A key challenge for dynamic Web service selection is that Web services are typically highly configurable and service requesters often have dynamic preferences on service configurations. Current approaches, such as WS-Agreement, describe Web services by enumerating the various possible service configurations, an inefficient approach when dealing with numerous service attributes with large value spaces. We model Web service configurations and associated prices and preferences more compactly using utility function policies, which also allows us to draw from multi-attribute decision theory methods to develop an algorithm for optimal service selection. In this paper, we present an OWL ontology for the specification of configurable Web service offers and requests, and a flexible and extensible framework for optimal service selection that combines declarative logic-based matching rules with optimization methods, such as linear programming. Assuming additive price/preference functions, experimental results indicate that our algorithm introduces an overhead of only around 2 sec. compared to random service selection, while giving optimal results. The overhead, as percentage of total time, decreases as the number of offers and configurations increase.

We present a description and analysis of the data access challenge in the Siemens Energy. We advocate for Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. Finally, we describe our preliminary installation and evaluation of the platform in Siemens. † The research was supported by the FP7 grant Optique (n. 318338).

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We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a fleet of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind like temperature measurements, they are different since they access structurally different data sources. We have investigated how Semantic Technologies can make such complex diagnostics simpler by providing an abstraction semantic layer that integrates heterogeneous data. We developed the system OPTIQUE to put our ideas in practice. In a nutshell, OPTIQUE allows to express complex diagnostic tasks with just a few high-level semantic queries. Then, the system can automatically enrich these queries, translate them into a fleet with a large number of low-level data queries, and finally optimise and efficiently execute the fleet in a heavily distributed environment. We will demo the benefits of OP-TIQUE on a real world scenario of Siemens Energy. For this purpose we prepared anonymised streaming and static data relevant to 950 Siemens power generating turbines with more than 100, 000 sensors and deployed OPTIQUE on multiple distributed environments with up to 128 nodes. By registering and monitoring continuous semantic high-level queries that combine streaming and static data the demo attendees will be able to see how OPTIQUE makes diagnostics of turbines easy. They will also see how OPTIQUE can handle more than a thousand concurrent complex diagnostic tasks that integrate heterogeneous data in real-time with a 10 TB/day throughput. Finally, they will see that creating a semantic layer, such as the one over the Siemens demo data, can be done in realistic time with the help of our bootstrapping interactive system.