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A Review-Based Reputation Evaluation Approach for Web Services

Journal of Computer Science & Technology 2009年第5期24卷 893-900页

作者：李海华杜小勇 Member 田萱 Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Information Science & Technology Beijing Forestry University Beijing 100083 China

Web services are commonly perceived as an environment of both offering opportunities and threats. In this environment, one way to minimize threats is to use reputation evaluation, which can be computed, for example, through transaction feedback. However, the current feedback-based approach is inaccurate and ineffective because of its inner limitations （e.g., feedback quality problem）. As the main source of feedback, the qualities of existing on-line reviews are often varied greatly from low to high, the main reasons include：（1） they have no standard expression formats, （2） dishonest comments may exist among these reviews due to malicious attacking. Up to present, the quality problem of review has not been well solved, which greatly degrades their importance on service reputation evaluation. Therefore, we firstly present a novel evaluation approach for review quality in terms of multiple metrics. Then, we make a further improvement in service reputation evaluation based on those filtered reviews. Experimental results show the effectiveness and efficiency of our proposed approach compared with the naive feedback-based approaches.

关键词： Web services reputation evaluation review quality measurement

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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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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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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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Representation learning via an integrated autoencoder for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第5期17卷 75-87页

作者： Yi ZHU Xindong WU Jipeng QIANG Yunhao YUAN Yun LI School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China

The purpose of unsupervised domain adaptation is to use the knowledge of the source domain whose data distribution is different from that of the target domain for promoting the learning task in the target *** key bottleneck in unsupervised domain adaptation is how to obtain higher-level and more abstract feature representations between source and target domains which can bridge the chasm of domain ***,deep learning methods based on autoencoder have achieved sound performance in representation learning,and many dual or serial autoencoderbased methods take different characteristics of data into consideration for improving the effectiveness of unsupervised domain ***,most existing methods of autoencoders just serially connect the features generated by different autoencoders,which pose challenges for the discriminative representation learning and fail to find the real cross-domain *** address this problem,we propose a novel representation learning method based on an integrated autoencoders for unsupervised domain adaptation,called *** capture the inter-and inner-domain features of the raw data,two different autoencoders,which are the marginalized autoencoder with maximum mean discrepancy(mAE)and convolutional autoencoder(CAE)respectively,are proposed to learn different feature *** higher-level features are obtained by these two different autoencoders,a sparse autoencoder is introduced to compact these inter-and inner-domain *** addition,a whitening layer is embedded for features processed before the mAE to reduce redundant features inside a local *** results demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.

关键词： unsupervised domain adaptation representation learning marginalized autoencoder convolutional autoen-coder sparse autoencoder

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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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Effective density queries for moving objects in road networks

Effective density queries for moving objects in road network...

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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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Who knows more? The role of structural hole spanners in accurate information identification on social media

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Pacific-Basin Finance Journal 2025年

作者： Man Guo Wen Long Yanqiang Zhong Wei Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 PR China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 PR China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 PR China Department of Information Systems and Management Engineering Southern University of Science and Technology Shenzhen 518055 PR China

Based on structural hole theory, this study explores the differences in the ability of Chinese social media users with different characteristics to provide accurate information. Using over 20 million interactive data from 2.19 million users, we construct a social network and identify structural hole spanners and ordinary users. As key players in a network, structural hole spanners are typically located at the intersections of different groups or information sources. They differ from ordinary users in the way they gather and disseminate information. The empirical results indicate that the accuracy of market judgements by structural hole spanners is at least 12.38 % higher than that of ordinary users. Strategy simulations show that using information from structural hole spanners to make investment decisions can achieve a 116 % cumulative return during the backtesting period, nearly four times that of ordinary users. This information advantage occurs primarily because opinion distance and information uniqueness play significant roles in improving information accuracy. This study not only helps deepen understanding of information dissemination characteristics among heterogeneous users on social media but also provides empirical support for the application of structural hole theory in finance, with important theoretical and practical implications.

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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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DB Facade: A web cache with improved data freshness

DB Facade: A web cache with improved data freshness

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2nd International Symposium on Electronic Commerce and Security, ISECS 2009

作者： Xiongpai, Qin Zhou, Xiaoyun Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information and Electrical Engineering China University of Mining Technology Xuzhou Jiangsu 221008 China

ISBN: (纸本)9780769536439

Traditional web database cache techniques have a major disadvantage, namely poor data freshness, because they employ an asynchronous data refresh strategy. A novel web database cache, DB Façade, is proposed in this paper. DB Facade uses a main memory database to cache result sets of previous queries for subsequent reusing. Updates on backend database system are managed by delta tables, and then propagated to web database cache in a near real-time manner, hence guarantee the freshness of data. DB Facade offloads query burden from backend database systems, and exploits the power of main memory database system to boost query performance. TPC-W testing result shows that the system's performance increases by about 17 percent. © 2009 IEEE.

关键词： Facades

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