A Novel System on Efficient Matching, Decision Making and Distributing

DOI: 10.4236/health.2009.11010   PDF   HTML     4,816 Downloads   8,222 Views  


The object matching and distribution problem is a traditional challenge in different kinds of networks, such as kidney distribution networks. Applying differential element analysis methods, decision tree, integer linear programming the-ory and stochastic processes ideas, we propose models for the objects matching, the distribu-tion network, the exchange system and the in-dividual decision-making strategy, and thor-oughly analyze the relationship between the matching rate and the waiting time, and their impacts on the efficiency of the donor-matching process. And as the experiments, we evaluate the algorithms and system by kidney matching, decision making and distribution problems on real world data.

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Liu, S. , Liu, Y. , Liu, C. , Zhao, C. , Luo, Y. and Wen, G. (2009) A Novel System on Efficient Matching, Decision Making and Distributing. Health, 1, 51-62. doi: 10.4236/health.2009.11010.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. R. Martinez and W. L. Martinez, Computational sta-tistics handbook with MATLAB, CHAPMAN & HALL/ CRC.
[2] A. Schrijver, (1986) Theory of linear and integer pro-gramming, Wiley-Interscience Series in Discrete Mathematics.
[3] B. B. Dunaev, (1986) Statistical model of change in population numbers, Cybernetics and Systems Analysis, 22(4), July.
[4] E. Newcomer and G. Lomow, (2004) Understanding SOA with web services (independent technology guides), Addison-Wesley Professional.
[5] Y. Hao, Y. H. Ding, G. H. Wen, (2001) SOAP protocol and its application [J], Computer Engineering, 27(6), 128-130.
[6] J. R. Quinlan, (1993) C4.5: Programs for machine learn-ing, Morgan Kaufmann Publishers Inc., March.
[7] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone (1984), Classification and regression trees, Wadsworth International Group, Belmont, California.
[8] J. R. Quinlan, (1986) Induction of decision trees, Ma-chine Learning, (1), 81-106.
[9] M. Mehta, R. Agrawal, and J. Rissanen (1996) SLIQ: A fast scalable classifier for data mining, Extending Data-base Technology.
[10] C. L. Li and L. Y. Li (2003) Apply agent to build service management [J], Journal of Network and Computer Ap-plications, 26, 323-340.
[11] M. Keen, A. Acharya, S. Bishop, A. Hopkins, S. Mil-inski, C. Nott, R. Robinson, J. Adams, and P. Ver-schueren, (2005) Patterns: Implementing an SOA using an enterprise service bus.
[12] OPTN, SRTR. (2005) The U. S. organ procurement and transplantation network and the scientific registry of transplant recipients, 2005 OPTN / SRTR Annual Re-port.
[13] S. Oliver, V. B. Prasad, D. Nigel, et a1., (2003) Leveraging the grid to provide a global platform for ubiquitous com-puting research, Technica1 Report Lancaster University, CSEG/2/03 [EB/OL].
[14] Shafer, Agrawal, Mehta, (1996) SPRINT: A scalable paral-lel classifier for data mining [C], Proceedings of the 22nd International Conference on Very Large Data Bases Mum-bai, India.
[15] H. Yamada and T. Kasvand, (1986) DP matching method for recognition of occluded, reflective and transparent objects with unconstrained background and illumination, in Proc. 8th Int. Conf. Pattern Recog., Paris, 95-98.
[16] E. Newcomer and G. Lomow, (2005) Understanding SOA with web services, Addison Wesley, ISBN 0-321- 18086-0.
[17] M. Bell, (2008) Introduction to service-oriented model-ing, service-oriented modeling: service analysis, design, and architecture, Wiley & Sons, 3, ISBN 978-0-470- 14111-3.
[18] T. Erl, (2005) Service-oriented architecture: Concepts, technology, and design, Upper Saddle River: Prentice Hall PTR, ISBN 0-13-185858-0.

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