With the rapid deployment of e-services, many workflow-like e-business process definition languages come into existence. At the same time, Ontology Web Language for Services (OWL-S) aims to build an ontology language to support the integration of various specifications. There has been some work on mapping WSDL (Web Services Description Language) to OWL-S to build a connection between the Web service and service profile. However, in the sense of activity relationships, there has been no effort so far trying to build the OWL-S service model from a workflow process model. Therefore, we design and develop an innovative mapping tool to translate BPEL4WS (Business Process Execution Language for Web Services) to OWL-S. Through this mapping, semantics in the traditional business process specifications can be enriched significantly to enable more flexible and automatic e-service functions by using existing OWL-S tools such as composition and discovery, especially the execution of workflow-based services.

This paper accomplishes the automatic composition of web services by leveraging semantics in XML Schemas, and by automatically generating aligned ontologies from mappings between XML schemas. Hence, we propose to encode the import of schemas and their mappings in HTML, which when combined with JavaScript libraries enable a web user to write mashups that hide coding and ontological complexities and achieve automated data mediation.

We present AUQ-ADMM, an adaptive uncertainty-weighted consensus ADMM method for solving large-scale convex optimization problems in a distributed manner. Our key contribution is a novel adaptive weighting scheme that empirically increases the progress made by consensus ADMM scheme and is attractive when using a large number of subproblems. The weights are related to the uncertainty associated with the solutions of each subproblem, and are efficiently computed using lowrank approximations. We show AUQ-ADMM provably converges and demonstrate its effectiveness on a series of machine learning applications, including elastic net regression, multinomial logistic regression, and support vector machines. We provide an implementation based on the PyTorch package 1 .

We present AUQ-ADMM, an adaptive uncertainty-weighted consensus ADMM method for solving large-scale convex optimization problems in a distributed manner. Our key contribution is a novel adaptive weighting scheme that empirically increases the progress made by consensus ADMM scheme and is attractive when using a large number of subproblems. The weights are related to the uncertainty associated with the solutions of each subproblem, and are efficiently computed using lowrank approximations. We show AUQ-ADMM provably converges and demonstrate its effectiveness on a series of machine learning applications, including elastic net regression, multinomial logistic regression, and support vector machines. We provide an implementation based on the PyTorch package 1 .