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Feature-Oriented Nonfunctional Requirement Analysis for Software Product Line

Journal of Computer Science & Technology 2009年第2期24卷 319-338页

作者：彭鑫 Seok-Won Lee 赵文耘 School of Computer Science Fudan University Knowledge-Intensive Software Engineering Research Group Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte

Domain analysis in software product line （SPL） development provides a basis for core assets design and imple- mentation by a systematic and comprehensive commonality/variability analysis. In feature-oriented SPL methods, products of the domain analysis are domain feature models and corresponding feature decision models to facilitate application-oriented customization. As in requirement analysis for a single system, the domain analysis in the SPL development should con- sider both flmctional and nonfunctional domain requirements. However, the nonfunctional requirements （NFRs） are often neglected in the existing domain analysis methods. In this paper, we propose a context-based method of the NFR analysis for the SPL development. In the method, NFRs are materialized by connecting nonfunctional goals with real-world context, thus NFR elicitation and variability analysis can be performed by context analysis for the whole domain with the assistance of NFR templates and NFR graphs. After the variability analysis, our method integrates both functional and nonfunc- tional perspectives by incorporating the nonfunctional goals and operationalizations into an initial functional feature model. NFR-related constraints are also elicited and integrated. Finally, a decision model with both functional and nonfunctional perspectives is constructed to facilitate application-oriented feature model customization. A computer-aided grading system （CAGS） product line is employed to demonstrate the method throughout the paper.

关键词： software product line nonfunctional requirement domain analysis feature-oriented method variability analysis

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Reports of the AAAI 2010 conference workshops

Reports of the AAAI 2010 conference workshops

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作者： Aha, David W. Boddy, Mark Bulitko, Vadim D'Avila Garcez, Artur S. Doshi, Prashant Edelkamp, Stefan Geib, Christopher Gmytrasiewicz, Piotr Goldman, Robert P. Halevy, Alon Hitzler, Pascal Isbell, Charles Josyula, Darsana Kaelbling, Leslie Pack Kersting, Kristian Kunda, Maithilee Lamb, Luis C. Marthi, Bhaskara McGreggor, Keith Mihalkova, Lilyana Nastase, Vivi Natarajan, Sriraam Provan, Gregory Raja, Anita Ram, Ashwin Riedl, Mark Russell, Stuart Sabharwal, Ashish Smaus, Jan-Georg Sukthankar, Gita Tuyls, Karl Van Der Meyden, Ron Adaptive Systems Section Naval Research Laboratory Washington DC United States Adventium Labs. Minneapolis MN United States Department of Computer Science University of Alberta Canada City University London United Kingdom University of Georgia United States University of Bremen Germany School of Informatics University of Edinburgh United Kingdom University of Illinois Chicago United States Smart Information Flow Technologies LLC Minneapolis MN United States Structured Data Group Google Research United States Department of Computer Science Wright State University Dayton OH United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science Bowie State University United States Department of Computer Science and Engineering Massachusetts Institute of Technology United States Statistical Relational Mining Group Fraunhofer IAIS Germany Department of Computer Science Federal University of Rio Grande Do sul Brazil Willow Garage Inc. CA United States Computer Science Department University of Maryland College Park United States Heidelberg Institute for Theoretical Studies Heidelberg Germany Department of Computer Science University of Wisconsin Madison United States University College Cork Ireland Cork Complex Systems Lab. Ireland Department of Software and Information Systems University of North Carolina Charlotte United States Department of Computer Science and Engineering University of California Berkeley United States Department of Computer Science Cornell University United States Institute for Computational Sustainability United States University of Freiburg Germany School of Electrical Engineering and Computer Science University of Central Florida United States Maastricht University Netherlands University of New South Wales NICTA Australia

The AAAI-10 workshop program was held on July 11-12, 2010, at the Westin Peachtree Plaza in Atlanta, Georgia. The workshop program included 13 workshops covering a wide range of topics in artificial intelligence. There were several presentations on particular formalisms for combining the levels of planning from the perspectives of classical planning, motion planning, POMDPs, search-based planning, and temporal logic-based constraints. Henry Lieberman from Massachusetts Institute of Technology Media Laboratory presented work developing a platform for knowledge acquisition and usage from expert users, built upon experience with eliciting information from nonexperts in a collaborative manner. The objective of the AAAI Workshop on Goal-Directed Autonomy (GDA) was to encourage discussion and novel contributions on intelligent agents that can self-select their goals. Jeremy Baxter from QinetiQ focused on user interaction, while Nick Hawes described a goal-directed reasoning framework for controlling a mobile robot.

关键词： Motion planning

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Addressing sustainability for research initiatives in mobile learning through scalability

Addressing sustainability for research initiatives in mobile...

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8th IASTED International Conference on Web-based Education, WBE 2009

作者： Wingkvist, Anna Ericsson, Morgan Center for Learning and Knowledge Technologies Department of Information Systems School of Mathematics and System Engineering Sweden

ISBN: (纸本)9780889867895

Understanding research initiatives in the field of mobile learning involves dealing with complexity. It is important to gather knowledge on how to nurse these attempts. To develop ideas and reach wide deployment of novel mobile learning is of great value. In this paper there is a discussion of the factors that describe attributes of mobile learning research initiatives and an investigation of whether these are sustainable or not, and what this means. Sustainability for mobile learning initiatives is essential for the wide acceptance from both public and academia. The paper also addresses scalability of mobile learning research initiatives. Scalability is a term used to describe how well something can grow to suit an increasing complexity, and is addressed as a possibility to reflect on good ideas. Scalability is important in order to reach sustainability. Finally, this paper introduces a model that illustrates the evolutionary life cycle of a mobile learning research initiative. The model presents four evolution stages and these are: Idea, Experiment, Project, and Release.

关键词： Scalability

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An Adaptive Method for the Efficient Similarity Calculation

An Adaptive Method for the Efficient Similarity Calculation

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14th International Conference on database systems for Advanced Applications

作者： Cai, Yuanzhe Liu, Hongyan He, Jun Du, Xiaoyong Jia, Xu Key Labs of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Department of Management Science and Engineering Tsinghua University China

ISBN: (纸本)9783642008863

SimRank is a well-known algorithm for similarity calculation based on object-to-object relationship. However, it suffers from high computation cost. Inthis paper, we find that the convergence behavior of different object pairs is different when we use SimRank to compute the similarity of objects. Many similarity scores converge fast, while others need more time before convergence. Based on this observation, we propose an adaptive method called Adaptive-SimRank to speed up similarity calculation. Using this method, we don't need to recalculate those converged pairs' similarity. The experiments conducted on web datasets and synthetic dataset show that our new method can reduce the running time by nearly 35%.

关键词： Similarity Calculation Linkage Mining

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Assessing the Influence Probability between Objects: A Random Walker Approach

Assessing the Influence Probability between Objects: A Rando...

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IEEE Symposium on Computational Intelligence and data Mining

作者： Li, Pei Li, Zhixu He, Jun Du, Xiaoyong Liu, Hongyan Key Labs of Data Engineering and Knowledge Engineering MOE China and School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9781424427659

Influence between objects needs to be assessed in many applications. Lots of measures have been proposed, but a domain-independent method is still expected. In this paper, we give a probabilistic definition of influence based on the random walker model on graphs. Two approaches, linear systems method and Basic InfRank algorithm, are shown and return equal results, but Basic InfRank is more efficient by iterative computation. Two variants on bipartite graphs and star graphs are discussed. Experiments show InfRank algorithms have good accuracy, fast convergent rate and high performance.

关键词： Linear systems

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Using link-based content analysis to measure document similarity effectively

Using link-based content analysis to measure document simila...

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

作者： Li, Pei Li, Zhixu Liu, Hongyan He, Jun Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Along with a massive amount of information being placed online, it is a challenge to exploit the internal and external information of documents when assessing similarity between them. A variety of approaches have been proposed to model the document similarity based on different foundations, but usually they are not applicable for combining internal and external information. In this paper, we introduce a link-based method into content analysis, which is based on random walk on graphs. By defining similarity as the meeting probability of two random surfers, we propose a computational model for content analysis, which can also be integrated with external information of documents. Empirical study shows that our method achieves good accuracy, acceptable performance and fast convergent rate in multi-relational document similarity measuring. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Computers

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Exploiting the block structure of link graph for efficient similarity computation

Exploiting the block structure of link graph for efficient s...

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13th Pacific-Asia Conference on knowledge Discovery and data Mining, PAKDD 2009

作者： Pei, Li Yuanzhe, Cai Hongyan, Liu Jun, He Xiaoyong, Du Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)3642013066

In many real-world domains, link graph is one of the most effective ways to model the relationships between objects. Measuring the similarity of objects in a link graph is studied by many researchers, but an effective and efficient method is still expected. Based on our observation of link graphs from real domains, we find the block structure naturally exists. We propose an algorithm called BlockSimRank, which partitions the link graph into blocks, and obtains similarity of each node-pair in the graph efficiently. Our method is based on random walk on two-layer model, with time complexity as low as O(n4/3) and less memory need. Experiments show that the accuracy of BlockSimRank is acceptable when the time cost is the lowest. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Artificial intelligence

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AutoPCS: A phrase-based text categorization system for similar texts

AutoPCS: A phrase-based text categorization system for simil...

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

作者： Li, Zhixu Li, Pei Wei, Wei Liu, Hongyan He, Jun Liu, Tao Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Nearly all text classification methods classify texts into predefined categories according to the terms appeared in texts. State-of-the-art of text classification prefer to simplely take a word as a term since it performs good on some famous datasets;some experts even pointed out that phrases don't improve or improve only marginally the classifiction accuracy. However, we found out that this is not always true when we try to categorize texts about similar topics in the same domain. With words only we can not categorize those texts effectively since they nearly share the same word set. Then we suppose the results might be improved if we also use phrases as terms. To testify our supposition, we propose our own phrase extraction way as well as select proper feature selection method and classifier by conducting experimental study on a data set which comes from paper abstracts in the field of databases. Accordingly, we also develop a system called AutoPCS which can be used to help experts in choosing relevant topics for newly coming papers from a predefined topic list only by their abstracts. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Classification (of information)

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Deprank: A probabilistic measure of dependence via heterogeneous links

Deprank: A probabilistic measure of dependence via heterogen...

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

作者： Li, Pei Hu, Bo Liu, Hongyan He, Jun Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Dependence is a common relationship between objects. Many works have paid their attentions on dependence, but many of them mainly focus on constructing or exploiting dependence graphs on some specific domain. In this paper, we give a generic definition of dependence and make equivalence to the situation of graphs, and propose an algorithm called DepRank, which quantifies the dependence degree of a node pair based on probabilities of all dependence paths. Empirical study shows that DepRank method has reasonable results and maintains a well balance between accuracy and efficiency. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Artificial intelligence

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Open framework middleware for intelligent WSN topology adaption in smart buildings

Open framework middleware for intelligent WSN topology adapt...

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2009 International Conference on Ultra Modern Telecommunications and Workshops

作者： Brennan, Rob Tai, Wei O'Sullivan, Declan Aslam, Muhammad S. Rea, Susan Pesch, Dirk Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland Centre for Adaptive Wireless Systems Department of Electronic Engineering Cork Institute of Technology Ireland

ISBN: (纸本)9781424439416

Modern smart buildings utilize sensor networks for facilities management applications such as energy monitoring. However as buildings become progressively more embedded with sensor networks, the challenge of managing and maintaining the sensor networks themselves becomes ever more significant. As a cost-sensitive activity, facilities management deployments are less likely to deploy node redundancy and specialized technical staff to maintain these networks. Hence there are strong requirements for the network to efficiently self-diagnose, self-heal and integrate with standard buildings management systems. This paper introduces a solution for WSN management in smart buildings that addresses these issues. It is based on the deployment of the Open Framework Middleware for sensor networks coupled with a structured knowledge and rule-based fault analysis engine to perform network event correlation and root cause analysis. The system also explicitly interfaces with a Building Management System (BMS) or the scheduling of network maintenance activities such as sensor battery replacement. ©2009 IEEE.

关键词： Topology

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