Srdjan Komazec

Sparkwave: continuous schema-enhanced pattern matching over RDF data streams

Data streams, often seen as sources of events, have appeared on the Web. Stream processing on the Web needs however to cope with the typical openness and heterogeneity of the Web environment. Semantic Web technologies, meant to facilitate data integration in an open environment, can help to address heterogeneities across multiple streams. In this paper we present Sparkwave, an approach for continuous pattern matching over RDF data streams. Sparkwave is based on the Rete algorithm, which allows efficient and truly continuous processing of data streams. Sparkwave is able to leverage RDF schema information associated to data streams to compute entailments, so that implicit knowledge is taken into account for pattern matching. In addition, it further extends Rete to support time-based sliding windows and static data instances, to cope with the streaming nature of processed data and real-world use cases.

WSMX 1.0: A Further Step toward a Complete Semantic Execution Environment

The Web Service Execution Environment (WSMX) project is a continuously ongoing effort that aims at delivering a middleware covering all the Semantic Web Services life cycle. WSMX represents the reference implementation of the Semantically enabled Service Oriented Architecture (SESA) [1]. In this demonstration we aim to present the latest achievements that include: Web Service monitoring, Web Service ranking and Web Service grounding.

An Integration of Semantically Enabled Service Oriented Architectures and Agent Platforms

The capability to provide on-demand service access for SMEs can further reduce costs and allow companies to concentrate investments on their core businesses which in turn facilitates the overall competitive advantage. In the FP7 European Project COIN, a platform for supporting SMEs is developed which combines the flexibility of an execution environment for Semantic Web services with agent-based service compositions. This paper presents an approach to integrate an implementation of a Semantic Web service platform with an agent platform. One major benefit of the combination is that through this the potential of agent systems, most notably flexibility, can be tapped. An obstacle is the integration of dynamically discovered services which often cannot be used by agents because of interoperability problems (e.g. data heterogeneity). By delegating this task to a Semantically Enabled Service Oriented Architecture (SESA), the agent platform can concentrate on coordination tasks. Coordination tasks include executing predefined compositions of services but also automatic compositions of semantic services by applying AI planning techniques, in particular by transforming service descriptions into the Planning Domain Definition Language (PDDL). The integration approach is motivated and described in the context of a high-level scenario coming from the area of enterprise interoperability.

Towards a Reactive Semantic Execution Environment

Managing complex and distributed software systems built on top of the service-oriented paradigm has never been more challenging. While Semantic Web Service technologies offer a promising set of languages and tools as a foundation to resolve the heterogeneity and scalability issues, they are still failing to provide an autonomic execution environment. In this paper we present an approach based on Semantic Web Services to enable the monitoring and self-management of a Semantic Execution Environment (SEE), a brokerage system for Semantic Web Services. Our approach is founded on the event-triggered reactivity paradigm in order to facilitate environment control, thus contributing to its autonomicity, robustness and flexibility.

What's new in WSMX?

The Web Service Execution Environment (WSMX) is the most complete implementation of a Semantic Execution Environment to support the automation of the Web service life-cycle. WSMX is a constantly evolving project. The demo will provide insight and justify the value of the newly introduced features: the Complex Event Processing engine, the Notification Broker engine and the Orchestration engine.

Enhancing event processing networks with semantics to enable self-managed SEE federations

As a common platform to easily exchange information, the Web is already overwhelmed with events which need to be processed in timely fashion. The opportunity to detect event occurrences in real time at a Web scale while correlating events from different sources has the potential to enable powerful applications. Recent contributions in the area of event processing provide a generic framework, usually referred to as Event Processing Networks. This paper proposes a grounding of the Event Processing Network conceptual model on Semantic Web technologies, in order to realize Semantically-enhanced Event Processing Networks (SEPNs). As a use case, the paper describes the integration of SEPNs with a Semantic Execution Environment (SEE), in order to achieve a suitable level of monitoring and self-management. While relying on the proposed monitoring ontology, we show how SEPNs are capable of observing events at the level of both single and federated SEE deployments and of responding accordingly.

Status, Perspectives, and Lessons Learned

1 Computer Science Dept. University of Stanford, Gates Building, Stanford, CA 94305-9020, USA {petrie@stanford.edu} 2 Institute for Computer Science, Friedrich-Schiller-University Jena, 07743 Jena, Germany ulrich.kuester@uni-jena.de 3 Chair of Service and Software Engineering, Institute for Informatics University of Potsdam, 14482 Potsdam, Germany margaria@cs.uni-potsdam.de 4 Semantic Technology Institute Innsbruck, University of Innsbruck, Technikerstr. 21, 6020 Innsbruck, Austria,