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42nd International Conference on Very Large data Bases, VLDB 2016

作者： Miao, Xiaoye Gao, Yunjun Chen, Gang Cui, Huiyong Guo, Chong Pan, Weida College of Computer Science Zhejiang University Hangzhou China The Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China

The incomplete data is universal in many real-life applications due to data integration, the limitation of devices, etc. In this demonstration, we present SI2P, a restaurant recommendation System with Preference queries on Incomplete Information. SI2P is capable of friendly recommending desirable restaurants based on preference queries that take the incomplete ratings information into consideration. It adopts the browser-server model, and incorporates three functionality modules including friendly and convenient query submission, flexible and useful result explanation, timely and incremental dataset interaction. SI2P provides the server side based on an extended PostgreSQL database that integrates two types of preference queries, namely, skyline and top-k dominating queries over incomplete data. It also offers the browser-based interface for the users to interact with the system. Using a real restaurant dataset from TripAdvisor, we demonstrate SI2P can recommend and explore the restaurants in a friendly way. © 2016 VLDB.

关键词： Query processing

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OnlineCM: Real-time consensus classification with missing values

OnlineCM: Real-time consensus classification with missing va...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Dong, Bowen Xie, Sihong Gao, Jing Fan, Wei Yu, Philip S. University of Illinois at Chicago ChicagoIL United States Department of Computer Science University at Buffalo BuffaloNY United States Baidu Big Data Lab. SunnyvaleCA United States Institute for Data Science Tsinghua University Beijing China

ISBN: (纸本)9781510811522

Combining predictions from multiple sources or models has been shown to be a useful technique in data mining. For example, in network anomaly detection, multiple detectors' output have to be combined to obtain the diagnostic decisions. Unfortunately, as data are generated at an increasingly high speed, existing prediction aggregation methods are facing new challenges. First, the high velocity and hugh volume of the data render existing batch mode prediction aggregation algorithms infeasible. Second, due to the heterogeneity, predictions from multiple models or data sources might not be perfectly synchronized, leading to abundant missing values in the prediction stream. We propose OnlineCM, short for Online Consensus Maximization, to address the above challenges. OnlineCM keeps only a minimal yet sufficient footprint for both consensus prediction and missing value imputation over the prediction stream. In particular, we show that the correlations among base models or data sources are sufficient for effective consensus prediction, require small storage and can be updated in an online fashion. Further, we identify a reinforcing relationship between missing value imputation and the consensus predictions, leading to a novel consensus-based missing values imputation method, which in turn makes model correlation estimation more accurate. Experiments demonstrates that OnlineCM achieves aggregated predictions that has close performance to the batch mode consensus maximization algorithm, and outperforms baseline methods significantly in 4 large real world datasets. Copyright © SIAM.

关键词： Forecasting

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A novel method for road centerline extraction from high spatial resolution satellite images

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Journal of Information Hiding and Multimedia Signal Processing 2016年第5期7卷 1069-1080页

作者： Xu, Rui Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No.523 Gongye Road Fuzhou350002 China College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China

An integrated method for road centerline extraction has been proposed in this paper which consists of four main steps. First, the Graph Cut algorithm using proposed similarity function is employed to segment the image, and the segmented image is thresholded to initial binary skeleton. Second, the road skeleton is refined by using a series of morphology operations and shape features. Third, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally,a regression method is performed to extract smooth and accurate road centerlines. Validation tests show that the proposed method is able to achieve a comparatively good performance in road centerline extraction from high spatial resolution satellite images. © 2016, Ubiquitous International. All Rights Reserved.

关键词： Regression analysis

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A hierarchical object-based method for suburban road extraction from remote sensing image

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2671-2676页

作者： Xu, Rui College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No. 523 Gongye Road Fuzhou350002 China

Road extraction from remote sensing image is a very popular topic. There are many methods dealing with this problem, but few of them focus on suburban roads. This paper presents a hierarchical object-based method for suburban road extraction. First, the image is preprocessed by the Normalized Difference Vegetation Index (NDVI) and the Hue-Saturation-Intensity (HSI) Index. Vegetation regions, shadows and water areas are then removed. Second, texture features are applied to classifying the preprocessed image into two categories: road and non-road. Homogeneous property is combined to remove false classification so as to generate initial road skeleton. Third, the road skeleton is refined by using a series of morphology operations and shape features. Fourth, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally, a regression method is performed to extract smooth and accurate road centerlines. The experimental results indicate that the proposed method is suitable for suburban road extraction. © 2016 ISSN 2185-2766.

关键词： Vegetation

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Personalized TV recommendation with mixture probabilistic matrix factorization

Personalized TV recommendation with mixture probabilistic ma...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Li, Huayu Zhu, Hengshu Ge, Yong Fu, Yanjie Ge, Yuan Computer Science Department UNC Charlotte China Baidu Research-Big Data Lab. United States Rutgers University United States Anhui Polytechnic University China

ISBN: (纸本)9781510811522

With the rapid development of smart TV industry, a large number of TV programs have been available for meeting various user interests, which consequently raise a great demand of building personalized TV recommender systems. Indeed, a personalized TV recommender system can greatly help users to obtain their preferred programs and assist TV and channel providers to attract more audiences. While different methods have been proposed for TV recommendations, most of them neglect the mixture of watching groups behind an individual TV. In other words, there may be different groups of audiences at different times in front of a TV. For instance, watching groups of a TV may consist of children, wife and husband, husband, wife, etc in many US household. To this end, in this paper, we propose a Mixture Probabilistic Matrix Factorization (mPMF) model to learn the program preferences of televisions, which assumes that the preference of a given television can be regarded as the mixed preference of different watching groups. Specifically, the latent vector of a television is drawn from a mixture of Gaussian and the mixture number is the estimated number of watching groups behind the television. To evaluate the proposed mPMF model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on a real-world data set. The experimental residís clearly demonstrate the effectiveness of our model. Copyright © SIAM.

关键词： Mixtures

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Convex Matrix Completion: A trace-ball optimization perspective

Convex Matrix Completion: A trace-ball optimization perspect...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Zeng, Guangxiang Luo, Ping Chen, Enhong Xiong, Hui Zhu, Hengshu Liu, Qi University of Science and Technology of China China Institute of Computing Technology CAS China Rutgers University United States Baidu Research-Big Data Lab. United States

ISBN: (纸本)9781510811522

The problem of Matrix Completion (MC) refers to the process of adding entries for unknown or missing values in a matrix. In this paper, we study the convex matrix completion problem in the form of trace norm bounding. Specifically, we propose a robust solution for this problem based on trace-ball optimization, which can creatively change the original trace norm constraint into the problem of low-rank matrix factorization. Therefore, by searching in a ball space defined by the new trace constraint, the rank of new matrix can be self-determined such that the local minimum for matrix factorization is the global minimum for the original matrix completion task. Meanwhile, we define a free parameter γ to control the model complexity of our approach in terms of how well it fits the training data. Particularly, we identify a value of γb, which is the minimal value of the trace norm, in a way such that the model can exactly fit the known entries in the matrix. Furthermore, we also empirically reveal an important property of our approach: that is, a variable η∗ generated by γ is always stable with the increase of the amount of training data. This can help to speed up the tuning of optimal parameters for large matrices. Finally, extensive experiments on several real-world datasets clearly validate the effectiveness of the proposed approach. Copyright © SIAM.

关键词： Matrix algebra

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Robust latent poisson deconvolution from multiple imperfect features for web topic detection

Robust latent poisson deconvolution from multiple imperfect ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Fei Tao Junbiao Pang Chunjie Zhang Liang Li Li Su Weigang Zhang Qingming Huang Guiping Su School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China College of Metropolitan Transportation Beijing University of Technology China Key Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781467372596

In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-Nearest Neighbor Similarity Graph (k-N 2 SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.

关键词： Visualization Deconvolution Robustness Feature extraction Optimization Correlation Noise measurement

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Exploring the impact of dynamic mutual influence on social event participation

Exploring the impact of dynamic mutual influence on social e...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Xu, Tong Zhong, Hao Zhu, Hengshu Xiong, Hui Chen, Enhong Liu, Guannan University of Science and Technology of China China Rutgers the State University of New Jersey United States Baidu Research - Big Data Lab. United States Tsinghua University China

ISBN: (纸本)9781510811522

Nowadays, it is commonly seen that an offline social event is organized through online social network services (SNS), in this way cyber strangers can be connected in physical world. While there are some preliminary studies on social event participation through SNS, they usually have more focus on the mining of event profiles and have less focus on the social relationships among target users. In particular, the importance of dynamic mutual influence among potential event participants has been largely ignored. In this paper, we develop a novel discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a variant two-stage discriminant framework to capture the users' preferences as well as their latent social connections. The experimental results on real-world data show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines, which validates the hypothesis that dynamic mutual influence could play an important role in the decisionmaking process of social event participation. Copyright © SIAM.

关键词： Social networking (online)

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Local Interactions and the Emergence of a Twitter Small-World Network

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Social Networking 2015年第2期4卷 33-40页

作者： Eugene Ch’ng School of Computer Science Big Data and Visual Analytics Lab University of Nottingham Ningbo China Ningbo China

The small-world phenomenon is found in many self-organising systems. Systems configured in small-world networks spread information more easily than in random or regular lattice-type networks. Whilst it is a known fact that small-world networks have short average path length and high clustering coefficient in self-organising systems, the ego centralities that maintain the cohesiveness of small-world network have not been formally defined. Here we show that instantaneous events such as the release of news items via Twitter, coupled with active community arguments related to the news item form a particular type of small-world network. Analysis of the centralities in the network reveals that community arguments maintain the small-world network whilst ac-tively maintaining the cohesiveness and boundary of the group. The results demonstrate how an active Twitter community unconsciously forms a small-world network whilst interacting locally with a bordering community. Over time, such local interactions brought about the global emergence of the small-world network, connecting media channels with human activities. Understanding the small-world phenomenon in relation to online social or civic movement is important, as evident in the spate of online activists that tipped the power of governments for the better or worst in recent times. The support, or removal of high centrality nodes in such networks has important ramifications in the self-expression of society and civic discourses. The presentation in this article anticipates further exploration of man-made self-organising systems where a larger cluster of adhoc and active community maintains the overall cohesiveness of the network.

关键词： Social Network Analysis Social Movement Community #Freejahar Small-World Networks

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Exploring data science in IT and quantitative management: Preface for ITQM 2015

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Procedia Computer science 2015年 55卷 1-7页

作者： Gomes, Luiz Flavio Autran Monteiro Shi, Yong Colcher, Raul Wolcott, Peter Herrera-Viedma, Enrique Ibmec/RJ Av. Presidente Wilson 118 11th floor Rio de Janeiro RJ Brazil Research Center on Fictitiotus Economy and Data Science Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha OmahaNE United States ASSESPRO Brazil DECSAI C/ Periodista Daniel Saucedo Aranda University of Granada Granada Spain

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