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9th Asia-Pacific Web Conference/8th International Conference on Web-Age Information Management (APWeb/WAIM 2007)

作者： Lai, Caifeng Wang, Ling Chen, Jidong Meng, Xiaofeng Zeitouni, Karine School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE PRISM Versailles Saint-Quentin University France

ISBN: (纸本)9783540724834

Recent research has focused on density queries for moving objects in highly dynamic scenarios. An area is dense if the number of moving objects it contains is above some threshold. Monitoring dense areas has applications in traffic control systems, bandwidth management, collision probability evaluation, etc. All existing methods, however, assume the objects moving in the Euclidean space. In this paper, we study the density queries in road networks, where density computation is determined by the length of the road segment and the number of objects on it. We define an effective road-network density query guaranteeing to obtain useful answers. We then propose the cluster-based algorithm for the efficient computation of density queries for objects moving in road networks. Extensive experimental results show that our methods achieve high efficiency and accuracy for finding the dense areas in road networks. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Object recognition

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Multi-view Feature Learning for the Over-penalty in Adversarial Domain Adaptation

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data Intelligence 2024年第1期6卷 183-200页

作者： Yuhong Zhang Jianqing Wu Qi Zhang Xuegang Hu School of Computer and Information Engineering Hefei University of TechnologyHefei 230601China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) The Ministry of Education of ChinaHefei 230009China

Domain adaptation aims to transfer knowledge from the labeled source domain to an unlabeled target domain that follows a similar but different ***,adversarial-based methods have achieved remarkable success due to the excellent performance of domain-invariant feature presentation ***,the adversarial methods learn the transferability at the expense of the discriminability in feature representation,leading to low generalization to the target *** this end,we propose a Multi-view Feature Learning method for the Over-penalty in Adversarial Domain ***,multi-view representation learning is proposed to enrich the discriminative information contained in domain-invariant feature representation,which will counter the over-penalty for discriminability in adversarial ***,the class distribution in the intra-domain is proposed to replace that in the inter-domain to capture more discriminative information in the learning of transferrable *** experiments show that our method can improve the discriminability while maintaining transferability and exceeds the most advanced methods in the domain adaptation benchmark datasets.

关键词： domain adaptation adversarial learning multi-view learning

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Hierarchical All-Pairs SimRank Calculation 28th

Hierarchical All-Pairs SimRank Calculation

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Zhang, Liangfu Li, Cuiping Zhang, Xue Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education School of Information Renmin University of China Beijing China

ISBN: (纸本)9783031306747

All-pairs SimRank calculation is a classic SimRank problem. However, all-pairs algorithms suffer from efficiency issues and accuracy issues. In this paper, we convert the non-linear simrank calculation into a new simple closed formulation of linear system. And we come up with a sequence of novel algorithms to efficiently solve the linear system with accuracy guarantees. To reduce the memory consumption and improve the computational efficiency, we build a hierarchical framework to calculate the all-pairs SimRank scores, which includes locally coarse calculation and globally refine calculation. We first solve the local linear systems generated from the subgraphs, then we refine the SimRank scores on the full graph from the residuals of the local structures. We also show that our algorithms outperform the state-of-the-art all-pairs SimRank computation algorithms on real graphs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational efficiency

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Classifier Belief Optimization for Visual Categorization 27th

Classifier Belief Optimization for Visual Categorization

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Yang, Gang Li, Xirong Key Lab of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783030678319

Classifier belief represents the confidence of a classifier making judgment about a special instance. Based on classifier belief, we propose an approach to realize classifier belief optimization. Through enriching prior knowledge and thus reducing the scope of candidate classes, our approach improves classification accuracy. A feature perturbation strategy containing an objective optimization is developed to automatically generate labeled instances. Moreover, we propose a classifier consensus strategy (CCS) for classifier optimization. CCS enables a given classifier to take full advantage of the test data to enrich prior knowledge. Experiments on three benchmark datasets and three classical classifiers justify the validity of the proposed approach. We improve the classification accuracy of a linear SVM by 6%. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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Utility-based anonymisation for dataset with multiple sensitive attributes

Utility-based anonymisation for dataset with multiple sensit...

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作者： Wang, Lixia Zhu, Qing Key Laboratory for Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing100872 China

Privacy-preserving data publication problem has attracted more and more attentions in recent years. A lot of related research works have been done towards dataset with single sensitive attribute. However, usually, original dataset contains more than one sensitive attribute. In this paper, we apply k-anonymity principle to solve the data publication problem for dataset with multiple sensitive attributes. We first cluster sensitive values based on a utility matrix. Then, we use a greedy strategy to partition tuples into equivalence classes. Our method can guarantee that the size of equivalence class is k except the last one, which reduces information loss. Also, we can guarantee the diversity of sensitive value in an equivalence class, which can protect privacy against the homogeneity attack. Experiments on a real dataset show that our method performs well on information loss, which indicates that we can guarantee data utility while protecting personal privacy. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Equivalence classes

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Fine-Grained Generation for Zero-Shot Learning 27th

Fine-Grained Generation for Zero-Shot Learning

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Sun, Weimin Xu, Jieping Yang, Gang Key Lab of Data Engineering and Knowledge Engineering Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9783030678319

Although zero-shot learning (ZSL) has achieved success in recognizing unseen classes images, most previous studies focus on feature projection from one domain to another, neglecting the domain shift problem caused by differences in classes. In this paper, we propose two novel methods in terms of attributes and features for ZSL problem. The first is Sample-Level Attribute Generation via GAN (SLA-GAN) that generates sample-level attributes. Considering the influence of lighting, environment, weather and other factors, the description based on class-level attributes will be biased. SLA-GAN generates sample-level attributes to describe each sample. Specially, we train a conditional Wasserstein GAN in which the generator synthesizes sample-level attributes from features and the discriminator distinguishes the sample-level from class-level attributes. Apart from that, we propose a novel method Fine-Grained Sample Generation via GAN (FGS-GAN) which can generate the unseen samples from the most similar samples of seen classes. Finally, we replace the class-level attributes with sample-level attributes in FGS-GAN and analyze the effects of sample-level attributes on synthesized features. Experiments on five benchmark data set show that our models outperform state-of-the-art methods in ZSL. © 2021, Springer Nature Switzerland AG.

关键词： Zero-shot learning

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Simulation of main memory database parallel recovery 09

Simulation of main memory database parallel recovery

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2009 Spring Simulation Multiconference, SpringSim 2009

作者： Qin, Xiongpai Cao, Wei Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China MOE Beijing100872 China

The paper describes the details of using J-SIM in main memory database parallel recovery simulation. In update intensive main memory database systems, I/O is still the dominant performance bottleneck. A proposal of parallel recovery scheme for large-scale update intensive main memory database systems is presented. Simulation provides a faster way of evaluating the new idea compared to actual system implementation. J-SIM is an open source discrete time simulation software package. The simulation implementation using J-SIM is elaborated in terms of resource modeling, transaction processing system modeling and workload modeling. Finally, with simulation results analyzed, the effectiveness of the parallel recovery scheme is verified and the feasibility of J-SIM's application in main memory database system simulation is demonstrated.

关键词： Recovery

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Lightweight data mining based database system self-optimization: A case study

Lightweight data mining based database system self-optimizat...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Qin, Xiongpai Cao, Wei Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China MOE Beijing 100872 China

ISBN: (纸本)9783642006715

With the system becoming more complex and workloads becoming more fluctuating, it is very hard for DBA to quickly analyze performance data and optimize the system, self optimization is a promising technique. A data mining based optimization scheme for the lock table in database systems is presented. After trained with performance data, a neural network become intelligent enough to predict system performance with newly provided configuration parameters and performance data. During system running, performance data is collected continuously for a rule engine, which chooses the proper parameter of the lock table for adjusting, the rule engine relies on the trained neural network to precisely provide the amount of adjustment. The selected parameter is adjusted accordingly. The scheme is implemented and tested with TPC-C workload, system throughput increases by about 16 percent. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Locks (fasteners)

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

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Journal of Computer Science & Technology 2021年第4期36卷 741-761页

作者： Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China

Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are performed artificially(e.g.,by database administrators),but it is hard for them to achieve high performance through tuning in various types of systems and in various *** recent years,there have been some studies on tuning traditional database systems,but all these methods have some *** this article,we put forward a tuning system based on attention-based deep reinforcement learning named WATuning,which can adapt to the changes of workload characteristics and optimize the system performance efficiently and ***,we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of *** algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems,and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate ***,WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of ***,WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make *** experimental results show that the throughput and the latency of WATuning are improved by 52.6%and decreased by 31%,respectively,compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.

关键词： attention mechanism auto-tuning system reinforcement learning(RL) workload-aware

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