Multi-dimension Density-Based Clustering Supporting Cloud Manufacturing Service Decomposition Model
Recent years, the research on Cloud Manufacturing (CMfg) has developed extensively, especially concerning its concept and architecture. Now we propose to consider the core of CMfg within its operating model. CMfg is a service platform for the whole manufacturing lifecycle with its countless resource diversity, where organization and categorization appear to be the main drivers to build a sustainable foundation for resource service transaction. Indeed, manufacturing resources cover a huge panel of capabilities and capacities, which necessarily needs to be regrouped and categorized to enable an efficient processing among the various applications. For a given manufacturing operation e.g. welding, drilling within its functional parameters, the number of potential resources can reach unrealistic number if to consider them singular. In this paper, we propose a modified version of DBSCAN (Density-based algorithm handling noise) to support Cloud service decomposition model. Beforehand, we discuss the context of CMfg and existing Clustering methods. Then, we present our contribution for manufacturing resources clustering in a CMfg.
KeywordsCloud manufacturing Clustering algorithms DBSCAN Cloud service decomposition model
This work has been partly funded by the MOST of China through the Project Key Technology of Service Platform for CMfg. The authors wish to acknowledge MOST for their support. We also wish to acknowledge our gratitude and appreciation to all the Project partners for their contribution during the development of various ideas and concepts presented in this paper.
- 1.Hongbo, L. (2009). Web-based rapid prototyping and manufacturing systems: A review. Computers in Industry, 60(9), 643–656.Google Scholar
- 2.Botond Kádár, László Monostori (2001). Approaches to increase the performance of agent-based production systems. In Engineering of intelligent systems (Vol. 2070, pp. 612–621). New York: Lecture Notes in Computer Science. Google Scholar
- 3.Knorr, E., & Gruman, G. What cloud computing really means. InfoWorld. www.infoworld.com/d/cloud-computing/what-cloud-computing-really-means-031
- 4.Bohu, L., Lin, Z., Lei, R., Xudong, C., Fei, T., Yongliang, L., Yongzhi, W., Chao, Y., Gang, H., & Xinpei Z. (2011). Further discussion on Cloud Manufacturing. Computer Integrated Manufacturing Systems, 17(3), 449–457.Google Scholar
- 5.Estivill-Castro, V., & Yang, J. (2000). A fast and robust general purpose clustering algorithm. In Pacific Rim International Conference on Artificial Intelligence (pp. 208–218).Google Scholar
- 6.Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. Google Scholar
- 7.Rehman, M., & Atif Mehdi, S. Comparison of density-based clustering algorithms.Google Scholar
- 8.Tekbir, M., & Albayrak, S. (2010). Recursive-Partitioned DBSCAN, Signal Processing and Communications Applications Conference (SIU), (Vol. 18, pp. 113–116).Google Scholar
- 9.Smiti, A., & Elouedi, Z. (2012). DBSCAN-GM: An improved clustering method based on Gaussian means and DBSCAN techniques. Intelligent Engineering Systems (INES), 16, 573–578.Google Scholar
- 10.Dai, B.-R., & Lin, I-C. (2012). Efficient map/reduce-based DBSCAN algorithm with optimized data partition. Cloud Computing (CLOUD), 5, 59–66.Google Scholar
- 11.Tao, F., Hu, Y., Ding, Y., Sheng, B., & Zhou, Z. (2006). Resources publication and discovery in manufacturing grid. Journal of Zhejiang University SCIENCE A, 7(10), 1676–1682.Google Scholar
- 12.Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. A density-based Algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96). Google Scholar