Share This Article:

A Novel System on Efficient Matching, Decision Making and Distributing

Abstract Full-Text HTML Download Download as PDF (Size:1336KB) PP. 51-62
DOI: 10.4236/health.2009.11010    4,612 Downloads   7,951 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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

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.


[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.

comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.