本研究為解決在巨量資料分析下所產生之主記憶體不足之問題,設計虛擬矩 陣架構,透過虛擬矩陣架構提供快速、高效能的矩陣操作及運算,並降低巨量資 料在運算時所佔據之主記憶體容量。並結合 R 語言,提供 R 語言巨量資料分析、 高速矩陣運算之能力。

1. 林思吟. (2006). 「中國上市公司財務危機預警模型研究」 政治大學金融研究所學位論文, 1-51.
2. 西內啟著, 陳亦苓譯（2016）「統計學，最強的商業武器 從買樂透到大數據，全都離不開統計學；不懂統計學，你就等著被騙吧！」悅知文化出版社.
3. Adler, D., Nenadic, O., Zucchini, W.& Glaser, C. (2008). The ff Package: Handling Large Data Sets in R with Memory Mapped Pages of Binary Flat Files.
4. Anton, H. (2010). Elementary linear algebra. John Wiley & Sons
5. Asymptotix (2011). Integrating RevoDeployR from Revolution through RESTful API or XML-RPC with .NET or Drupal. Retrieved from http://www.asymptotix.eu/news/integrating-revodeployr-revolution-through-restful-api-or-xml-rpc-net-or-drupal
6. Beyer, M. A.& Laney, D. (2012). The Importance of ‘Big Data’: A Definition. Stamford, CT: Gartner, 2014-2018.
7. Bivand, R. S. (2000). Using the R statistical data analysis language on GRASS 5.0 GIS database files. Computers & Geosciences, 26(9), 1043-1052.
8. Boyland, J. T. (2005, July). Handling Out of Memory Errors. In ECOOP 2005 Workshop on Exception Handling in Object-Oriented Systems.
9. Cai, X., Nie, F., & Huang, H. (2013, August). Multi-View K-Means Clustering on Big Data. In IJCAI.
10. Constantine A. C., Tim P. (2013). High-Volume Data Collection and Real Time Analytics Using Redis. Retrieved from http://conferences.oreilly.com/strata/strata2013/public/schedule/detail/27350
11. Cribari-Neto, F., & Zarkos, S. G. (1999). R: Yet another econometric programming environment. Journal of Applied Econometrics, 14(3), 319-329.
12. Debasis, S. (2009). Classic Data Structures 2Nd Ed. PHI Learning Pvt. Ltd..
13. Derksen, S. & Keselman, H. J. (1992). Backward, Forward and Stepwise Automated Subset Selection Algorithms: Frequency of Obtaining Authentic and Noise Variables. British Journal of Mathematical and Statistical Psychology, 45(2), 265-282.
14. Golub, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). JHU Press.
15. Han, J., Haihong, E., Le, G. & Du, J. (2011, October). Survey on NoSQL Database. In Pervasive Computing and Applications (ICPCA), 2011 6th International Conference on IEEE，363-366.
16. IBM (2015). The Four V's of Big Data. Retrieved from http://www.ibmbigdatahub.com/infographic/four-vs-big-data
17. Ihaka, R.& Gentleman, R. (1996). R: a Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314.
18. Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note, 6, 70.
19. Matloff, N. (2008). R for Programmers. University of California.
20. Ordonez, C., Zhang, Y., & Cabrera, W. (2016). The Gamma matrix to summarize dense and sparse data sets for big data analytics. IEEE Transactions on Knowledge and Data Engineering, 28(7), 1905-1918.
21. Rabl, T., Gómez-Villamor, S., Sadoghi, M., Muntés-Mulero, V., Jacobsen, H. A.& Mankovskii, S. (2012). Solving Big Data Challenges for Enterprise Application Performance Management. Proceedings of the VLDB Endowment, 5(12), 1724-1735.
22. Scott, J. A. (2015). Getting Started with Apache Spark. MapR Technologies.
23. Team, R. C. (2000). R Language Definition. Vienna, Austria: R Foundation for Statistical Computing.
24. Venables, W. N.& Smith, D. M. (2009). An Introduction to R. Network Theory Limited.
25. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., Mccauley, M.& Stoica, I. (2012). Fast and Interactive Analytics Over Hadoop Data with Spark. USENIX Login,37(4),45-51.
26. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M.& Stoica, I. (2012, April). Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In Proceedings of The 9th USENIX Conference on Networked Systems Design and Implementation, USENIX Association,2-2.