17737533
Semantic Matching of Web Services Based on Choreographies
Web services facilitate the efficient execution of B2B e-commerce by integrating business processes over the Internet, which needs dynamic and flexible binding of services. However, current Web service standards do not support it. A formal approach for semantic matching of service specifications based on choreographies, i.e., the behavior of Web services, is developed in this paper. It can accurately and automatically discover required services for integrating business processes. Firstly an extended deterministic finite state automaton (EDFA) is proposed by labeling state transitions using binary-tuples (input, output) rather than letters. The automata describe Web services in a more accurate way: the nodes represent states maintained by services; the state transitions represent communication activities of services. The automata depict the temporal sequences of communication activities that describe the behaviors of services. Further, the intersection of two EDFAs is defined. Finally, an algorithm for testing the emptiness of the languages accepted by EDFAs is presented and used to evaluate the compatibility of Web services
28131114
Utilizing Fuzzy Petri Net for Choreography Based Semantic Web Services Discovery
Semantic Web Services have received great popularity these days for their capability of providing more automatic web services discoveries and compositions. However, the issue of behavior compatibility is still not properly solved. In this paper, we propose a choreography-based formal approach to discover semantic web services that are compatible with their behaviors. The fuzzy petri net is used to perform the modeling, verification, analysis and evaluation for the matchmaking of semantic web services. The requested and provided services are modeled by two fuzzy petri nets combined through a set of combination rules. By analyzing the composite fuzzy petri net, the behavior compatibility can be verified, and the matching degree between requested and provided services can be obtained. With our approach, the success rate and accuracy of the semantic web services discoveries can be improved, and the final services set can be optimized.
683657
An adaptive social network for information access: Theoretical and experimental results
We consider a social network of software agents who assist each other in helping their users find information. Unlike in most previous approaches, our architecture is fully distributed and includes agents who preserve the privacy and autonomy of their users. These agents learn models of each other in terms of expertise (ability to produce correct domain answers) and sociability (ability to produce accurate referrals). We study our framework experimentally to study how the social network evolves. Specifically, we find that under our multi-agent learning heuristic, the quality of the network improves with interactions: the quality is maximized when both expertise and sociability are considered; pivot agents further improve the quality of the network and have a catalytic effect on its quality even if they are ultimately removed. Moreover, the quality of the network improves when clustering decreases, reflecting the intuition that you need to talk to people outside your close circle to get the best information.
14066256
Grid Service Discovery with Rough Sets
The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities. This paper presents ROSSE, a Rough sets-based search engine for grid service discovery. Building on the Rough sets theory, ROSSE is novel in its capability to deal with the uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct nonfunctional properties related to the quality of service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximize user satisfaction in service discovery. ROSSE is evaluated from the aspects of accuracy and efficiency in discovery of computing services.
16993770
Service matchmaking with rough sets
With the wide adoption of open grid services architecture (OGSA) and Web services resource framework (WSRF), the grid is emerging as a service-oriented computing infrastructure for engineers and scientists to solve data and computationally intensive problems. It is envisioned that computing resources in a future grid environment will be exposed as services. Service discovery becomes an issue of vital importance for a wider uptake of the grid. This paper presents RSSM, a rough sets based service matchmaking algorithm for service discovery with an aim to tolerate uncertainty in identifying service properties. The evaluation results show that the RSSM algorithm is more effective in service discovery compared with other mechanisms such as UDDI and OWLS.
652118
Stagewise Lasso
Many statistical machine learning algorithms minimize either an empirical loss function as in AdaBoost, or a penalized empirical loss as in Lasso or SVM. A single regularization tuning parameter controls the trade-off between fidelity to the data and generalizability, or equivalently between bias and variance. When this tuning parameter changes, a regularization "path" of solutions to the minimization problem is generated, and the whole path is needed to select a tuning parameter to optimize the prediction or interpretation performance. Algorithms such as homotopy-Lasso or LARS-Lasso and Forward Stagewise Fitting (FSF) (aka e-Boosting) are of great interest because of their resulted sparse models for interpretation in addition to prediction. 
 
In this paper, we propose the BLasso algorithm that ties the FSF (e-Boosting) algorithm with the Lasso method that minimizes the L1 penalized L2 loss. BLasso is derived as a coordinate descent method with a fixed stepsize applied to the general Lasso loss function (L1 penalized convex loss). It consists of both a forward step and a backward step. The forward step is similar to e-Boosting or FSF, but the backward step is new and revises the FSF (or e-Boosting) path to approximate the Lasso path. In the cases of a finite number of base learners and a bounded Hessian of the loss function, the BLasso path is shown to converge to the Lasso path when the stepsize goes to zero. For cases with a larger number of base learners than the sample size and when the true model is sparse, our simulations indicate that the BLasso model estimates are sparser than those from FSF with comparable or slightly better prediction performance, and that the the discrete stepsize of BLasso and FSF has an additional regularization effect in terms of prediction and sparsity. Moreover, we introduce the Generalized BLasso algorithm to minimize a general convex loss penalized by a general convex function. Since the (Generalized) BLasso relies only on differences not derivatives, we conclude that it provides a class of simple and easy-to-implement algorithms for tracing the regularization or solution paths of penalized minimization problems.
18656180
Application of PGA on Optimization of Distribution of Shopping Centers
In this study, the distribution of shopping centers is optimized in terms of realizing the shortest car-based shopping trips in an urban area. Modal split is performed between road and public traffic networks is calculated, and then the interaction between land-use and transportation in the context of choice of shopping destinations is modeled to build the optimal function. Parallel genetic algorithm (PGA) is applied to solve the optimal problem on distribution of the area of shopping centers. Several problems in application of PGA are discussed. A case study is undertaken in order to examine the effectiveness of this method.