Function and service pattern analysis for reconfiguring collaboration systems

Recently, manufacturing companies tried to increase competitiveness in their business collaboration with cooperative companies rather than within their own company. In Korea, more than 600 manufacturing companies are using web-based collaboration systems developed by the government-led project, referred to as i-Manufacturing. The project is being conducted to facilitate on-line collaboration among manufacturing companies since 2004. There are lots of functions and services supported by each collaboration system. In order to re-apply a developed system to other companies, however, we have to modify, upgrade, or newly develop some parts of the system, which is called as customization. During customization processes, we reconfigure functions or services of the system to satisfy user requirements. To facilitate reconfiguration of collaboration systems, therefore, we first define user patterns. Then we introduce an analysis technique to investigate and analyze patterns by using data mining. The analysis technique verifies normal or abnormal patterns(e.g., drastic increase in using a specific function or service), and automatically make the system recognize an abnormal pattern as a new normal pattern when the abnormal pattern continue for a long time. Including the analysis technique, we also suggest a reconfiguration guideline for reorganizing function modules into services in a specific collaboration system.

[1]  Yong-Soo Kim,et al.  The Design and Implementation of Anomaly Traffic Analysis System using Data Mining , 2008, Int. J. Fuzzy Log. Intell. Syst..

[2]  Robert W. Brennan,et al.  Developments in dynamic and intelligent reconfiguration of industrial automation , 2008, Comput. Ind..

[3]  Young-Koo Lee,et al.  Sliding window-based frequent pattern mining over data streams , 2009, Inf. Sci..

[4]  Yue-Shi Lee,et al.  Incremental and interactive mining of web traversal patterns , 2008, Inf. Sci..

[5]  Hon-Zong Choi,et al.  i-Manufacturing: Korean-style Manufacturing Innovation Strategy Promoting Balanced Regional Growth Based on Collaboration , 2008, 2008 International Conference on Smart Manufacturing Application.

[6]  Richard Nock,et al.  Mining evolving data streams for frequent patterns , 2007, Pattern Recognit..

[7]  Talib S. Hussain,et al.  Adaptive reconfiguration of data networks using genetic algorithms , 2004, Appl. Soft Comput..

[8]  Hwang-Bin Ryou,et al.  Anomaly Detection Scheme Using Data Mining in Mobile Environment , 2003, ICCSA.

[9]  José Luis Martín,et al.  Tornado: A self-reconfiguration control system for core-based multiprocessor CSoPCs , 2007, J. Syst. Archit..

[10]  Enver Yücesan,et al.  CPM: A collaborative process modeling for cooperative manufacturers , 2007, Adv. Eng. Informatics.

[11]  Harold S. Javitz,et al.  The NIDES Statistical Component Description and Justification , 1994 .

[12]  Kwangyeol Ryu,et al.  i-Manufacturing project for collaboration-based Korean manufacturing innovation , 2008, PICMET '08 - 2008 Portland International Conference on Management of Engineering & Technology.

[13]  Evangelos Triantaphyllou,et al.  Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications , 2008, Series on Computers and Operations Research.

[14]  Sheng-Tun Li,et al.  A web-aware interoperable data mining system , 2002, Expert Syst. Appl..