Skip to main content
Log in

Study on resource service match and search in manufacturing grid system

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Resource service match and search (RSMS) is the core to realize manufacturing grid (MGrid) resource scheduling. In order to realize effectively RSMS between resource demanders and providers, a RSMS framework is proposed and the key technologies to realize it are studied. The describing information of resource services are classified into four categories: (a) word concept information, (b) sentence information, (c) number information, including number interval and fuzzy number, and (d) entity class (or data structure) information. The similarity matching algorithms of each kind of describing information are investigated, respectively, including word matching algorithms, sentence matching algorithms, number matching algorithms, and entity class matching algorithms. Based on the proposed matching algorithms, the match and search processes of MGrid resource services are divided into four phases: first, matching the basic information of resource services, such as service name and service description, namely, basic matching; second, matching the inputs and outputs information of resource services, namely, I/O matching; third, matching the quality of service (QoS) information of resource services, namely QoS matching; last, combining the above three matching results and generating an integrated matching result, namely, integrated matching. The matching functions and algorithms of each phase are described in detail. A case study illustrates the application of proposed methods, and the accuracy and efficiency of the proposed method are measured.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Qiu RG (2004) Manufacturing grid: a next generation manufacturing. 2004 IEEE International Conference on System, Man and Cybemetics (SMC2004), October 10–13. The Hague, The Netherlands, pp 4667–4672

  2. Li Z, Jin X, Cao Y, Zhang X, Li Y (2007) Conception and implementation of a collaborative manufacturing grid. Int J Adv Manuf Technol 34:1224–1235. doi:10.1007/s00170-006-0677-1

    Article  Google Scholar 

  3. Tao F, Hu YF, Zhou ZD (2008a) Study on manufacturing grid & its resources optimal-selection system. Int J Adv Manuf Technol 37(9–10):1022–1041. doi:10.1007/s00170-007-1033-9

    Article  Google Scholar 

  4. Tao F, Hu YF, Zhao DM, Zhou ZD, Zhang HJ, Lei ZZ (2008b) Study on manufacturing grid resource service QoS modeling and evaluation. Int J Adv Manuf Technol. doi. 10.1007/s00170-008-1534-1

  5. Tao F, Hu YF, Zhou ZD (2007) Application and modeling of resource service trust-QoS evaluation in manufacturing grid system. Int J Prod Research. doi:10.1080/00207540701551927

  6. Li L, Horrock I (2004) A software framework for matchmaking based on semantic web technology. Int J Electron Commerce 8(4):39–60

    Google Scholar 

  7. Paolucci M, Kawamura T, Payne T, Sycara K (2002) Semantic matching of web services capabilities. Proceedings of the First International Semantic Web Conference (ISWC 2002), June 9–12. Sardinia, Italy, pp 333–347

  8. Sycara K, Klusch M, Widoff S, Lu J (2002) Larks: dynamic matchmaking among heterogeneous software agents in cyberspace. J Autonomous Agents Multi-Agent Syst 5(2):173–203. doi:10.1023/A:1014897210525

    Article  Google Scholar 

  9. Shen ZN, Su JW (2005) Web service discovery based on behavior signatures. Proceedings of the 2005 IEEE International Conference on Service Computing (SCC’05), July 11–15. Orlando, Florida, USA, pp 279–286

  10. Perryea CA, Chuang S (2006) Community-based service discovery. The 2006 IEEE International Conference on Web Service (ICWS’06), September 18–22. Chicago, USA, pp 903–906

  11. Doulkeridis C, Zafeiris V, Norvag K, Vazirgiannis M, Giakoumakis EA (2007) Context-based caching and routing for P2P web service discovery. Distrib Parallel Databases 21(1):59–84. doi:10.1007/s10619-006-7000-x

    Google Scholar 

  12. Balken R, Haukrogh J, Jensen JL, Jensen MN, Roost LJ, Toft PN et al (2007) Context-sensitive service discovery experimental prototype and evaluation. Wirel Pers Commun 40(3):417–431. doi:10.1007/s11277-006-9200-0

    Article  Google Scholar 

  13. Raverdy PG, Issarny V (2005) Context-aware service discovery in heterogeneous networks. Proceedings of the sixth IEEE International Symposium on a Word of Wireless Mobile and Multimedia Networks (WoWMoM’05), June 13–16. Taormina, Italy, pp 478–480

  14. Lee C, Helal S (2003) Context attributes: an approach to enable context-awareness for service discovery. Proceedings of the 2003 symposium on application and the Internet (SAINT’03), January 27–31. Orlando, Florida, pp 22–30

  15. Kokash N, Birukou A, D’Andrea V (2007) Web service discovery based on past user experience. LNCS 4439:95–107

    Google Scholar 

  16. Alberto F Matteo, Cesar C, Sascha O (2007) A role-based support mechanism for service description and discovery. SOCASE 2007, LNCS 4504, pp 132–146

  17. Tomas V, Maciej Z, Matthew M (2007) Dynamic service discovery through met-interaction with service provider, ESWC 2007. LNCS 4519:84–98

    Google Scholar 

  18. Jia Y, Srikumar V, Rajlumar B (2006) A market-oriented grid directory service for publication and discovery of grid service providers and their services. J Supercomput 36(1):17–31. doi:10.1007/s11227-006-3073-6

    Google Scholar 

  19. Zisman A, Spanoudakis G (2006) UML-based service discovery framework. 4th International Conference on Service Oriented Computing (ICSOC 2006), December 4–7. Chicago, USA, LNCS 4292, pp 402–414

  20. Stollberg M, Keller U, Lausen H, Heymans S (2007) Two-phase web service discovery based on rich functional description. 4th European Semantic Web Conference (ESWC 2007), June 3–7. Tyrol region of Innsbruck, Austria, pp 99–113

  21. Bianchini D, DeAntonellis V, Melchiori M (2005) An ontology-based architecture for service discovery and advice system. Proceedings of the 16th International Workshop on Database and Expert Systems Applications (DEXA’05), August 22–26. Copenhagen, Denmark, pp 551–556

  22. Liu LL, Yu T, Shi ZB, Fang ML (2003) Self-organization manufacturing grid and its task scheduling algorithm. Comput Integr Manuf Syst 9(6):449–454

    Google Scholar 

  23. Deng H, Chen L, Wang CT, Deng QN (2006) A grid-based scheduling system of manufacturing resources for a virtual enterprise. Int J Adv Manuf Technol 28:137–141. doi:10.1007/s00170-004-2335-9. doi:10.1007/s00170-004-2388-9

    Article  Google Scholar 

  24. Chen Li Deng H, Deng Q N, Wu Z Y (2004) A research of grid manufacturing and its application in custom artificial joint. Proceedings of International Conference of Computer Science 2004(ICCS2004), April. Krakow, Poland, LNCS 3036:507–510

  25. Zhang CS, Mo R, Shi SY, Chang ZY (2006a) Research on manufacturing grid resource scheduling based on genetic algorithm. Chin Mech Eng 17(18):1916–1920

    Google Scholar 

  26. Lv BS, Shi SY, Mo R, Chang ZY, Yang HC (2006) Market equilibrium based resource optimal allocation for manufacturing grid. Comput Integr Manuf Syst 12(12):2011–2016

    Google Scholar 

  27. Tan W, Fan YS (2005) Research on service matching and composition in networked manufacturing environment. Comput Integr Manuf Syst 11(106):1408–1413

    Google Scholar 

  28. Zhang L, Yuan WZ, Wang W (2006b) An ontology based approach of automated service chaining for manufacturing grid. Chin Mech Eng 17(14):1484–1488

    Google Scholar 

  29. Zhang L, Yuan WZ, Wang W (2006c) Automotive service composition for manufacturing grid based on domain-specific ontology. J Comput Appl 26(1):57–60

    Google Scholar 

  30. Lee KM, Chio KH, Her SP, Shin DR (2005a) Matchmaking algorithms to improve dynamitic service matching in ubiquitous environments. Proceedings of Fourth Annual ACIS International Conference on Computer and Information Science (ICIS’05), July 14–16. Jeju Island, South Korea, pp 239–244

  31. Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. Proceedings of the 14th International Joint Conference on Artificial Intelligence, August 20–25. Montreal, Quebec, Canada, pp 448–453

  32. Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. International Conference on Research in Computational Linguistics (ROCLING X 1997), August 22–24. Taiwan, pp 19–33

  33. Lin LF, Gao P, Cai M, Dong JX (2005) A knowledge service-based model of collaborative manufacturing process planning for networked manufacturing. Journal of Computer-Aided Design & Computer Graphics 17(9):2085–2091

    Google Scholar 

  34. Li YH, Bandar ZA, McLean D (2003) An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans Knowl Data Eng 15(4):871–882. doi:10.1109/TKDE.2003.1209005

    Google Scholar 

  35. Li YH, McLean D, Bandar ZA, O’Shea JD, Crockett K (2006) Sentence similarity based on semantic nets and corpus statistics. IEEE Trans Knowl Data Eng 18(8):1138–1150. doi:10.1109/TKDE.2006.130

    Article  Google Scholar 

  36. Lee KH (2005b) Fuzzy number, First course on fuzzy theory and applications (Advance in Soft Computing) [M], vol 27. Springer, Berlin, pp 129–151 (ISBN:978-3-540-22988-9). 10.1007/3-540-32366-X

  37. Yang MS, Hung WL, Chang-Chien SJ (2005) On a similarity measure between LR-type fuzzy numbers and its application to database acquisition. Int J Intell Syst 20(10):1001–1016. doi:10.1002/int.20102

    Article  MATH  Google Scholar 

  38. Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352. doi:10.1037/0033-295X.84.4.327

    Article  Google Scholar 

  39. Rodriguez MA, Egenhofer MJ (2003) Determining semantic similarity among entity classes from different ontologies. IEEE Trans Knowl Data Eng 15(2):442–456. doi:10.1109/TKDE.2003.1185844

    Article  Google Scholar 

  40. Li M, Yu B, Rana O, Wang Z (2008) Grid service discovery with rough sets. IEEE Trans Knowl Data Eng 20(6):851–862. doi:10.1109/TKDE.2007.190744

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fei Tao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tao, F., Hu, Y., Zhao, D. et al. Study on resource service match and search in manufacturing grid system. Int J Adv Manuf Technol 43, 379–399 (2009). https://doi.org/10.1007/s00170-008-1699-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-008-1699-7

Keywords

Navigation