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  • 1
    Publication Date: 2012-11-08
    Description:    Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been done in discovering frequent patterns in uncertain data streams. The current solutions for frequent pattern mining in uncertain streams take a FP-tree-based approach; however, recent studies have shown that FP-tree-based algorithms do not perform well in the presence of data uncertainty. In this paper, we propose two hyper-structure-based false-positive-oriented algorithms to efficiently mine frequent itemsets from streams of uncertain data. The first algorithm, UHS-Stream, is designed to find all frequent itemsets up to the current moment. The second algorithm, TFUHS-Stream, is designed to find frequent itemsets in an uncertain data stream in a time-fading manner. Experimental results show that the proposed hyper-structure-based algorithms outperform the existing tree-based algorithms in terms of accuracy, runtime, and memory usage. Content Type Journal Article Category Regular Paper Pages 1-26 DOI 10.1007/s10115-012-0581-y Authors Chandima HewaNadungodage, Department of Computer and Information Science, Indiana University—Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA Yuni Xia, Department of Computer and Information Science, Indiana University—Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA Jaehwan John Lee, Department of Electrical and Computer Engineering, Indiana University—Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202-5132, USA Yi-cheng Tu, Department of Computer Science and Engineering, University of South Florida, 4202 E. Fowler Ave., ENB 118, Tampa, FL 33620, USA Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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    Topics: Computer Science
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  • 2
    Publication Date: 2012-09-25
    Description:    Multi-task learning (MTL) aims to enhance the generalization performance of supervised regression or classification by learning multiple related tasks simultaneously. In this paper, we aim to extend the current MTL techniques to high dimensional data sets with structured input and structured output (SISO), where the SI means the input features are structured and the SO means the tasks are structured. We investigate a completely ignored problem in MTL with SISO data: the interplay of structured feature selection and task relationship modeling. We hypothesize that combining the structure information of features and task relationship inference enables us to build more accurate MTL models. Based on the hypothesis, we have designed an efficient learning algorithm, in which we utilize a task covariance matrix related to the model parameters to capture the task relationship. In addition, we design a regularization formulation for incorporating the structured input features in MTL. We have developed an efficient iterative optimization algorithm to solve the corresponding optimization problem. Our algorithm is based on the accelerated first order gradient method in conjunction with the projected gradient scheme. Using two real-world data sets, we demonstrate the utility of the proposed learning methods. Content Type Journal Article Category Regular Paper Pages 1-20 DOI 10.1007/s10115-012-0543-4 Authors Hongliang Fei, EECS Department, University of Kansas, Lawrence, KS, USA Jun Huan, EECS Department, University of Kansas, Lawrence, KS, USA Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 3
    Publication Date: 2012-09-25
    Description:    We propose in this paper a new approach for bootstrapping trust of Web services in which the interactions of a Web service with a user are observed during a certain time frame. The observations sequence is modeled as a Hidden Markov Model and matched against pre-defined trust patterns in order to assess the behavior of such Web service. The pre-defined trust patterns are specifications of possible behaviors of Web services such as trusted, malicious, betraying, oscillating, and redemptive. Based on the matching result, an initial trust value is assigned to the Web service. Our experimental results show that our approach enjoys good precision and recall values and provides a fair distribution of trust values. Besides, the proposed approach is applied on a dataset of real-world Web services. A comparative study with published bootstrapping approaches shows a better bootstrapping success rate for our new approach. Content Type Journal Article Category Regular Paper Pages 1-28 DOI 10.1007/s10115-012-0554-1 Authors Hamdi Yahyaoui, Computer Science Department, Kuwait University, 5969, Safat,  13060 State of Kuwait Sami Zhioua, Information and Computer Sciences Department, KFUPM 958, Dhahran,  31261 KSA Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 4
    Publication Date: 2012-09-22
    Description:    A recent surge of participatory web and social media has created a new laboratory for studying human relations and collective behavior on an unprecedented scale. In this work, we study the predictive power of social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes a product, whether she would like to vote for a presidential candidate, etc. Since an actor is likely to participate in multiple different communities with each regulating the actor’s behavior in varying degrees, and a natural hierarchy might exist between these communities, we propose to zoom into a network at multiple different resolutions and determine which communities reflect a targeted behavior. We develop an efficient algorithm to extract a hierarchy of overlapping communities. Empirical results on social media networks demonstrate the promising potential of the proposed approach in real-world applications. Content Type Journal Article Category Short Paper Pages 1-19 DOI 10.1007/s10115-012-0555-0 Authors Xufei Wang, Computer Science and Engineering, Arizona State University, Tempe, AZ, USA Lei Tang, Advertising Sciences, Walmart Labs, Santa Clara, CA, USA Huan Liu, Computer Science and Engineering, Arizona State University, Tempe, AZ, USA Lei Wang, School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 5
    Publication Date: 2012-09-27
    Description:    This paper proposes a novel approach based on the planning-graph to solve the top-k QoS-aware automatic composition problem of semantic Web services. The approach includes three sequential stages: a forward search stage to generate a planning-graph to reduce the search space of the following two stages greatly, an optimal local QoS calculating stage to compute all the optimal local QoS values of services in the planning, and a backward search stage to find the top-K composed services with optimal QoS values according to the planning-graph and the optimal QoS value. In order to validate the approach, experiments are carried out based on the test sets offered by the WS-Challenge competition 2009. The results show that the approach can find the same optimal solution as the champion system from the competition but also can provide more alternative solutions with the optimal QoS for users. Content Type Journal Article Category Regular Paper Pages 1-27 DOI 10.1007/s10115-012-0541-6 Authors Shuiguang Deng, College of Computer Science and Technology, Zhejiang University, Hang Zhou, 310012 China Bin Wu, College of Computer Science and Technology, Zhejiang University, Hang Zhou, 310012 China Jianwei Yin, College of Computer Science and Technology, Zhejiang University, Hang Zhou, 310012 China Zhaohui Wu, College of Computer Science and Technology, Zhejiang University, Hang Zhou, 310012 China Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 6
    Publication Date: 2012-10-13
    Description:    Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled. Content Type Journal Article Category Regular Paper Pages 1-38 DOI 10.1007/s10115-012-0569-7 Authors Alejandro Torreño, Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain Eva Onaindia, Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain Óscar Sapena, Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 7
    Publication Date: 2012-10-13
    Description:    Web service recommendation has become a hot yet fundamental research topic in service computing. The most popular technique is the Collaborative Filtering (CF) based on a user-item matrix. However, it cannot well capture the relationship between Web services and providers. To address this issue, we first design a cube model to explicitly describe the relationship among providers, consumers and Web services. And then, we present a Standard Deviation based Hybrid Collaborative Filtering (SD-HCF) for Web Service Recommendation ( WSRec ) and an Inverse consumer Frequency based User Collaborative Filtering (IF-UCF) for Potential Consumers Recommendation ( PCRec ). Finally, the decision-making process of bidirectional recommendation is provided for both providers and consumers. Sets of experiments are conducted on real-world data provided by Planet-Lab. In the experiment phase, we show how the parameters of SD-HCF impact on the prediction quality as well as demonstrate that the SD-HCF is much better than extant methods on recommendation quality, including the CF based on user, the CF based on item and general HCF. Experimental comparison between IF-UCF and UCF indicates the effectiveness of adding inverse consumer frequency to UCF. Content Type Journal Article Category Regular Paper Pages 1-21 DOI 10.1007/s10115-012-0562-1 Authors Jie Cao, Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China Zhiang Wu, Jiangsu Provincial Key Laboratory of E-Business, Nanjing University of Finance and Economics, Nanjing, China Youquan Wang, College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China Yi Zhuang, College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 8
    Publication Date: 2012-10-11
    Description:    The main aim of this paper is to propose an efficient and novel Markov chain-based multi-instance multi-label (Markov- Miml ) learning algorithm to evaluate the importance of a set of labels associated with objects of multiple instances. The algorithm computes ranking of labels to indicate the importance of a set of labels to an object. Our approach is to exploit the relationships between instances and labels of objects. The rank of a class label to an object depends on (i) the affinity metric between the bag of instances of this object and the bag of instances of the other objects, and (ii) the rank of a class label of similar objects. An object, which contains a bag of instances that are highly similar to bags of instances of the other objects with a high rank of a particular class label, receives a high rank of this class label. Experimental results on benchmark data have shown that the proposed algorithm is computationally efficient and effective in label ranking for MIML data. In the comparison, we find that the classification performance of the Markov- Miml algorithm is competitive with those of the three popular MIML algorithms based on boosting, support vector machine, and regularization, but the computational time required by the proposed algorithm is less than those by the other three algorithms. Content Type Journal Article Category Regular Paper Pages 1-22 DOI 10.1007/s10115-012-0567-9 Authors Qingyao Wu, Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China Michael K. Ng, Department of Mathematics, Hong Kong Baptist University, Hongkong, China Yunming Ye, Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 9
    Publication Date: 2012-10-11
    Description:    Adaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments. Content Type Journal Article Category Survey Paper Pages 1-34 DOI 10.1007/s10115-012-0565-y Authors Juan M. Alberola, Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain Vicente Julian, Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain Ana Garcia-Fornes, Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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  • 10
    Publication Date: 2012-09-24
    Description:    Recently, a large amount of work has been devoted to the study of spectral clustering—a simple yet powerful method for finding structure in a data set using spectral properties of an associated pairwise similarity matrix. Most of the existing spectral clustering algorithms estimate only one cluster number or estimate non-unique cluster numbers based on eigengap criterion. However, the number of clusters not always exists one, and eigengap criterion lacks theoretical justification. In this paper, we propose non-unique cluster numbers determination methods based on stability in spectral clustering (NCNDBS). We first utilize the multiway normalized cut spectral clustering algorithm to cluster data set for a candidate cluster number k . Then the ratio value of the multiway normalized cut criterion of the obtained clusters and the sum of the leading eigenvalues (descending sort) of the stochastic transition matrix is chosen as a standard to decide whether the k is a reasonable cluster number. At last, by varying the scaling parameter in the Gaussian function, we judge whether the reasonable cluster number k is also a stability one. By three stages, we can determine non-unique cluster numbers of a data set. The Lumpability theorem concluded by Meil \breve a and Xu provides a theoretical base for our methods. NCNDBS can estimate non-unique cluster numbers of the data set successfully by illustrative experiments. Content Type Journal Article Category Regular Paper Pages 1-20 DOI 10.1007/s10115-012-0547-0 Authors Sumuya Borjigin, School of Economics and Management, Inner Mongolia University, Hohhot, 010021 People’s Republic of China Chonghui Guo, Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024 People’s Republic of China Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
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