Patrick Kapahnke’s research while affiliated with Deutsches Forschungszentrum für Künstliche Intelligenz and other places

Purpose The need to flexibly react to changing demands and to cost-efficiently manage customized production even for lot size of one requires a dynamic and holistic integration of service-based processes within and across enterprises of the value chain. In this context, this paper aims at presenting ODERU, the authors’ novel pragmatic approach for automatically implementing service-based manufacturing processes at design and runtime within a cloud-based elastic manufacturing platform. Design/methodology/approach ODERU relies on a set of semantic annotations of business process models encoded into an extension of the business process model and notation (BPMN) 2.0 standard. Leveraging the paradigms of semantic SOA and XaaS, ODERU integrates pattern-based semantic composition of process service plans with QoS-based optimization based on multi-objective constraint optimization problem solving. Findings The successful validation of ODERU in two industrial use cases for maintenance process optimization and automotive production in the European project CREMA revealed its usefulness for service-based process optimization in general and for significant cost reductions in maintenance in particular. Originality/value ODERU provides a pragmatic and flexible solution to optimal service composition with the following three main advantages: full integration of semantic service selection and composition with QoS-based optimization; executability of the generated optimal process service plans by an execution environment as they include service assignments, data flow (variable bindings) and optimal variable assignments; and support of fast replanning in a single model and plan.

One of the main problems for the practical use of tools and technologies of the Semantic Web is the difficulty for a non-expert user of conceiving, analyzing, extending and merging ontologies. Despite the various existing approaches for representing, editing, profiling and comparing ontologies, no integrated solution is available for domain experts. In this paper, we present an initial web-based tool developed to partially solve this issue, by simplifying the exploration, modification and profile creation of already existing annotated ontologies. DLP offers the functionality identified as fundamental for enabling a domain expert to start working and extending a partially defined semantic data source, lowering the entry barrier for learning the technicalities behind a standard ontology. Additionally, the tool allows ontology modeling experts to interact with the semantic source using the standard SPARQL language.

To keep pace with the needs of the manufacturing industry of the future, companies need to flexibly react to changing demands and be able to manage production capacities in a rapid and efficient way. This requires agile collaboration among supply chain partners in context of Service-Oriented Architectures (SOA). To this end, we propose a novel pragmatic approach for automatically implementing service-based manufacturing processes at design and runtime, called ODERU. Relying on a set of semantic annotations of business process models encoded into an extension of the BPMN 2.0 standard, it combines pattern-based semantic composition of process service plans and optimization of non-functional aspects by means of QoS-based constraint optimization problem (COP) solving. The ODERU tool is part of a platform for cloud-based elastic manufacturing. In this paper we present the foundations of ODERU, showcasing its application to two manufacturing processes with conflicting requirements showing how it solves the problem by leveraging the Everything-as-a-Service (XaaS) approach. Some initial evaluation sketches the expected benefits of such a solution, depicting its usefulness and potentialities.

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring to react rapidly and efficiently on the production capacities. Coupling it with the affirmed Service-Oriented Architectures (SOA) induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. To this end, we propose a novel pragmatic approach for automatically implementing service-based manufacturing processes at design and run-time, called ODERU. It provides an optimal plan for a business process model, relying on a set of semantic annotations and a configurable QoS-based constraint optimisation problem (COP) solving. The additional information encoding the optimal process service plan produced by means of pattern-based semantic composition and optimisation of non-functional aspects, are mapped back to the BPMN 2.0 standard formalism, through the use of extension elements, generating an enactable optimal plan. This paper presents the approach, the technical architecture and sketches two initial real-world industrial application in the manufacturing domains of metal press maintenance and automotive exhaust production.

One of the most important requirements for the manufacturing industry is the scalable and fault tolerant realization of their business processes. While there are already several propositions towards elastic process execution on cloud resources, compensation for faulty tasks often remains a manual task. In this paper, we present FCE4BPMN, which realizes a cloud-based execution environment, which can compensate business model executions exceptions on-demand. These compensations are performed just-in-time and ad-hoc, based on a semantically annotated BPMN model. Besides basic compensations, the optimization component is also capable of optimizing the overall process execution based on different QoS criteria. We, therefore, provide an extension to the BPMN standard as well as an execution environment to run business processes on cloud resources. Finally, we validate our approach based on a set of requirements originating from the manufacturing domain.

Ontology engineering is known to be a complex, time-consuming, and costly process, in particular, if an ontology has to be developed from scratch, and respective domain knowledge has to be formally encoded. This paper presents the largest publicly available manufacturing ontology CDM-Core in the standard formal ontology language OWL2. The CDM-Core ontology has been developed within the European research project CREMA in close collaboration with the user partners in order to sufficiently cover the CREMA use case domains of metal press maintenance and automotive exhaust production. CDM-Core makes use of many relevant standard vocabularies and ontologies, with only about one fifth of its size being CREMA use case specific.The practical applicability of CDM-Core for semantic annotation of domain-related process models, sensor data and services has been approved by the user partners, and its quality according to selected common criteria of verification and validation was successfully evaluated. From the public release of the CDM-Core, we expect to cover the lack of a base common ontology for the manufacturing domain, thanks to feedbacks from industrial reuse and improvements from the community.

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 se mantic web service search.

Mobile peer-to-peer (P2P) computing with applications such as for video on demand, file sharing, and video conferencing is gaining momentum based on new standards and technolo-gies such as IETF PPSP, WiFi-Direct and BitTorrent live streaming. In this paper, we describe the mobile system MyMedia, that allows users to search, share and experience videos and live recordings using P2P and at the best qual-ity possible with respect to available network capacity. In particular, the MyMedia system features a high-precision semantic P2P search and dynamic network-adaptive P2P live streaming of MPEG videos over HTTP based on the ISO/IEC standard MPEG-DASH from mobile to mobile de-vices in unstructured wireless P2P networks. Experimental evaluation of these features in terms of performance and en-ergy consumption, and a first, limited evaluation of user ac-ceptance at a film festival showed that the MyMedia system is suitable for its purpose. The MyMedia system is available as open-source software for the Android operating system.

We present the first system, called ICM-Wind, for semantics-empowered fluid condition monitoring (FCM) in wind turbines. It monitors the condition of fluids in the wind turbine gearbox, recognizes actual and the onset of failures of FCM sensors and components installed on the gearbox, and provides knowledge-based failure diagnosis support to non-experts. For this purpose, the ICM-Wind system performs semantic sensor data analysis by applying semantic technologies for interpreting the state of turbine parts and answering questions related to their maintenance. Domain knowledge is encoded in OWL2 and with SPIN rules. Fault detection and diagnosis queries are answered by use of the semantic reasoners Fact++, STAR, TopSPIN rule engine, and SwiftOWLIM store. The system prototype was successfully tested in cooperation with the HYDAC Filter Systems GmbH based on given selected samples of a two-year recording of FCM multi-sensor and operational data for two wind turbines of a regional on-shore wind farm operated by the ABO Wind AG.