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parallel computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

引用

1st international conference on Intelligent Systems and Computational networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices 17

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

引用

17th international conference on COMmunication Systems and networks, COMSNETS 2025

作者： Mathkar, Saumya Aiyer, Shreya Bapat, Yashovardhan Pinki, Pinki Paul, Arnab K. Naik, Vinayak BITS Pilani Department of CSIS Goa India

ISBN: (纸本)9798331531195

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size. © 2025 IEEE.

关键词： distributed computing Edge computing Optimization

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

引用

20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628636

The proceedings contain 76 papers. The special focus in this conference is on Network and parallel computing. The topics include: AsymFB: Accelerating LLM Training Through Asymmetric Model parallelism;DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies;Diagnosability of the Lexicographic Product of Paths and Complete Bipartite Graphs Under PMC Model;DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating;Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster;HP-CSF: An GPU Optimization Method for CP Decomposition of Incomplete Tensors;JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection;optimizing Vo-Viso: A Modified Methodology to parallel computing with Isolating Data in Memristor Arrays;parallel Computation of the Combination of Two Point Operations in Conic Curves Cryptosystem over GF(2n) Using Tile Self-assembly;parallel Construction of Independent Spanning Trees on 3-ary n-cube networks;SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling;swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor;VConv: Autotiling Convolution Algorithm Based on MLIR for Multi-core Vector accelerators;ACH-Code: An Efficient Erasure Code to Reduce Average Repair Cost in Cloud Storage Systems of Multiple Availability Zones;CMS: A Computility Resource Status Management and Storage Framework;fast Memory Disaggregation with SwiftSwap;HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in parallel computing Programs;lightFinder: Finding Persistent Items with Small Memory;miDedup: A Restore-Friendly Deduplication Method on Docker Image Storage Systems;SPLR: A Selective Packet Loss Recovery for Improved RDMA Performance;a Cluster-Based Platoon Formation Scheme for Realistic Automated Vehicle Platooning;AnaNET: Anatomical Network fo

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628292

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A distributed Technique of Optimization Problems Solving Based on Efficient Workload Assignment 27th

A Distributed Technique of Optimization Problems Solving Ba...

引用

27th international conference on distributed Computer and Communication networks, DCCN 2024

作者： Klimenko, Anna Institute of IT and Security Technologies Russian State University for Humanities Moscow Russia

ISBN: (纸本)9783031808524

Currently, the efficiency improvement of complex technical systems is an urgent scientific task, which in numerous cases is formalized via optimization problem and solved by some well-known optimization methods. Metaheuristics are used intensively in this field due to their possibility to generate acceptable solutions in restricted time periods. However, it is known that with the decrease of performing time the solution accuracy of metaheuristics degrades, as well as algorithm’s convergence depends on its exploitation/exploration features and preliminary set parameters. parallel metaheuristics implementations are used to improve algorithms performance. In this paper a distributed technique of computationally-hard optimization problems solving based on efficient workload assignment is presented and described. The novelty of the technique proposed is that the additional procedure of metaheuristics instances forming and distribution is added, which creates metaheuristic instances of a various computational complexity and assigns them to the most suited computing resources to perform parallel independent runs within the operation time restriction. The workload generated by metaheuristics blocks is distributed efficiently by means of metaheuristics portfolio usage. The latter makes it possible to improve the metaheuristics instances distribution and computational complexities forming so as to put the largest metaheuristic block to the node with the highest performance with the iterative improvement of the distribution, considering the full set of computing resources constraints and criteria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Convolutional Neural networks Parameter Training for SCM Algorithm Based on Hausdorff Difference 25th

Convolutional Neural Networks Parameter Training for SCM Al...

引用

25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Jian, Jing Gao, Zhe Xiao, Jianyu Zhang, Haibin Beijing University of Technology Beijing100022 China Liaoning University Shenyang110036 China

ISBN: (纸本)9789819642069

In this paper, a convolutional neural network parameter training method based on Hausdorff difference is proposed to solve the problems of gradient vanishing and local optimum in the momentum algorithm. A momentum algorithm is proposed based on Hausdorff difference, introduced similar to the conception of Hausdorff derivative. Furthermore, the proposed algorithm is improved by using two new methods for adaptive nonlinear adjustment of the order. We analyse the influence of the order on the training results of the network parameters and verify the effectiveness of the proposed methods in improving the recognition accuracy and convergence speed of convolutional neural networks by the fashion-MNIST dataset and the CIFAR-10 dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615476

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A Neural Network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615278

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