Information Flow Analysis Based Security Checking of Health Service Composition Plans

In this paper, we present an approach to solve the problem of provably secure execution of semantic web service composition plans. The integrated components of this approach include our OWL-S service matchmaker, OWLSMX, the service composition planner, OWLS-XPlan, and the security checker module for formally verifying the compliance of the created composition plan to be executed with given data and service security policies using type-based information flow analysis. We demonstrate this approach by means of its application to a use case scenario of health service composition planning

Fast Composition Planning of OWL-S Services and Application

In this paper, we present the implementation, evaluation, and application of our OWL-S service composition planner OWLS-XPlan. Medical services described in OWL-S 1.1 and ontologies are converted to initial state and goal descriptions in PDDL 2.1, which are then used by the fast heuristic FF planner XPlan for generating an execution complete composition plan. Results of experimental evaluation of XPlan shows its top performance compared with other selected AI planners. OWLS-XPlan is used in an agent based mobile e-Health system for emergency medical assistance planning tasks

Digital reality: a model-based approach to supervised learning from synthetic data

Hierarchical neural networks with large numbers of layers are the state of the art for most computer vision problems including image classification, multi-object detection and semantic segmentation. While the computational demands of training such deep networks can be addressed using specialized hardware, the availability of training data in sufficient quantity and quality remains a limiting factor. Main reasons are that measurement or manual labelling are prohibitively expensive, ethical considerations can limit generating data, or a phenomenon in questions has been predicted, but not yet observed. In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. By investigating the parameter space defined of such models, training data can be generated in a controlled way compared to data that was captured from real world situations. We propose the Digital Reality concept and demonstrate its potential in different application domains, including industrial inspection, autonomous driving, smart grid, and microscopy research in material science and engineering

Semantic web service search: a brief survey

Scalable means for the search of relevant web services are essential for the development of intelligent service-based applications in the future Internet. Key idea of semantic web services is to enable such applications to perform a high-precision search and automated composition of services based on formal ontology-based representations of service semantics. In this paper, we briefly survey the state of the art of semantic web service search

Contract Aware Components, 10 years after

The notion of contract aware components has been published roughly ten years ago and is now becoming mainstream in several fields where the usage of software components is seen as critical. The goal of this paper is to survey domains such as Embedded Systems or Service Oriented Architecture where the notion of contract aware components has been influential. For each of these domains we briefly describe what has been done with this idea and we discuss the remaining challenges.Comment: In Proceedings WCSI 2010, arXiv:1010.233