22235359
Towards an Optimization-Based Method for Consolidating Domain Variabilities in Domain-Specific Web Services Composition
In this paper, we put forward an automatic method of acquiring the specific system composition model from a domain composition model and requirements for the specific system in domain-specific Web services composition. This is referred to as the variability consolidation problem in this paper. To achieve this goal, we designed a language to describe domain properties for Web services composition. The basis of our approach is to transform the domain composition model and the requirements for the specific system into a mathematical optimization problem, which can be solved by existing algorithms. Thus, this method is fully automatic and not prone to human errors. Our preliminary experimental results show that our method is quite feasible for solving problems with real world sizes.
1882847
User Feedback-Based Refinement for Web Services Retrieval using Multiple Instance Learning
A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback
17777200
A similarity-aware approach to testing based fault localization
Debugging is a time-consuming task in software development and maintenance. To accelerate this task, several approaches have been proposed to automate fault localization. In particular, testing based fault localization (TBFL), which utilizes the testing information to localize the faults, seem to be very promising. However, the similarity between test cases in the test suite has been ignored in the research on TBFL. In this paper, we investigate this similarity issue and propose a novel approach named similarity-aware fault localization (SAFL), which can calculate the suspicion probability of each statement with little impact by the similarity issue. To address and deal with the similarity between test cases, SAFL applies the theory of fuzzy sets to remove the uneven distribution of the test cases. We also performed an experimental study for two real-world programs at different size levels to evaluate SAFL together with another two approaches to TBFL. Experimental results show that SAFL is more effective than the other two approaches when the test suites contain injected redundancy, and SAFL can achieve a competitive result with normal test suites. SAFL can also be more effective than applying test suite reduction to current approaches to TBFL.
1111153
Towards the UML evaluation using taxonomic patterns on meta-classes
In order to evaluate the design quality of the UML, understanding meta-classes is a key activity as they are the primary weapons by which the UML specifies the application domains. The paper introduces taxonomic patterns for clustering the UML meta-classes based on the observation of their evolution and fitness. The result sets of the patterns assist in finding the evidence of the concerns about the UML design and quality. It not only helps to find out problematic meta-classes, possible design defects of the UML and the inconsistency between the UML meta-models and the application domains; but also provides valuable information for guiding the development and evaluation of the UML. The work can be the basis of further quality analysis of the UML meta-models.
18028053
An Experimental Study of Increasing Diversity for Case-Based Diagnosis
Increasing diversity for case-based reasoning (CBR) is an issue that has recently drawn the attention of researchers in the CBR field. Several diversification techniques have been proposed and discussed in the literature. However, whether and to what extent those techniques can bring about benefits to end-users remains in question. In this paper, we report an experiment in applying a diversification technique to a case-based diagnosis tool in a product maintenance domain. The results of this offer some evidence in support of diversification techniques.
14328389
Alternative scalable algorithms for lattice-based feature location
Considering the scalability of using formal concept analysis to locate features in source code, we present a set of alternative straightforward algorithms to achieve the same objectives. A preliminary experiment indicates that the alternative algorithms are more scalable to deal with the large numbers of data to some extent.
44970083
Web Services Relationships Binding for Dynamic e-Business Integration
Web services are becoming a major research topic for computer scientists, engineers and business consulting professionals. In this preface, I would like to outline the challenges of the current Web services research topics from the modeling, in-teroperability, and mathematical foundations points of view. Then I will introduce some research opportunities and possible future directions for moving Web services forward via some illustrative ideas such as business semantic computing as well as killer application driven Web services research approaches. For the business semantic computing aspect I will present some example application domains such as federated Web services discovery, dynamic Web services composition and extended business collaboration.
32965761
The Next Big Thing: Web Services Collaboration
In this paper, we introduce the lifecycle of a Web services solution that consists of Web services modeling, development, deployment, publishing, as well as discovery, composition and collaboration, plus monitoring and analytical control. Then some research and development challenges of building a Web services solution in the context of Web services solution lifecycle are described. Especially, we present an emerging Web services research direction, namely, Web services collaboration, followed by a few major research issues associated with facilitating extended business collaboration using Web services and semantic annotation.
16359335
Scalable relevance feedback using click-through data for web image retrieval
Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalability, efficiency and effectiveness issues. In this paper we proposed a scalable relevance feedback mechanism using click-through data for web image retrieval. The proposed mechanism regards users' click-through data as implicit feedback which could be collected at lower cost, in larger quantities and without extra burden on the user. During RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.
2094926
Conflict driven learning in a quantified Boolean Satisfiability solver
Within the verification community, there has been a recent increase in interest in Quantified Boolean Formula evaluation (QBF) as many interesting sequential circuit verification problems can be formulated as QBF instances. A closely related research area to QBF is Boolean Satisfiability (SAT). Recent advances in SAT research have resulted in some very efficient SAT solvers. One of the critical techniques employed in these solvers is Conflict Driven Learning. In this paper, we adapt conflict driven learning for application in a QBF setting. We show that conflict driven learning can be regarded as a resolution process on the clauses. We prove that under certain conditions, tautology clauses obtained from resolution in QBF also obey the rules for implication and conflicts of regular (non-tautology) clauses; and therefore they can be treated as regular clauses and used in future search. We have implemented this idea in a new QBF solver called Quaffle and our initial experiments show that conflict driven learning can greatly speed up the solution process for most of the benchmarks we tested.
38635352
A neural network based classifier for handwritten Chinese character recognition
In this paper, a geometrical approach for building neural networks is proposed. With the proposed approach, it is very easy to construct an efficient neural classifier to solve the handwritten Chinese character recognition problem, as well as other pattern recognition problems of large scale. Experiments are conducted to evaluate the performance of the proposed approach and results obtained are promising.