Ontology-based Multi-perspective Task Decomposition to Support Composite Manufacturing Service Discovery

This paper explores an ontology-based multi-perspective task decomposition strategy to support composite service discovery of manufacturing resources across ubiquitous virtual enterprises in distributed manufacturing environments. A new ontology-based decomposition-supported service modeling approach is proposed for distributed management of heterogeneous manufacturing resources, enabling to add decomposition-aware semantics to manufacturing services to endow them with semantic capabilities needed for their flexible decomposition and reuse in distributed manufacturing. The multi-perspective task decomposition strategy based on description logic, production rule and workflow is used to support the decomposition and discovery of the heterogeneous manufacturing services in a meaningful manner. The proposed work can be combined with existing manufacturing service modeling and discovery works to handle much complicated service-oriented manufacturing tasks.

[1]  Deborah L. McGuinness,et al.  OWL Web ontology language overview , 2004 .

[2]  Volker Haarslev,et al.  Racer: A Core Inference Engine for the Semantic Web , 2003, EON.

[3]  Runcai Huang,et al.  Semantic Web-based Context-aware Service Selection in Task-computing , 2008, 2008 International Workshop on Modelling, Simulation and Optimization.

[4]  Fei Tao,et al.  Study on manufacturing grid & its resource service optimal-selection system , 2008 .

[5]  Andy Carpenter,et al.  Goal-Oriented Service Selection in Business Processes , 2009, 2009 Fourth International Conference on Software Engineering Advances.

[6]  K. Zhang,et al.  ManuHub: A Semantic Web System for Ontology-Based Service Management in Distributed Manufacturing Environments , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[7]  Dejan S. Milojicic,et al.  SLA Decomposition: Translating Service Level Objectives to System Level Thresholds , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[8]  Fei Tao,et al.  Resource Service Composition and Its Optimal-Selection Based on Particle Swarm Optimization in Manufacturing Grid System , 2008, IEEE Transactions on Industrial Informatics.

[9]  Jianwei Yin,et al.  Weaving a semantic grid for multidisciplinary collaborative design , 2009 .

[10]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[11]  Ramachandran Baskaran,et al.  Qualitative Analysis on Matchmaking Techniques for Web Service Discovery , 2010, Int. J. Adv. Comp. Techn..

[12]  Debasish Dutta,et al.  A Matchmaking Methodology for Supply Chain Deployment in Distributed Manufacturing Environments , 2008, J. Comput. Inf. Sci. Eng..

[13]  Naser Nematbakhsh,et al.  Reputation Improved Web Services Discovery Based on QoS , 2010, J. Convergence Inf. Technol..

[14]  E. J. Friedman-hill. Jess,et al.  The expert system shell for the java platform , 2002 .

[15]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .