检索结果-内蒙古大学图书馆

High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International conference for

作者： Naw Safrin Sattar Hao Lu Feiyi Wang Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.

关键词： Degradation Runtime High performance computing conferences Graphics processing units Benchmark testing data structures Supercomputers Partitioning algorithms Performance analysis

来源：评论

学校读者我要写书评

暂无评论

efficient Grid Map data structures for Autonomous Driving in Large-Scale Environments

Efficient Grid Map Data Structures for Autonomous Driving in...

引用

IEEE Intelligent Transportation Systems conference (ITSC)

作者： Wellhausen, Constantin Clemens, Joachim Schill, Kerstin Univ Bremen Cognit Neuroinformat Grp Bremen Germany

ISBN: (纸本)9781728191423

In this paper, we investigate data structures for grid mapping in autonomous driving. While the traversed environment can be virtually infinitely large, one is mainly interested in the immediate surroundings of the vehicle. Thus, a world-fixed, square grid map centered roughly at the vehicle is used. For an efficient data handling, this grid map is subdivided into multiple submaps, which can be added and removed in order to move the currently considered window with the vehicle. We evaluate different choices for the underlying data structures (array, hashmap, quadtree) as well as different submap sizes on both a theoretical and practical level: We provide a complexity analysis and a discussion on possible overheads. Furthermore, we present empirical results regarding memory consumption and runtime of common operations (mapping, scan matching, map retrieval) on real-world datasets of different scenarios (suburb, rural area, highway). Finally, we give recommendations depending on the actual requirements of the used algorithm.

关键词： Road transportation Runtime Heuristic algorithms Memory management Object detection Complexity theory Arrays

来源：评论

学校读者我要写书评

暂无评论

Fast Rotation Kernel Density Estimation over data streams 21

Fast Rotation Kernel Density Estimation over Data Streams

引用

27th ACM SIGKDD International conference on Knowledge Discovery and data Mining (KDD)

作者： Lei, Runze Wang, Pinghui Li, Rundong Jia, Peng Zhao, Junzhou Guan, Xiaohong Deng, Chao Xi An Jiao Tong Univ MOE KLINNS Lab Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Shenzhen Res Inst Xian Shaanxi Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ NLIST Lab Beijing Peoples R China China Mobile Res Inst Beijing Peoples R China

ISBN: (纸本)9781450383325

Kernel density estimation method is a powerful tool and is widely used in many important real-world applications such as anomaly detection and statistical learning. Unfortunately, current kernel methods suffer from high computational or space costs when dealing with large-scale, high-dimensional datasets, especially when the datasets of interest are given in a stream fashion. Although there are sketch methods designed for kernel density estimation over data streams, they still suffer from high computational costs. To address this problem, in this paper, we propose a novel Rotation Kernel. The Rotation Kernel is based on a Rotation Hash method and is much faster to compute. To achieve memory-efficient kernel density estimation over data streams, we design a method, RKD-Sketch, which compresses high dimensional data streams into a small array of integer counters. We conduct extensive experiments on both synthetic and real-world datasets, and experimental results demonstrate that our RKD-Sketch saves up to 216 times computational resources and up to 104 times space resources than state-of-the-arts. Furthermore, we apply our Rotation Kernel in active learning. Results show that our method achieves up to 256 times speedup and saves up to 13 times space to achieve the same accuracy as the baseline methods.

关键词： Kernel density estimation Streaming algorithms

来源：评论

学校读者我要写书评

暂无评论

Bit-Parallel (Compressed) Wavelet Tree Construction

Bit-Parallel (Compressed) Wavelet Tree Construction

引用

data Compression conference (DCC)

作者： Patrick Dinklage Johannes Fischer Florian Kurpicz Jan-Philipp Tarnowski TU Dortmund University Otto-Hahn-Str. 14 Dortmund Germany Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe Germany

The wavelet tree is a data structure that indexes a text over an integer alphabet for efficient rank and select queries. Using the Huffman encoding, it can be stored in zero-order entropycompressed space. We present a highly engineered open source implementation of an efficient sequential construction algorithm that makes use of bit parallelism via vector instructions. On hardware featuring ultrawide registers of up to 512 bits, it outperforms the currently fastest known practical sequential construction algorithms by a factor of up to 2.5.

关键词： data compression Parallel processing data structures Hardware Encoding Registers Indexes

来源：评论

学校读者我要写书评

暂无评论

The Role of IoT and Machine Learning in Automating space Docking: Challenges, Advancements, and Future Prospects

The Role of IoT and Machine Learning in Automating Space Doc...

引用

Machine Learning and Autonomous Systems (ICMLAS), International conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and Machine Learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with spaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made.

关键词： space vehicles Mars space missions Reinforcement learning Prediction algorithms Real-time systems Sensors Internet of Things Next generation networking Monitoring

来源：评论

学校读者我要写书评

暂无评论

A Metaheuristic Optimization Model Based on Corporate Hierarchical Dynamics for efficient and Scalable Feature Selection in High-Dimensional data

A Metaheuristic Optimization Model Based on Corporate Hierar...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： Bandi Rambabu S. Anupkant M. Archana VNVLS Swathi Satyanarayana Nimmala A Mallareddy CVR College of Engineering Hyderabad Telangana India Vjjay Rural Engineering College Panimalar Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Feature selection is a critical step in machine learning and data analysis, aimed at identifying the most relevant features while reducing dimensionality and computational overhead. This paper introduces a novel Heap-Based Optimization Algorithm (HBO) for feature selection, inspired by the hierarchical structure and priority-driven organization of heap data structures. The proposed algorithm strategically explores and exploits the feature space by prioritizing features based on a fitness function that considers relevance, redundancy, and contribution to model accuracy. HBO employs a heap-based approach to iteratively refine the feature subset, ensuring convergence toward optimal solutions while maintaining computational efficiency. Experimental evaluations on diverse datasets demonstrate that HBO outperforms traditional and state-of-the-art feature selection techniques, achieving higher accuracy, reduced feature subsets, and improved model interpretability. The results underline HBO's effectiveness in handling high-dimensional data, reducing redundancy, and enhancing model performance.

关键词： Accuracy Machine learning algorithms High dimensional data Computational modeling Optimization models Metaheuristics Termination of employment Organizations Feature extraction Convergence

来源：评论

学校读者我要写书评

暂无评论

efficient Complete Event Trend Detection over High-Velocity streams 21

Efficient Complete Event Trend Detection over High-Velocity ...

引用

50th International conference on Parallel Processing (ICPP)

作者： Mei, Huiyao Chen, Hanhua Jin, Hai Hua, Qiang-Sheng Zhou, Bing Bing Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

ISBN: (纸本)9781450390682

Complete Event Trend (CET) detection over large-scale event streams is important and challenging in various applications such as financial services, real-time business analysis, and supply chain management. A potential large number of partial intermediate results during complex event matching can raise prohibitively high memory cost for the processing system. The state-of-the-art scheme leverages compact graph encoding, which represents the common sub-sequences of different complex events using a common sub-graph to achieve space efficiency for storing the intermediate results. However, we show that such a design raises unacceptable computation cost for the graph traversal needed whenever a new event comes. To address this problem, in this paper, we propose a novel attribute-based indexing (ABI) graph model to represent the relationship between events. By classifying the predicates and constructing the graph based on both the comparators in the predicates and the attribute values of the events, we achieve parallel event stream processing and efficient graph construction. Our design significantly reduces the total computation cost of graph construction from O(n2) to O(nlo.(m)), where n is the number of events andm is the number of the attribute vertices. We further design several efficient traversal-based algorithms to extract CETs from the graph. We implement our design and conduct comprehensive experiments to evaluate the performance of this design. The results show that our design wins a couple of orders of magnitude back from state-of-the-art schemes.

关键词： Big data stream process complete event trend

来源：评论

学校读者我要写书评

暂无评论

基于采样的数据流差分隐私快速发布算法

引用

计算机研究与发展 2024年第10期61卷 2433-2447页

作者：王修君莫磊郑啸卫琳娜董俊刘志郭龙坤安徽工业大学计算机科学与技术学院安徽马鞍山243032 安徽省工业互联网智能应用与安全工程研究中心(安徽工业大学) 安徽马鞍山243032 福州大学数学与统计学院福州350108 中国科学院合肥物质科学研究院智能机械研究所合肥230031 电气通信大学日本东京163-8001

基于云原生数据库的许多应用场景需要处理海量的数据流.为了实时分析数据流中的群体趋势信息而又不泄露单个用户的隐私,这些应用需要在每个时刻都可以为数据流中的最近数据集快速创建可以安全发布的差分隐私直方图.然而,现有的直方图发... 详细信息

基于云原生数据库的许多应用场景需要处理海量的数据流.为了实时分析数据流中的群体趋势信息而又不泄露单个用户的隐私,这些应用需要在每个时刻都可以为数据流中的最近数据集快速创建可以安全发布的差分隐私直方图.然而,现有的直方图发布方法因缺乏高效数据结构,导致无法快速提取关键信息以确保数据的实时可用性.为解决此问题,深入分析数据采样与隐私保护之间的关系,提出基于采样的数据流差分隐私快速发布算法SPF(sampling based fast publishing algorithm with differential privacy for data stream).SPF首创高效数据流采样草图结构(efficient data stream sampling sketch structure,EDS),EDS对滑动窗口内数据进行采样统计估计,并过滤不合理数据,实现了对关键信息的快速提取.然后,证明EDS结构输出的近似值理论上等效于对真实值添加差分隐私噪声.最后,为了满足用户所提供的隐私保护强度,并且避免正确反映原始数据流的真实情况,提出了一种基于高效数据流采样的自适应加噪算法.根据用户的隐私保护强度和EDS结构所提供的隐私保护强度之间的关系,通过隐私分配的方式自适应生成最终可发布直方图.实验证明,相较于现有算法,SPF在保持相同数据可用性的前提下显著降低了时间和空间开销.

关键词：云原生数据库滑动窗口数据流差分隐私数据采样数据发布

来源：评论

学校读者我要写书评

暂无评论

Cost-Effective space Partitioning Approach for IoT data Indexing and Retrieval 5

Cost-Effective Space Partitioning Approach for IoT Data Inde...

引用

5th IEEE International conference on Networking and Advanced Systems, ICNAS 2021

作者： Kemouguette, Ibtissem Kouahla, Zineddine Benrazek, Ala-Eddine Farou, Brahim Seridi, Hamid 8 May 1945 University LabSTIC Laboratory Computer Science Department P.O. Box 401 Guelma24000 Algeria

ISBN: (纸本)9781665428217

IoT technology implies a huge group of connected devices to capture a tremendous quantity of information. Computing and storing this huge amount of data requires scalable and efficient indexing solutions. Among the latest indexing structures in the BCCF-tree metric space, one framework is focused on providing recursive clustering of the space using the k-means algorithm. This solution suffers from data overlap between space partitioning at large scale. This study aims to optimize the structure of the BCCF. We propose to replace the k-means algorithm with an algorithm for estimating the overlap rate between the partitions of the space. Experimental results show good performance of the construction and search algorithms on several types of datasets. © 2021 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

IndoorViz: A Demonstration System for Indoor Spatial data Management 21

IndoorViz: A Demonstration System for Indoor Spatial Data Ma...

引用

ACM SIGMOD International conference on Management of data (SIGMOD)

作者： Li, Yue Yang, Shiyu Cheema, Muhammad Aamir Shao, Zhou Lin, Xuemin East China Normal Univ Shanghai Peoples R China Guangzhou Univ Guangzhou Peoples R China Monash Univ Melbourne Vic Australia Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781450383431

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this demonstration, we present IndoorViz , a new indoor spatial data management system that integrates three novel index structures proposed in [4] and [6] with well designed query processing algorithms and 3D visualization functions. The IndoorViz is able to support indoor spatial object indexing, efficient query processing and interactive 3D display.

关键词： Indoor Location-based Service Indoor Index Indoor Query Processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：