A Hierarchical Data Access(HDA) Method in Enterprise Distributed Cluster Environment

In enterprise computing, clustering is a useful architecture because most companies have a need for high-availability, high-performance and stabilized load-balanced computing power. However, when we use clustering architecture, we must consider a data control method. Representative of the two methods of data control are centralization and distribution. In this paper, we will propose the hybrid method and we will introduce our system implementing the stock trading system method of Meritz Securities. Our system has three hierarchical layers and is operated with some special method we called the HDA. This HDA method has some useful factors for our data control method.

[1]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[2]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[3]  J. Efrim Boritz,et al.  Effectiveness of neural network types for prediction of business failure , 1995 .

[4]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[5]  Andreas Charitou,et al.  ANNALS OF OPERATIONS RESEARCH , 2000 .

[6]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[7]  O. Mangasarian.  Linear and Nonlinear Separation of Patterns by Linear Programming , 1965 .

[8]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[9]  Mu-Chen Chen,et al.  Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..

[10]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[11]  Hai Jin,et al.  Cluster Computing Tools Applications and Australian Initiatives for Low Cost Super Computing , 2000 .

[12]  M. Peel,et al.  Predicting corporate failure— Some results for the UK corporate sector , 1986 .

[13]  Hemant K. Bhargava,et al.  Beyond Spreadsheets: Tools for Building Decision Support Systems , 1999, Computer.

[14]  Parag C. Pendharkar,et al.  A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem , 2005, Comput. Oper. Res..

[15]  Michael T. Dugan,et al.  The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher , 2001 .

[16]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[17]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[18]  Jonathan Crook,et al.  Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms , 1997 .

[19]  Chaochang Chiu,et al.  A case-based customer classification approach for direct marketing , 2002, Expert Syst. Appl..

[20]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[21]  Yong Woo Lee.  Seoul Grid Portal: A Grid Resource Management System for Seoul Grid Testbed , 2004, GCC.

[22]  Yongwoo Lee,et al.  Web Service for Seoul Grid Testbed , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[23]  Murugan Anandarajan,et al.  Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks , 2001, Intell. Syst. Account. Finance Manag..

[24]  T. Abdelwahed,et al.  New evolutionary bankruptcy forecasting model based on genetic algorithms and neural networks , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[25]  Geert Wets,et al.  Using association rules for product assortment decisions: a case study , 1999, KDD '99.

[26]  Ivan Marsic,et al.  Hybrid cluster computing with mobile objects , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.

[27]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1996 .

[28]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[29]  J. Wiginton.  A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior , 1980, Journal of Financial and Quantitative Analysis.

[30]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.