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2010 7th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2010

作者： Zhang, XuePing Zhu, YanXia Hua, Nan College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou 350002 China Technology Department Zhengzhou SuperHW Network Technology Co. Ltd. Zhengzhou 450000 China

ISBN: (纸本)9781424459346

Based on FP-tree algorithm, this article introduced the method of multi-thread processing and a Multi-Threaded Paralleled frequent item-set mining Algorithm - MTPA was proposed .It has been applied to an enterprise human resources management system. Through the experiments of paralleled mining by using increasing multi-thread processing, it has been proved that MTPA which on the condition of multi-core processors can improve the efficiency of frequent item-set mining effectively. ©2010 IEEE.

关键词： data mining

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Design and implementation of WPKI-based Gas station online payment system

Design and implementation of WPKI-based Gas station online p...

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Chinese Control Conference (CCC)

作者： Jian Wang Yanheng Liu Tianzhe Dong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun P. R. China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun P. R. China

Through analyzing the security problems which exist in general on-line payment systems, this paper proposes a scheme based on WPKI (Wireless Public key Infrastructure) and further designs and implements a gas station on-line payment system. By using WPKI technology which includes SSL (Secure Sockets Layer) protocol, digital signature and digital certificate, the designed on-line payment system can achieve data privacy, authenticity, integrity and non-repudiation during on-line payment process.

关键词： Servers Protocols Authentication Business Encryption Public key

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A Stable Stochastic Optimization Algorithm for Triangulation of Bayesian Networks

A Stable Stochastic Optimization Algorithm for Triangulation...

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International Workshop on knowledge Discovery and data Mining (WKDD)

作者： Xuchu Dong Dantong Ouyang Yuxin Ye Shasha Feng Haihong Yu Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun Jilin China

ISBN: (纸本)9781424453979

In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover operator and a 2-fold mutation mechanism utilizing the MinFillWeight heuristic. Experiments on representative benchmark show that the deterministic heuristic and the stochastic algorithm have good performance and stability to various problems.

关键词： Stochastic processes Bayesian methods Tree graphs Ethics Genetic algorithms Inference algorithms Genetic mutations Stability Ant colony optimization data mining

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A Multi-heuristic Cooperative Ant Colony System for Optimizing Elimination Ordering of Bayesian Networks

A Multi-heuristic Cooperative Ant Colony System for Optimizi...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xuchu Dong Dantong Ouyang Yuxin Ye Haihong Yu Yonggang Zhang Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun Jilin China

To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will be identified and be reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a better performance than other swarm intelligence methods.

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Efficient Common Items Extraction from Multiple Sorted Lists

Efficient Common Items Extraction from Multiple Sorted Lists

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International Asia-Pacific Web Conference (APWEB)

作者： Wei Lu Chuitian Rong Jinchuan Chen Xiaoyong Du Gabriel Pui Cheong Fung Xiaofang Zhou Ministry of Education Key Laboratories of Data Engineering and Knowledge Engineering China School of Information Renmin University of China China School of Computing Informatics Arizona State University USA School of ITEE University of Queensland Australia

Given a set of lists, where items of each list are sorted by the ascending order of their values, the objective of this paper is to figure out the common items that appear in all of the lists efficiently. This problem is sometimes known as common items extraction from sorted lists. To solve this problem, one common approach is to scan all items of all lists sequentially in parallel until one of the lists is exhausted. However, we observe that if the overlap of items across all lists is not high, such sequential access approach can be significantly improved. In this paper, we propose two algorithms, MergeSkip and MergeESkip, to solve this problem by taking the idea of skipping as many items of lists as possible. As a result, a large number of comparisons among items can be saved, and hence the efficiency can be improved. We conduct extensive analysis of our proposed algorithms on one real dataset and two synthetic datasets with different data distributions. We report all our findings in this paper.

关键词： data mining data engineering knowledge engineering Informatics Algorithm design and analysis Computer science Application software Information retrieval

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Ontology-assisted schema matching for deep web query interfaces

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Journal of Information and Computational Science 2010年第2期7卷 543-549页

作者： Wang, Ying Zuo, Wanli Wang, Xin Zhang, Aiqi Peng, Tao College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Computation and Knowledge Engineering Ministry of Education China College of Software Changchun Institute of Technology Changchun 130012 China

Schema matching is important for schema integration and thus has got great attention. In this paper, we present an ontology-based algorithm to match a larger number of interface schemas, which can hand both simple and complex mappings of attributes. Firstly, construct a core ontology and the semantics of concepts in the core ontology can be enriched during schema matching. Secondly, design the ontology-assisted schema matching algorithmic for deep web query interface. Lastly, capture the matching relationships between source schema and target schema using domain-specific ontology. During the schema matching of deep web entries, ontology is the ″bridging″ which is used to obtain the mapping relationships between different query interfaces. Experiment result shows that this method is feasible and effective. Copyright © 2010 Binary Information Press.

关键词： Ontology

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Mining data records based on ontology evolution for deep web

Mining data records based on ontology evolution for deep web

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International Conference on Computer engineering and Technology, ICCET

作者： Wen Zhang Kerui Chen Fan Zhang College of Information Engineering Zhongzhou University Zhengzhou China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9781424463473;9781424463497;9781424463503

Currently, the research for the extraction of information in deep web is pretty active. Although many researchers already adopted ontology in the data extraction, many problems still exist. This paper proposed an ontology evolution based method for mining in the data area. Not only will this method solve the problem when the website only consists of one record, but it also can identify he meaning of data that has no labels. With the evolution of ontology, the extraction of data records is being more accurate. Experiments indicate that this method could improve the accuracy and efficiency of data extraction.

关键词： data mining Ontologies Entropy educational institutions knowledge engineering Web pages data engineering Random processes Computer science educational technology

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Heterogeneous deep web data extraction using ontology evolution

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Journal of Convergence Information Technology 2010年第8期5卷 23-23页

作者： Chen, Kerui Zhao, Jinchao Zuo, Wanli He, Fengling Chen, Yongheng College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450002 China

This paper proposed a complex ontology evolution based method of extracting data, and also completely designed an extraction system, which consists of four important components: Resolver, Extractor, Consolidator and the ontology construction components. The system gives priority to the construction of mini-ontology. When the user submits query keywords to the deep web query interface, the returned result will pass through the prior three components;after that, the final execution result will be returned to user in a unified form. This paper adopted an extraction method that is different from the general ontology extraction. More specifically, the ontology used in extraction here is dynamic evolution, which can adapt various data source better. Experimental results proved that this method could effectively extract the data in the query result pages.

关键词： Ontology

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Adaptive feature selection and extraction approaches for image retrieval based on region

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Journal of Multimedia 2010年第1期5卷 85-92页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Computer Graphics and Image Processing Dalian Nationalities University Dalian China State Key Laboratory of CAD and CG Zhejiang University Hangzhou China

Image retrieval based on region is one of the most promising and active research directions in recent year's CBIR, while region segmentation, feature selection and feature extraction of region are key issues. However, the existing approaches always adopt a uniform approach of segmentation and feature extraction for all images in the same system. In this paper, we propose adaptive image segmentation and feature extraction approach according to different category image for image retrieval system. To improve performance, we propose adaptive segmentation approach according to different category image. Textured image is segmented by Gaussian Mixture Models (GMM), while non-textured image is segmented by our proposed block-based normalized cut. To accurately describe feature of region, we propose weight assignment method for centroid pixel and its neighbor by convolution with normal distribution when image segmentation by GMM. To improve generalization, we propose adaptive number of Fourier descriptors of shape signature which depends on the energy distribution of Fourier descriptors, instead of fixed number by experience. To simply and efficiently describe the spatial relationships of multi-object or multi-region in same image, we apply simplified topological relationships. The experiments demonstrate that proposed approaches are superior to the traditional approaches. © 2010 ACADEMY PUBLISHER.

关键词： Feature extraction

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Local Methods for Estimating SimRank Score

Local Methods for Estimating SimRank Score

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International Asia-Pacific Web Conference (APWEB)

作者： Xu Jia Hongyan Liu Li Zou Jun He Xiaoyong Du Yuanzhe Cai Ministry of Education Key Laboratories of Data Engineering and Knowledge Engineering China Department of Computer Science Renmin University of China China Department of Management Science and Engineering Tsinghua University China Department of Computer Science University of Texas Arlington TX USA

SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm “Local-SimRank”. The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically.

关键词： Computer science Application software Web pages data engineering knowledge engineering Algorithm design and analysis Large-scale systems Computer science education Conference management knowledge management

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