Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption. This means that datasets are natively expressed in other formats and require a data translation process for full compliance. This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a format compliant with EU standards by means of Semantic Web technologies. Our solution addresses the challenge and also contributes to build a multimodal transport Knowledge Graph of interlinked and interoperable information that enables intelligent querying and exploration, as well as facilitates the design of added-value services.

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Service discovery is a key activity to actually identify the Web services (WSs) to be invoked and composed. Since it is likely that more than one service fulfill a set of user requirements, some ranking mechanisms based on non-functional properties (NFPs) are needed to support automatic or semi-automatic selection.This paper introduces an approach to NFP-based ranking of WSs providing support for semantic mediation, consideration of expressive NFP descriptions both on provider and client side, and novel matching functions for handling either quantitative or qualitative NFPs. The approach has been implemented in a ranker that integrates reasoning techniques with algorithmic ones in order to overcome current and intrinsic limitations of semantic Web technologies and to provide algorithmic techniques with more flexibility. Moreover, to the best of our knowledge, this paper presents the first experimental results related to NFP-based ranking of WSs considering a significant number of expressive NFP descriptions, showing the effectiveness of the approach.

Abstract-Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled and specified in data contracts to support concern-aware data selection and utilization. Instead of relying on a specific definition of data contracts, we analyze contemporary data contracts and we present an abstract model for data contracts. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some realworld scenarios.

Abstract. Recently, the Software-as-a-Service (SaaS) model has been increasingly supported, becoming a major part of the new emerging cloud computing paradigms. Although SaaS exists in different forms, supporting and providing SaaS developed based Web services has attracted a large effort from industries and academics because this form of SaaS allows software to be easily composed and integrated to offer new services for customers. Even though various service composition techniques, based on functional and non-functional parameters, have been proposed, the issue of service contract compatibility has been neglected. This issue is of paramount importance in the Web services-based SaaS model because services are provided by different providers, associated with different contracts which are defined by different specifications. This paper proposes techniques for supporting service composers to deal with the heterogeneity of service contracts in service composition. We describe a novel approach for modeling and mapping different service contract specifications, and a set of techniques for evaluating service contract compatibility. Our techniques consider contract terms associated with data and control flows, as well as composition patterns. Illustrating scenarios are proposed to demonstrate the efficiency of our techniques.

Currently, rich and diverse data types have been increasingly provided using the dataas-a-service (DaaS) model, a form of cloud computing services and the core element of data marketplaces. This facilitates the on-the-fly data composition and utilisation for several dataintensive applications in e-science and business domains. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the view that data concerns should be explicitly modelled and specified in data contracts to support concern-aware data selection and utilisation. We perform a detailed analysis of current techniques for data contracts in the cloud. Instead of relying on a specific representation of data contracts, we introduce an abstract model for data contracts that can be used to build different types of data contracts for specific types of data. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some real-world scenarios and show how data contracts can be integrated into data agreement exchange services in the cloud.

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The discovery of a Semantic Web Service (SWS) is the act of locating a machine-processable description of a SWSrelated resource that may have been previously unknown and that meets certain functional criteria. The increasing availability of services that offer similar functionalities requires the discovery process to be enhanced with a selection phase that considers non-functional properties (NFPs) of services. This paper proposes a model to describe these properties and a novel approach to service selection. Our approach is based on the design of matching rules by means of mediators defined by sets of rules stating the condition for successful matches. These rules are based on the ontological description of objects representing NFPs that are required and offered. In particular, we define a set of rule schemas to support mediation and matching for a class of user-defined NFP-constraints clustered according to specified constraint operators. Rules support matching for both qualitative and quantitative non-functional properties.