We propose and evaluate a chained accelerator execution model where identified compute-intensive functions are accelerated and pipelined to avoid invocation from the core, achieving a 3x improvement over the baseline system while nearly matching identical performance to an ideal fully asynchronous execution model.

关键词： data analytics databases hyperscale computing cloud computing warehouse-scale computing profiling accelerators accelerator-chaining

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A Comparative Analysis of Advanced Machine Learning Techniques in Health Care Domain

A Comparative Analysis of Advanced Machine Learning Techniqu...

引用

2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sivaraman, M. Sumitha, J. Jayashree, J. Aswathinath, T. Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Department of Computer Science Tamilnadu India Dr. SNS Rajalakshmi College of Arts and Science Coimbatore Department of Commerce Tamilnadu India

ISBN: (纸本)9798350319200

Diabetes Mellitus, is a widespread endocrine illness that affects the entire world today. This is a necessity for an effective model that can predict diabetes and its variants with greater accuracy as early as possible. For increasing the accurateness of prediction with best effectiveness innovative ML Model is proposed. This survey aims to learn about and analyze the operation and performance of different Machine Learning techniques. This survey examines the possible applications of machine learning in the healthcare field. It works with new technologies such as social media platforms for predicting human predilections;in the field of medicine for predicting disease and so on. There are many techniques in data mining each model with its own level of accuracy. However, there is no model which can be said to be flawless as the performance of a model depends greatly on the datasets used. Here in these studies some of the classification algorithms have been deeply discussed. The main aim of this study is to deeply understand and analyze the working of some of the ML algorithms. © 2023 IEEE.

关键词： data mining techniques Diabetes disease Health Domain Machine learning model

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Scaling New Heights: Transformative Cross-GPU Sampling for Training Billion-Edge Graphs 24

Scaling New Heights: Transformative Cross-GPU Sampling for T...

引用

2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Xia, Yaqi Yang, Donglin Zhou, Xiaobo Cheng, Dazhao Wuhan Univ Sch Comp Sci Wuhan Peoples R China NVIDIA Santa Clara CA USA Univ Macau IOTSC Taipa Macao Peoples R China

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Efficient training of Graph Neural Networks (GNNs) on billion-edge graphs poses significant challenges due to memory constraints and data transfer bottlenecks, particularly affecting GPU-based sampling. Traditional methods either face severe CPU-GPU data transfer bottlenecks or encounter excessive data shuffling and synchronization overheads in multi-GPU setups. To overcome these challenges in GNN training on large-scale graphs, we introduce HyDRA, a pioneering framework that elevates mini-batch, sampling-based training. HyDRA innovates in multi-GPU memory sharing and multi-node feature retrieval, transforming cross-GPU sampling by seamlessly integrating sampling and data transfer into a single kernel operation. It develops a hybrid pointer-driven data placement technique to enhance neighbor retrieval efficiency, designs a targeted replication strategy for high-degree vertices to reduce communication overhead, and leverages dynamic cross-batch data orchestration with pipelining to minimize redundant data transfers. Evaluated on systems equipped with up to 64 A100 GPUs, HyDRA significantly outperforms current leading methods, achieving 1.4x to 5.3x faster training speeds compared to DSP and DGL-UVA and demonstrating up to a 42x improvement in multi-GPU scalability. HyDRA sets a new benchmark for high-performance GNN training at large scales.

关键词： Training Protocols Scalability Memory management data transfer Graph neural networks Topology Synchronization Kernel Pipeline processing

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Evaluation of data Enrichment Methods for Distributed Stream processing Systems 11

Evaluation of Data Enrichment Methods for Distributed Stream...

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11th IEEE international conference on Cloud Engineering (IC2E)

作者： Scheinert, Dominik Casares, Fabian Geldenhuys, Morgan K. Styp-Rekowski, Kevin Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798350343946

Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Things, business intelligence, and telecommunications, real-time processing of unbounded data streams has become a necessity. DSP systems provide a solution to this challenge, offering high horizontal scalability, fault-tolerant execution, and the ability to process data streams from multiple sources in a single DSP job. Often enough though, data streams need to be enriched with extra information for correct processing, which introduces additional dependencies and potential bottlenecks. In this paper, we present an in-depth evaluation of data enrichment methods for DSP systems and identify the different use cases for stream processing in modern systems. Using a representative DSP system and conducting the evaluation in a realistic cloud environment, we found that outsourcing enrichment data to the DSP system can improve performance for specific use cases. However, this increased resource consumption highlights the need for stream processing solutions specifically designed for the performance-intensive workloads of cloud-based applications.

关键词： Distributed Stream processing data Enrichment data Analysis Resource Management Cloud computing

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A Review of DNA Cryptography: From a data Protection Perspective 16

A Review of DNA Cryptography: From a Data Protection Perspec...

引用

16th international conference on Security of Information and Networks, SIN 2023

作者： Parmar, Parth Gadhiya, Jekil Vats, Satvik Verma, Deepak Kumar Vaghela, Krunal Computer Engineering Marwadi University Rajkot India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350343212

The rate of development in today's IT industry is unprecedented. Information is being stored and transformed at a faster and faster rate. Therefore, the importance of protecting sensitive data is growing. Everyone is concerned about preventing hackers from gaining access to their personal data. Several algorithms from the fields of classical cryptography and steganography can be used to conceal data. DNA cryptography is an emerging field that aims to protect information kept in DNA. DNA cryptography takes use of DNA's innate computing power at the molecular level. In this paper, we are exploring diverse cryptographic algorithms and methods that are used to secure the stored data in DNA. The paper represents the critical review that includes merits and demerits of the diverse cryptographic algorithms for securing the data in DNA. © 2023 IEEE.

关键词： DNA

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