Desktop version

Home arrow Computer Science arrow Applied Big Data Analytics in Operations Management

CONCLUSION

To perform prediction analysis over large scale data, coming from operation management process, different prediction analytics techniques can be used. MapReduce is resulting as a great solution for processing the large volume of data using a parallel framework. There is a need to modify the conventional techniques for extracting information from this type of data using parallel framework of MapRecue. These modified parallel techniques have been discussed in this chapter. In order to achieve efficiency and scalability MapReduce framework based methods are used. These techniques helps in predicting different behaviours of operations. There are several other techniques which can be used for similar purpose.

REFERENCES

Halper, F. (2014). Predictive Analytics for Business Advantage. TDWI Research.

Butler, M. (2013). Predictive Analytics in Business. Butler Analytics.

Buytendijk, F., & Trepanier, L. (2010). Predictive Analytics: Bringing the tools to the data. Redwood Shores, CA: Oracle Corporation.

Song, Y., Alatorre, G., Mandagere, N., & Singh, A. (2013, June). Storage mining: where IT management meets big data analytics. In Big Data (BigData Congress), 2013 IEEE International Congress on (pp. 421-422). IEEE. doi:10.1109/BigData. Congress.2013.66

Islek, I., & Oguducu, S. G. (2015, June). A retail demand forecasting model based on data mining techniques. In Industrial Electronics (ISIE), 2015 IEEE 24th International Symposium on (pp. 55-60). IEEE doi:10.1109/ISIE.2015.7281443

Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113. doi:10.1145/1327452.1327492

Lammel, R. (2008). Google’s MapReduce programming model. Science of Computer Programming, 70(1), 1-30. doi:10.1016/j.scico.2007.07.001

Han, J., & Kamber, M. (2001). Data mining: Concepts and techniques. Morgan Kauffman.

Elkan, C. (2013). Predictive analytics and data mining. Retrieved from http://www. cseweb.ucsd.edu

Liu, Z., Li, H., & Miao, G. (2010, August). MapReduce-based backpropagation neural network over large scale mobile data. In Natural Computation (ICNC), 2010 Sixth International Conference on (Vol. 4, pp. 1726-1730). IEEE. doi:10.1109/ ICNC.2010.5584323

Sun, Z., & Fox, G. (2012, January). Study on parallel SVM based on MapReduce. In

Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA).

Cheng, Y. H., Hai-Wei, L., & Chen, Y. S. (2006, June). Implementation of a back- propagation neural network for demand forecasting in a supply chain-A practical case study. In Service Operations and Logistics, and Informatics, 2006. SOLI’06. IEEE International Conference on (pp. 1036-1041). IEEE.

Sarhani, M., & El Afia, A. (2014, November). Intelligent system based support vector regression for supply chain demand forecasting. In Complex Systems (WCCS), 2014 Second World Conference on (pp. 79-83). IEEE. doi:10.1109/ICoCS.2014.7060941

Predictive Analytics. (2015). In Wikipedia. Retrieved August 27, 2015, from https:// en.wikipedia.org/wiki/Predictive_analytics

What is Predictive Analytics ? (2015). Retrieved August 27, 2015, from, http://www. predictiveanalyticsworld.com/predictive_analytics.php

Big Data Analytics and Predictive Analytics. (2015). Retrieved August 30, 2015, from, http://www.predictiveanalyticstoday.com/big-data-analytics-and-predictive-analytics

Backpropagation. (2015). In Wikipedia. Retrieved September 9, 2015, from https:// en.wikipedia.org/wiki/Backpropagation

Support Vector Machine. (2015). In Wikipedia. Retrieved September 15, 2015, from https://en.wikipedia.org/wiki/Support_vector_machine

Operations Management. (2015). Retrieved November 18, 2015, from https://ids355. wikispaces.com/Ch.+3+Forecasting

 
Source
< Prev   CONTENTS   Source   Next >