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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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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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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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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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Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph databases 39

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Qiu, Rui Ming, Yi Hong, Yisen Li, Haoyu Yang, Tong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peking University Academy for Advanced Interdisciplinary Studies China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350322279

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases. © 2023 IEEE.

关键词： Graph databases

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HyperCalm Sketch: One-Pass Mining Periodic Batches in data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng 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 Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

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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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RIM: Reliable influence-based active learning on graphs

arXiv

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arXiv 2021年

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Cao, Meng Huang, Ping Shan, Jiulong Yang, Zhi Cui, Bin School of CS Peking University Apple National Engineering Laboratory for Big Data Analysis and Applications China

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph. However, the labeling process can be tedious, costly, and error-prone in practice. In this paper, we propose to unify active learning (AL) and message passing towards minimizing labeling costs, e.g., making use of few and unreliable labels that can be obtained cheaply. We make two contributions towards that end. First, we open up a perspective by drawing a connection between AL enforcing message passing and social influence maximization, ensuring that the selected samples effectively improve the model performance. Second, we propose an extension to the influence model that incorporates an explicit quality factor to model label noise. In this way, we derive a fundamentally new AL selection criterion for GCN and LP–reliable influence maximization (RIM)–by considering quantity and quality of influence simultaneously. Empirical studies on public datasets show that RIM significantly outperforms current AL methods in terms of accuracy and efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

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Hydrogen-powered smart grid resilience

Energy Conversion and Economics

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Energy Conversion and Economics 2023年第2期4卷 89-104页

作者： Han, Jiayi Wang, Jianxiao He, Zhihao An, Qi Song, Yiyang Mujeeb, Asad Tan, Chin-Woo Gao, Feng Department of Industrial Engineering and Management College of Engineering Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China College of Electrical Engineering Zhejiang University Zhejiang China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Department of Electrical Engineering Tsinghua University Beijing China Department of Civil and Environmental Engineering Stanford University StanfordCA United States

With an increasing frequency of natural disasters and security attacks, the safe and stable operation of smart grid has been challenged unprecedently. To reduce the economic loss and social impact caused by power outage accidents, it is urgent to develop and improve the smart grid technology, and strengthen the disaster resistance and recovery capability of smart grids when faced with extreme events. As an efficient and flexible secondary energy source, hydrogen is crucial in improving the resilience of smart grid and supporting energy security. To further promote the deep integration of hydrogen systems and smart grid and improve the energy system resilience, the resilience of smart grids supported by hydrogen is assessed in this study. First, a technical framework of hydrogen-powered smart grid resilience is established, and the value of hydrogen-powered smart grid resilience is analysed considering different time frames (before, during and after an extreme event) of smart grids facing extreme events. Then, hydrogen-powered smart grid resilience is investigated from perspectives of pre-prevention regulation, in-process correction regulation, and post-recovery regulation. Finally, future direction for hydrogen-powered smart grid resilience is investigated and related policy suggestions are provided. © 2023 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Smart power grids

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TA-MoE: topology-aware large scale mixture-of-expert training 22

TA-MoE: topology-aware large scale mixture-of-expert trainin...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Chang Chen Min Li Zhihua Wu Dianhai Yu Chao Yang Center for Data Science Peking University School of Mathematics Sciences Peking University and National Engineering Laboratory for Big Data Analysis and Applications Peking University Baidu Inc. School of Mathematics Sciences Peking University and National Engineering Laboratory for Big Data Analysis and Applications Peking University and Institute for Computing and Digital Economy Peking University

ISBN: (纸本)9781713871088

Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to fully exploit the underlying heterogeneous network environments. In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology. Based on communication modeling, we abstract the dispatch problem into an optimization objective and obtain the approximate dispatch pattern under different topologies. On top of that, we design a topology-aware auxiliary loss, which can adaptively route the data to fit in the underlying topology without sacrificing the model accuracy. Experiments show that TA-MoE can substantially outperform its counterparts on various hardware and model configurations, with roughly 1.01x-1.61x, 1.01x-4.77x, 1.25x-1.54x improvements over the popular DeepSpeed-MoE, FastMoE and FasterMoE systems.

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