Business-to-Business (B2B) workflow/service interoperation across Virtual Organisations (VOs) brings about novel business scenarios. In these scenarios, parts of workflows (or services) corresponding to different part...
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Business-to-Business (B2B) workflow/service interoperation across Virtual Organisations (VOs) brings about novel business scenarios. In these scenarios, parts of workflows (or services) corresponding to different partners can be aggregated in a producer-consumer manner, leading to hierarchical structures of added value interpreted as service value chains. Service Level Agreements (SLAs), which are contracts between service providers and service consumers, guarantee the expected quality of service (QoS) to different stakeholders at various levels of a service value chain. The essential requirements for such an SLA-based choreography of services include agile component-based infrastructure to support the corresponding choreography of their SLAs;proactive validation of SLAs to prevent violations;reactive validation of SLAs for penalty enforcement and breach management;and business enabling requirements such as trust, privacy, security and automation. In this paper we highlight the significance of these issues and propose their solutions. We then proceed to weave these solution components together into a comprehensive SLA validation framework that blends together two major systems: a rule based distributed system for SLA validation and a service monitoring system for the prevention of SLA violations.
Search engines and web crawlers can not access the Deep Web directly. The workable way to access the hidden database is through query interfaces. Automatic extracting attributes from query interfaces and translating q...
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In this paper, a hybrid algorithm named DPSOSA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. ...
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In this paper, a hybrid algorithm named DPSOSA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. Computational tests show that this hybrid method is very effective and robust for the elimination ordering problem.
To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heu...
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To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate its convergence. Computational experiments show that MHC-HS-ACS can find very high quality solutions.
According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are prop...
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According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more robust than other existing swarm intelligence methods when solving the elimination ordering problem.
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 met...
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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.
Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in ...
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ISBN:
(纸本)9781424452422
Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.
With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags...
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With the rapid development of web2.0 technologies, tagging become much more important today to organize information and help users search the information they need with social bookmarking tools. In order to finish the...
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With the rapid development of web2.0 technologies, tagging become much more important today to organize information and help users search the information they need with social bookmarking tools. In order to finish the second task of ECML PKDD challenge 2009, we propose a graph-based collaborative filtering tag recommendation system. We also refer to an algorithm called FolkRank, which is an adaptation of the famous Page Rank. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for our task.
Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, ...
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Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, so that a good tag recommendation system can ease the process of finding some useful and relevant keywords for users. Researchers have made lots of relevant work for recommendation system, but those traditional collaborative systems do not fit to our tag recommendation. In this paper, we present two different methods: a simple language model and an adaption of topic model. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for the task one of PKDD Challenge 2009.
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