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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Zou, Yang Wang, Jianxiao School of Engineering Peking University Beijing China National Engineering Laboratory For Big Data Analysis and Applications Peking University Beijing China Peking University Ordos Research Institute of Energy Ordos China

ISBN: (纸本)9798350345094

Recently, many energy storage-related enterprises have been facing difficulties brought out by the limitation of land and the increase in loan cost. As a promising approach to improving space utilization rate, puzzle-based energy storage systems (PBESSs) are drawing more attention from logistics researchers. In previous research about PBESS, concentration has been paid to single target item problems. However, there are no consensus algorithms to solve load retrievals route programming in PBESSs with multiple target items. In this paper, a heuristic algorithm is proposed to solve load retrieval route programming in PBESSs with multiple target items, multiple escorts, and multiple (Inputs Outputs) IOs. This paper defines new concepts about the proposed algorithm, including target IOs, target position of escorts, number of escorts required, et al. Then, the decision procedures are designed according to these concepts. Based on the Markov decision process, the proposed algorithm makes the decision of the next action by analyzing the current status, until all the target items arrive at the IOs. The case study results have shown the effectiveness and efficiency of the proposed heuristic algorithm. © 2023 IEEE.

关键词： Markov processes

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Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation 40

Accelerating Scalable Graph Neural Network Inference with No...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Xinyi Zhang, Wentao Yu, Junliang Shao, Yingxia Nguyen, Quoc Viet Hung Cui, Bin Yin, Hongzhi The University of Queensland Brisbane Australia Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Beijing University of Posts and Telecommunications Beijing China Griffith University Gold Coast Australia

ISBN: (纸本)9798350317152

Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. Although existing Scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure, these methods still suffer from scalability issues when making inferences on unseen nodes, as the feature preprocessing requires the graph to be known and fixed. To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation. The trade-off between accuracy and latency can be flexibly managed through simple hyper-parameters to accommodate various latency constraints. Moreover, to compensate for the inference accuracy loss caused by the potential early termination of propagation, we further propose Inception Distillation to exploit the multi-scale receptive field information within graphs. The rigorous and comprehensive experimental study on public datasets with varying scales and characteristics demonstrates that the proposed inference acceleration framework outperforms existing state-of-the-art graph inference acceleration methods in terms of accuracy and efficiency. Particularly, the superiority of our approach is notable on datasets with larger scales, yielding a 75× inference speedup on the largest Ogbn-products dataset. © 2024 IEEE.

关键词： Graph neural networks

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Graph Condensation for Inductive Node Representation Learning 40

Graph Condensation for Inductive Node Representation Learnin...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Gao, Xinyi Chen, Tong Zang, Yilong Zhang, Wentao Hung Nguyen, Quoc Viet Zheng, Kai Yin, Hongzhi The University of Queensland Brisbane Australia Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350317152

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph. © 2024 IEEE.

关键词： Condensation

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Correction to: Well-Balanced Fifth-Order Finite Volume WENO Schemes with Constant Subtraction Technique for Shallow Water Equations

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Journal of Scientific Computing 2025年第1期103卷 1-4页

作者： Zhao, Lidan Tao, Zhanjing Zhang, Min School of Mathematics Jilin University Changchun China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Chongqing Research Institute of Big Data Peking University Chongqing China

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Shaping future low-carbon energy and transportation systems: Digital technologies and applications

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iEnergy 2022年第3期1卷 285-305页

作者： Jie Song Guannan He Jianxiao Wang Pingwen Zhang Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China School of Mathematical Sciences Peking Univer-sityBeijing 100871China

Digitalization and decarbonization are projected to be two major trends in the coming *** the already widespread process of digitalization continues to progress,especially in energy and transportation systems,massive data will be produced,and how these data could support and promote decarbonization has become a pressing *** paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure,common mechanisms and algorithms,and system-level impacts,as well as the application of digital technologies to coupled energy and transportation systems with electric *** paper also identifies corresponding challenges and future research directions,such as in the field of blockchain,digital twin,vehicle-to-grid,low-carbon computing,and data security and pri-vacy,especially in the context of integrated energy and transportation systems.

关键词： Digitalization decarbonization energy system transportation system energy and transportation integration.

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SketchConf: A Framework for Automatic Sketch Configuration 39

SketchConf: A Framework for Automatic Sketch Configuration

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Miao, Ruijie Dong, Fenghao Zhao, Yikai Zhao, Yiming Wu, Yuhan Yang, Kaicheng Yang, Tong Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github. © 2023 IEEE.

关键词： Frequency estimation

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SteadySketch: Finding Steady Flows in data Streams 31

SteadySketch: Finding Steady Flows in Data Streams

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Li, Xiaodong Fan, Zhuochen Li, Haoyu Zhong, Zheng Guo, Jiarui Long, Sheng Yang, Tong Cui, Bin School of Computer Science Peking University National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350399738

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub. © 2023 IEEE.

关键词： Steady flow

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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Computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Hierarchical Interest Modeling of Long-tailed Users for Click-Through Rate Prediction 39

Hierarchical Interest Modeling of Long-tailed Users for Clic...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Xie, Xu Niu, Jin Deng, Lifang Wang, Dan Zhang, Jiandong Wu, Zhihua Bian, Kaigui Cao, Gang Cui, Bin China Alibaba Group China Lazada National Engineering Lab for Big Data Analysis and Applications China China Peking University Institute of Computational Social Science Qingdao China

ISBN: (纸本)9798350322279

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users' accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key *** this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users' limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://***/xiaojin-nj/HIM. © 2

关键词： Recommender systems

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