检索结果-内蒙古大学图书馆

Efficient Storage Management for Social Network Events Based on Clustering and Hot/Cold Data Classification

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 120-130页

作者： Xie, Yulai Tong, Shuai Zhou, Pan Li, Yuli Feng, Dan Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Hubei Engn Res Ctr Big Data Secur Wuhan 430074 Peoples R China Minist Educ China Wuhan Natl Lab Optoelect Key Lab Informat Storage Syst Engn Res Ctr Data Storage Syst & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

Social network events are related to the national economy and people's livelihood, so timely perception and processing of massive social network events data are becoming increasingly important for public opinion analysis. How to fully exploit the access feature of event information to store and manage social network events is of great significance for accurate and real-time query analysis. We propose a social network event storage management method based on microblog text clustering and hot/cold data classification. First, for the microblog text data, we construct a keyword provenance graph by using the information entropy to measure the weight of the edge between keyword nodes. Then, we cluster the events using the provenance-based community partition (PCP) with local modularity to improve the event clustering accuracy. In addition, we can further filter noisy data via incremental clustering, enable hot/cold event data classification and dynamic migration, and compress cold data to save space on a hybrid storage architecture. The experimental results show that the clustering purity can reach more than 93% and the query time can be reduced by more than 70% using clustering and hybrid storage policy.

关键词： Blogs Social networking (online) Clustering algorithms Mutual information partitioning algorithms Storage management Semisupervised learning Data provenance event clustering hot cold data classification social network event

来源：评论

学校读者我要写书评

暂无评论

A Space-Efficient Fair Cache Scheme Based on Machine Learning for NVMe SSDs

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第1期34卷 383-399页

作者： Liu, Weiguang Cui, Jinhua Li, Tiantian Liu, Junwei Yang, Laurence T. Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

Non-volatile memory express (NVMe) solid-state drives (SSDs) have been widely adopted in multi-tenant cloud computing environments or multi-programming systems. The on-board DRAM cache inside NVMe SSDs can efficiently reduce the disk accesses and extend the lifetime of SSDs. Current SSD cache management research either improves cache hit ratio while ignoring fairness, or improves fairness while sacrificing overall performance. In this paper, we present MLCache, a space-efficient shared cache management scheme for NVMe SSDs. By learning the impact of reuse distance on cache allocation, a workload-generic neural network model is built. At runtime, MLCache continuously monitors the reuse distance distribution for the neural network module to obtain space-efficient allocation decisions. MLCache also proposes an efficient parallel writing back strategy based on hit ratio and response time, to improve fairness. Experimental results show MLCache improves the write hit ratio when compared to baseline, and MLCache strongly safeguards the fairness of SSDs with parallel write-back and maintains a low level of degradation.

关键词： partitioning algorithms Nonvolatile memory Time factors Throughput Writing Resource management Random access memory NVMe SSDs cache partition fairness reuse distance machine learning DFTL

来源：评论

学校读者我要写书评

暂无评论

Rearranging 'indivisible' Blocks for Community Detection

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第6期35卷 6252-6263页

作者： Sun, Peng Gang Wu, Xunlian Quan, Yining Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Unattributed social networks are more complicated, and it tends not to determine the best division by over-optimizing a theoretical measure for unsupervised algorithms. Nowadays, communities strongly overlap due to the fact that people strongly interact, which makes community detection even more challenging. The paper develops a new algorithm by rearranging 'indivisible' blocks (RaidB). In RaidB, we first initialize 'indivisible' blocks by disjoint k-clique blocks in a network, and then these blocks are rearranged by moving nodes from one block to another based on maximizing modularity to uncover non-overlapping communities. For identifying overlapping communities, the above blocks are further rearranged, i.e., each block is subdivided and expanded to determine sub-blocks by introducing a dynamic linear threshold (DLT) model for influence interpenetration, and we finally determine a division from these sub-blocks with the minimum size that can cover the network. We compare RaidB with the existing state of the art methods for non-overlapping and overlapping community detection. The results show that RaidB tends to achieve better performance especially on sparse networks with unobvious communities and networks with strongly overlapping communities.

关键词： partitioning algorithms Social networking (online) Optimization methods Sun Scalability Mathematical models Generative adversarial networks Community detection dynamic linear threshold model rearrange indivisible blocks

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Robust Clustering Over Time for Temporal Data

引用

IEEE TRANSACTIONS ON CYBERNETICS 2023年第7期53卷 4334-4346页

作者： Zhao, Qi Yan, Bai Yang, Jian Shi, Yuhui Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

In many clustering scenes, data samples' attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search toward the preferred direction. This a priori manner leads to a risk of converging to an unexpected region because it is not always the case that a reasonable preference can be elicited given the little prior knowledge about the data. To address this issue, this article proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner, which avoids unexpected convergence that occurs in existing algorithms. Furthermore, the proposed framework automatically infers the a posteriori preference to temporal smoothness without data's affinity matrix and predefined parameters, which holds better applicability and efficiency. The effectiveness and efficiency of the proposed framework are confirmed by comparing with state-of-the-art algorithms on both synthetic and real datasets.

关键词： Clustering algorithms partitioning algorithms Optimization Indexes Linear programming Tuning Task analysis Dynamic optimization evolutionary algorithm (EA) evolutionary clustering temporal data

来源：评论

学校读者我要写书评

暂无评论

Linear Clustering Process on Networks

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第6期10卷 3697-3706页

作者： Jokic, Ivan Van Mieghem, Piet Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands

We propose a linear clustering process on a network consisting of two opposite forces: attraction and repulsion between adjacent nodes. Each node is mapped to a position on a one-dimensional line. The attraction and repulsion forces move the nodal position on the line, depending on how similar or different the neighbourhoods of two adjacent nodes are. Based on each node position, the number of clusters in a network and each node's cluster membership is estimated. The performance of the proposed linear clustering process is benchmarked on synthetic networks against widely accepted clustering algorithms such as modularity, Leiden method, Louvain method and the non-back tracking matrix. The proposed linear clustering process outperforms the most popular modularity-based methods, such as the Louvain method, on synthetic and real-world networks, while possessing a comparable computational complexity.

关键词： Clustering algorithms Force Benchmark testing partitioning algorithms Heuristic algorithms Mutual information Matrix decomposition Communities graph clustering modularity linear process

来源：评论

学校读者我要写书评

暂无评论

Graph algorithms With Partition Transparency

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1554-1566页

作者： Fan, Wenfei Liu, Muyang Lu, Ping Yin, Qiang Univ Edinburgh Shenzhen Inst Comp Sci Edinburgh EH8 9AB Scotland Univ Edinburgh Sch Informat Edinburgh Scotland Beihang Univ SKLSDE Beijing 100191 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

Graph computations often have to be conducted in parallel on partitioned graphs. The choice of graph partitioning strategies, however, has strong impact on the design of graph computation algorithms. A graph algorithm developed under edge-cut partitions may not work correctly under vertex-cut, and vice versa. We often have to rewrite our algorithms when we switch from, e.g., edge-cut to vertex-cut. To cope with this, we propose a notion of partition transparency, such that graph algorithms are able to work correctly under different partitions without changes and moreover, benefit from recent hybrid partitions to speed up computations. Furthermore, we identify conditions under which graph algorithms are guaranteed to be partition-transparent, in graph-centric and vertex-centric models. We show that a variety of graph algorithms can be made partition-transparent. Using real-life and synthetic graphs, we experimentally verify that partition-transparent algorithms compute correct answers under different partitions;better still, under hybrid partitions these algorithms perform better than algorithms tailored for edge-cut and vertex-cut partitions in efficiency.

关键词： partitioning algorithms Program processors Image edge detection Switches Pipelines Mirrors Heuristic algorithms Graph partition partition transparency graph-centric algorithms vertex-centric algorithms

来源：评论

学校读者我要写书评

暂无评论

A Clustering Algorithm for Polygonal Data Applied to Scientific Journal Profiles

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13766-13777页

作者： Silva, Wagner J. F. Souza, Pedro J. C. Souza, Renata M. C. R. Cysneiros, Francisco Jose A. Univ Fed Pernambuco Informat Ctr Recife PE Brazil Univ Fed Pernambuco Dept Estatst Recife Brazil

Millions of papers are submitted and published every year, but researchers often do not have much information about the journals that interest them. In this paper, we introduced the first dynamical clustering algorithm for symbolic polygonal data and this was applied to build scientific journals profiles. Dynamic clustering algorithms are a family of iterative two-step relocation algorithms involving the construction of clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between clusters and their corresponding prototypes The application gives a powerful vision to understand the main variables that describe journals. Symbolic polygonal data can represent summarized extensive datasets taking into account variability. In addition, we developed cluster and partition interpretation indices for polygonal data that have the ability to extract insights about clustering results. From these indices, we discovered, e.g., that the number of difficult words in abstract is fundamental to building journal profiles.

关键词： Clustering algorithms Heuristic algorithms Prototypes partitioning algorithms Clustering methods Text mining Data science Dynamical clustering algorithm polygonal data scientific journal profiles symbolic data analysis

来源：评论

学校读者我要写书评

暂无评论

Joint DNN Partition and Resource Allocation Optimization for Energy-Constrained Hierarchical Edge-Cloud Systems

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3930-3944页

作者： Su, Yi Fan, Wenhao Gao, Li Qiao, Lei Liu, Yuan'an Wu, Fan Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety Intelligent Monitoring Beijing 100876 Peoples R China

Hierarchical edge-cloud systems collaboratively utilize the resources of both the edge server and central cloud, enabling deep neural network (DNN) partition between the edge and cloud to accelerate the inference. However, the limited energy budgets of both the edge server and central cloud restrict them from providing optimal DNN inference services. Moreover, considering the high dynamics in stochastic environments, the long-term system performance should be optimized under long-term energy constraints. How to improve the long-term DNN inference performance in such energy-constrained hierarchical edge-cloud systems is less studied by existing related works. In this paper, we aim to jointly optimize DNN partition and computing resource allocation to minimize the long-term average end-to-end delay of multiple types of deep learning (DL) tasks while guaranteeing the energy consumption of the edge server and central cloud within their energy budgets. Based on the Lyapunov optimization technique and reinforcement learning, we design a novel deep deterministic policy gradient based DNN partition and resource allocation (DDPRA) algorithm to train policy to decide DNN partition dynamically by observing the environment. Moreover, the DDPRA algorithm is embedded with a heuristic computing resource allocation (HCRA) algorithm, which effectively reduces the complexity of policy training by decoupling and optimizing the computing resource allocation separately. We analyze the complexity of our algorithms and conduct extensive simulations. The numerical results demonstrate the superiority of our algorithm in comparison with 5 other schemes in multiple scenarios.

关键词： Servers Task analysis Resource management partitioning algorithms Cloud computing Computational modeling Delays Hierarchical edge-cloud systems DNN partition computing resource allocation end-to-end delay energy budget

来源：评论

学校读者我要写书评

暂无评论

Breaking the Computational Bottleneck: Probabilistic Optimization of High-Memory Spatially-Coupled Codes

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第2期69卷 886-909页

作者： Yang, Siyi Hareedy, Ahmed Calderbank, Robert Dolecek, Lara Univ Calif Los Angeles Los Angeles CA 90095 USA Middle East Tech Univ METU Dept Elect & Elect Engn TR-27708 Ankara Turkiye Duke Univ Dept Elect & Comp Engn Durham NC USA Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA USA

Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a convolutional manner. The number of partitioned components determines the memory of SC codes. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. While adopting higher memories results in improved SC code performance, obtaining finite-length, high-performance SC codes with high memory is known to be computationally challenging. We break this computational bottleneck by developing a novel probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low-complexity optimization algorithms that minimize the number of detrimental objects to construct high-memory, high-performance quasi-cyclic SC codes. We apply our framework to various objects of interest, from the simplest short cycles, to more sophisticated objects such as concatenated cycles aiming at finer-grained optimization. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and optimized SC codes with uniform partitioning. The performance gain is shown to be universal over a variety of channels, from canonical channels such as additive white Gaussian noise and binary symmetric channels, to practical channels underlying flash memory and magnetic recording systems.

关键词： Codes Optimization Parity check codes Memory management partitioning algorithms Probabilistic logic Convolutional codes LDPC codes spatially-coupled codes absorbing sets edge distribution gradient descent near-optimal partitioning data storage flash memories magnetic recording communications

来源：评论

学校读者我要写书评

暂无评论

HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4153-4166页

作者： Shehnepoor, Saeedreza Togneri, Roberto Liu, Wei Bennamoun, Mohammed Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia Univ Western Australia Dept Comp Sci & Software Engn Perth WA 6009 Australia

Social reviews are indispensable resources for modern consumers' decision making. To influence the reviews, for financial gains, some companies may choose to pay groups of fraudsters rather than individuals to demote or promote products and services. This is because consumers are more likely to be misled by a large amount of similar reviews, produced by a group of fraudsters. Semantic relation such as content similarity (CS) and polarity similarity is an important factor characterizing solicited group frauds. Recent approaches on fraudster group detection employed handcrafted features of group behaviors that failed to capture the semantic relation of review text from the reviewers. In this article, we propose the first neural approach, HIN-RNN, a heterogeneous information network (HIN) compatible recurrent neural network (RNN) for fraudster group detection that makes use of semantic similarity and requires no handcrafted features. The HIN-RNN provides a unifying architecture for representation learning of each reviewer, with the initial vector as the sum of word embeddings (SoWEs) of all review text written by the same reviewer, concatenated by the ratio of negative reviews. Given a co-review network representing reviewers who have reviewed the same items with similar ratings and the reviewers' vector representation, a collaboration matrix is captured through the HIN-RNN training. The proposed approach is demonstrated to be effective with marked improvement over state-of-the-art approaches on both the Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4% and 2% in terms of recall and F1-value, respectively) datasets.

关键词： Feature extraction Semantics Task analysis Collaboration Australia Recurrent neural networks partitioning algorithms Fraudster group heterogeneous information network (HIN) HIN-recurrent neural network (RNN) sum of word embedding (SoWE)

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：