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学者姓名：程永利
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Existing graph systems focus mainly on the execution efficiency of the graph analysis tasks, often ignoring the importance and efficiency of time-evolving graph storage. However, to effectively mine the potential application values, an efficient storage system is important for time-evolving graphs whose storage requirement scales with the increasing number of snapshots. Storage cost and snapshot access speed are the two most important performance indicators for a time-evolving graph storage system, which are challenging for designers of such systems because they are conflicting goals. In this article, we address these challenges by proposing an efficient storage scheme for the large time-evolving graphs. We first design a Snapshot-level Data Deduplication (SLDD) strategy to eliminate the large number of repeated vertices and edges among the snapshots, and then a Structure-Changing Graph Representation (SCGR) to significantly improve the snapshot access speed. We implement an efficient time-evolving graph storage system, TgStore, based on this scheme to effectively store large-scale time-evolving graphs, aiming to efficiently support the time-evolving graph analysis tasks. Experimental results show that TgStore can obtain a high compression ratio of 43.03:1 when storing 100 snapshots of Twitter, while with an average snapshot access speedup of 16x. Efficient storage scheme enables TgStore to efficiently support time-evolving graph algorithms. For example, when executing the Pagerank algorithm on the time-evolving graph of Twitter, TgStore outperforms Graphone, a state-of-the-art time-evolving graph storage system, by 15.9x in algorithm execution speed and 1.45x in memory usage.
Keyword ：
Big Data Big Data Blogs Blogs Costs Costs data deduplication data deduplication data representation data representation Market research Market research Pandemics Pandemics Social networking (online) Social networking (online) storage system storage system Task analysis Task analysis Time-evolving graph Time-evolving graph
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| GB/T 7714 | Cheng, Yongli , Ma, Yan , Jiang, Hong et al. TgStore: An Efficient Storage System for Large Time-Evolving Graphs [J]. | IEEE TRANSACTIONS ON BIG DATA , 2024 , 10 (2) : 158-173 . |
| MLA | Cheng, Yongli et al. "TgStore: An Efficient Storage System for Large Time-Evolving Graphs" . | IEEE TRANSACTIONS ON BIG DATA 10 . 2 (2024) : 158-173 . |
| APA | Cheng, Yongli , Ma, Yan , Jiang, Hong , Zeng, Lingfang , Wang, Fang , Xu, Xianghao et al. TgStore: An Efficient Storage System for Large Time-Evolving Graphs . | IEEE TRANSACTIONS ON BIG DATA , 2024 , 10 (2) , 158-173 . |
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In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP jobs. In this paper, we propose GraphCP, a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. Moreover, GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future iterations. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 20.5x and 8.9x faster than two out-of-core graph processing systems GridGraph and GraphZ, and 3.5x and 1.7x faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.
Keyword ：
concurrent jobs concurrent jobs disk I/O disk I/O graph processing graph processing
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| GB/T 7714 | Xu, Xianghao , Wang, Fang , Jiang, Hong et al. A disk I/O optimized system for concurrent graph processing jobs [J]. | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (3) . |
| MLA | Xu, Xianghao et al. "A disk I/O optimized system for concurrent graph processing jobs" . | FRONTIERS OF COMPUTER SCIENCE 18 . 3 (2024) . |
| APA | Xu, Xianghao , Wang, Fang , Jiang, Hong , Cheng, Yongli , Feng, Dan , Fang, Peng . A disk I/O optimized system for concurrent graph processing jobs . | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (3) . |
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Big data applications increasingly rely on the analysis of large graphs. In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, by efficiently using the secondary storage (e.g., hard disk, SSD). Unfortunately, these graph processing systems continue to suffer from poor performance, despite of many solutions proposed to address the disk I/O bottleneck problem, a commonly recognized root cause. However, our experimental results show that another root cause of the poor performance is the subgraph construction phase of graph processing, which induces a large number of random memory accesses that substantially weaken cache access locality and thus greatly degrade the performance. In this paper, we propose an efficient out-of-core graph processing system, LOSC, to substantially reduce the overheads of subgraph construction. LOSC proposes a locality-optimized subgraph construction scheme that significantly improves the in-memory data access locality of the subgraph construction phase. Furthermore, LOSC adopts a compact edge storage format and a lightweight replication of vertices to reduce I/O traffic and improve computation efficiency. Extensive evaluation results show that LOSC is respectively 9.4x and 5.1x faster than GraphChi and GridGraph, two representative out-of-core systems. In addition, LOSC outperforms other state-of-art out-of-core graph processing systems including FlashGraph, GraphZ, G-Store and NXGraph. For example, LOSC can be up to 6.9x faster than FlashGraph.(c) 2022 Elsevier Inc. All rights reserved.
Keyword ：
Graph processing Graph processing Out-of-core Out-of-core Subgraph construction Subgraph construction
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| GB/T 7714 | Xu, Xianghao , Wang, Fang , Jiang, Hong et al. LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 172 : 51-68 . |
| MLA | Xu, Xianghao et al. "LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 172 (2023) : 51-68 . |
| APA | Xu, Xianghao , Wang, Fang , Jiang, Hong , Cheng, Yongli , Hua, Yu , Feng, Dan et al. LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 172 , 51-68 . |
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时变图连通分量已经被广泛应用到不同场景,?如交通路网建设、推荐系统的信息推送等.?然而当前多数连通分量求解方法忽视了NUMA体系结构对计算效率产生的影响,?即过高的远程内存访问延迟导致低下的算法执行效率.?本文针对时变图的弱连通分量求解问题,?提出一种基于NUMA延迟发送的时变图弱连通分量求解方法,?它通过合理的数据内存布局,?合理控制NUMA节点间的信息交换次数,?最大限度减少远程内存访问数量,?显著提高了算法执行效率.?实验结果表明,?该方法的性能明显优于当前流行的图处理系统Ligra和Polymer提供的方法.
Keyword ：
NUMA NUMA 图计算 图计算 延迟发送 延迟发送 弱连通分量 弱连通分量 时变图 时变图
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| GB/T 7714 | 梁锐杰 , 程永利 . 基于NUMA延迟发送的时变图弱连通分量求解 [J]. | 计算机系统应用 , 2023 , 32 (3) : 322-329 . |
| MLA | 梁锐杰 et al. "基于NUMA延迟发送的时变图弱连通分量求解" . | 计算机系统应用 32 . 3 (2023) : 322-329 . |
| APA | 梁锐杰 , 程永利 . 基于NUMA延迟发送的时变图弱连通分量求解 . | 计算机系统应用 , 2023 , 32 (3) , 322-329 . |
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为了解决高校存在大数据高层次创新人才培养与产业需求脱节的问题,通过对大数据产业专班研究生联合培养示范基地建设进行探索,完善现有的基地建设方案、持续稳定扩大专班规模、深化培养模式改革,为培育符合大数据产业需求的创新型人才提供思路.福州大学通过多年的探索与实践,为企业培养了一大批具有大数据产业创新能力的高层次人才,实践经验可为高校开设相关产业研究生联合培养基地建设提供参考.
Keyword ：
大数据 大数据 研究生培养 研究生培养 示范基地 示范基地
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| GB/T 7714 | 郭文忠 , 陈星 , 程永利 . 大数据产业专班研究生联合培养示范基地建设探索与实践 ——以福州大学为例 [J]. | 科技创业月刊 , 2023 , 36 (4) : 153-156 . |
| MLA | 郭文忠 et al. "大数据产业专班研究生联合培养示范基地建设探索与实践 ——以福州大学为例" . | 科技创业月刊 36 . 4 (2023) : 153-156 . |
| APA | 郭文忠 , 陈星 , 程永利 . 大数据产业专班研究生联合培养示范基地建设探索与实践 ——以福州大学为例 . | 科技创业月刊 , 2023 , 36 (4) , 153-156 . |
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Many applications need to execute Single-Source Shortest Paths (SSSP) algorithm on each snapshot of a time evolving graph, leading to long waiting times experienced by the users of such applications. However, these applications are often time-sensitive, the delayed computation results can lead to the loss of best decision-making opportunities. To address this problem, in this paper we propose an efficient SSSP algorithm for time-evolving graphs, called V-Grouper. The main idea of V-Grouper is to avoid the redundant computations of the same vertex in different snapshots. Our experimental results over real-world time-evolving graphs show that, due to the high similarity of consecutive snapshots, the computation results of one vertex in neighboring snapshots are equal with a high probability. At the beginning of computation, V-Grouper first divides all the versions of a given vertex in different snapshots into vertex groups, where the computation result of each version is predicted based on the aforementioned insight of neighboring snapshots having equal results. The versions of the vertex in each group have the same predicted computation result. During the computation process for each vertex group, only one version needs to participate in computation, avoiding a large number of redundant computations. Experimental results show that V-Grouper is up to 64.31x faster than the state-of-the-art SSSP algorithm.
Keyword ：
Grouper Grouper Predicted computation results Predicted computation results SSSP SSSP Time-evolving graph Time-evolving graph
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| GB/T 7714 | Cheng, Yongli , Huang, Chuanjie , Jiang, Hong et al. An efficient SSSP algorithm on time-evolving graphs with prediction of computation results [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 186 . |
| MLA | Cheng, Yongli et al. "An efficient SSSP algorithm on time-evolving graphs with prediction of computation results" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 186 (2023) . |
| APA | Cheng, Yongli , Huang, Chuanjie , Jiang, Hong , Xu, Xianghao , Wang, Fang . An efficient SSSP algorithm on time-evolving graphs with prediction of computation results . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 186 . |
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图分区质量极大程度上影响着计算机之间的通信开销和负载平衡,这对于大规模并行图计算的性能是至关重要的.然而,随着图数据规模的越来越大,图分区算法的执行时间成了一个不可避免的问题.因此,研究如何优化图分区算法的执行效率是有必要的.本文提出了一个基于广度优先遍历加权图生成的启发式图分割方法,该方法在实现较低的通信代价和较好负载平衡的同时,只引入了少量的预处理时间开销.实验结果表明,本文的划分方法减少了复制因子,降低通信开销,并且引入的时间开销较小.
Keyword ：
图分区 图分区 图分析 图分析 图计算 图计算 负载平衡 负载平衡 顶点切割分区 顶点切割分区
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| GB/T 7714 | 蹇冬宇 , 程永利 . 基于广度优先遍历加权图生成的启发式图分区 [J]. | 计算机系统应用 , 2023 , 32 (12) : 218-223 . |
| MLA | 蹇冬宇 et al. "基于广度优先遍历加权图生成的启发式图分区" . | 计算机系统应用 32 . 12 (2023) : 218-223 . |
| APA | 蹇冬宇 , 程永利 . 基于广度优先遍历加权图生成的启发式图分区 . | 计算机系统应用 , 2023 , 32 (12) , 218-223 . |
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Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement).
Keyword ：
Deep metric learning Deep metric learning Multimedia retrieval Multimedia retrieval Multi-modal hash Multi-modal hash Multi-view hash Multi-view hash
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| GB/T 7714 | Zhu, Jian , Ruan, Xiaohu , Cheng, Yongli et al. Deep Metric Multi-View Hashing for Multimedia Retrieval [J]. | 2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME , 2023 : 1955-1960 . |
| MLA | Zhu, Jian et al. "Deep Metric Multi-View Hashing for Multimedia Retrieval" . | 2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME (2023) : 1955-1960 . |
| APA | Zhu, Jian , Ruan, Xiaohu , Cheng, Yongli , Huang, Zhangmin , Cui, Yu , Zeng, Lingfang . Deep Metric Multi-View Hashing for Multimedia Retrieval . | 2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME , 2023 , 1955-1960 . |
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为了解决大数据高层次创新人才培养与产业需求脱节问题，对现有大数据研究生培养模式进行改革是十分必要的。本文提出一种产教融合背景下大数据专班研究生培养模式，契合大数据产业对高层次创新人才的需求，注重符合大数据产业需求人才培育和创新技能提升，通过凝练专班研究方向，建立专班课程体系，建设专班实践基地，最终形成产教融合培养模式。福州大学多年的实践结果表明，该培养方案可以有效解决大数据研究生培养与产业需求脱节问题，向知名企业输送了大量具有大数据产业创新能力的研究生。
Keyword ：
专班 专班 产教整合 产教整合 大数据 大数据 研究生培养 研究生培养
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| GB/T 7714 | 郭文忠 , 陈星 , 程永利 . 产教融合背景下大数据专班研究生培养模式 [J]. | 福建电脑 , 2022 , 38 (08) : 115-118 . |
| MLA | 郭文忠 et al. "产教融合背景下大数据专班研究生培养模式" . | 福建电脑 38 . 08 (2022) : 115-118 . |
| APA | 郭文忠 , 陈星 , 程永利 . 产教融合背景下大数据专班研究生培养模式 . | 福建电脑 , 2022 , 38 (08) , 115-118 . |
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With the rapid development of manipulation technologies, the generation of Deep Fake videos is more accessible than ever. As a result, face forgery detection becomes a challenging task, attracting a significant amount of attention from researchers worldwide. However, most previous work, consisting of convolutional neural networks (CNN), is not sufficiently discriminative and cannot fully utilise subtle clues and similar textures during the process of facial forgery detection. Moreover, these methods cannot simultaneously consider accuracy and time efficiency. To address such problems, we propose a novel framework named FPC-Net to extract some meaningful and unnatural expressions in local regions. This framework utilises CNN, long short-term memory (LSTM), channel groups loss (CG-Loss) and adaptive feature fusion to detect face forgery videos. First, the proposed method exploits spatial features by CNN, and a channel-wise attention mechanism is employed to separate channels. Specifically, with the help of channel groups loss, the channels are divided into two groups, each representing a specific class. Second, LSTM is applied to learn the correlation of spatial features. Finally, the correlation of features is mapped into other latent spaces. Through a lot of experiments, the results are that the detection speed of the proposed method reaches 420 FPS and the auc scores achieve best performance of 99.7%, 99.9%, 94.7%, and 82.0% on Raw Celeb-DF, Raw Face Forensics++, F2F and NT datasets respectively. The experimental results demonstrate that the proposed framework has great time efficiency performance while improving the detection performance compared with other frame-level methods in most cases.
Keyword ：
adaptive feature fusion adaptive feature fusion facial forgery detection facial forgery detection
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| GB/T 7714 | Wu, Bin , Su, Lichao , Chen, Dan et al. FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss [J]. | IET COMPUTER VISION , 2022 , 17 (3) : 330-340 . |
| MLA | Wu, Bin et al. "FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss" . | IET COMPUTER VISION 17 . 3 (2022) : 330-340 . |
| APA | Wu, Bin , Su, Lichao , Chen, Dan , Cheng, Yongli . FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss . | IET COMPUTER VISION , 2022 , 17 (3) , 330-340 . |
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