Abstract

Big data has been an emerging problem these days. To solve this problem Hadoop has evolved as a most widely used tool and adopted by various popular MNCs like Facebook and Yahoo. To search large number of pattern in big data is a challenging task. Map/Reduce is used to write codes to perform pattern matching on big data. In this work OpenCL is combined with Apache Hadoop to write fast Map/Reduce for pattern matching in data using suffix arrays.

References

• Cheikh Kacfah Emani, Nadine Cullot and Christophe Nicolle “Understandable Big Data: A survey” in Computer Science Review Volume 17, August 2015, Pages 70–81.
• H. Hu, Y. Wen, T.-S. Chua, and X. Li, ‘‘Towards scalable systems for big data analytics: A technology tutorial,’’ IEEE Access, vol. 2, pp. 652–687, 2014.
• Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reitererand, S., Stettinger, M.: Basic approaches in recommendation systems. In: Robillard, M., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, Chap. 2. Springer, Heidelberg (2014).
• S. Meng, W. Dou, X. Zhang and J. Chen, "KASR: A keyword-aware service recommendation method on Map-Reduce for big data application", IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 12, pp. 3221-3231, 2014.
• M. Grossman, M. Breternitz and V. Sarkar, "HadoopCL: Map-Reduce on Distributed Heterogeneous Platforms Through Seamless Integration of Hadoop and OpenCL", Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing Workshops and PhD Forum, pp. 1918-1927.
• A. Rabkin and R. H. Katz, "How Hadoop Clusters Break," IEEE Software, vol. 30, pp. 88-94, 2013.
• P. Jaaskelainen, C. Lama, P. Huerta, and J. Takala, "OpenCL-based design methodology for application-specific processors," Embedded Computer Systems (SAMOS), 2010 International Conference, pp. 223- 230, 2010.
• Gupta, K.G., Agrawal, N. and Maity, S.K., "Performance analysis between aparapi (a parallel API) and JAVA by implementing sobel edge detection Algorithm," in PARCOMPTECH, Bangalore, Feb. 2013, pp. 1-5.
• Niwattanakul S, Singthongchai J, Naenudorn E, Wanapu S. Using of Jaccard coefficient for keywords similarity. In: Proc. of the international multi conference of engineers and computer scientists, vol I; 2013. p. 380–4.
• A. Huang, Similarity measures for text document clustering, in: Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC2008), Christchurch, New Zealand, 2008, pp. 49–56.
• P. WillettThe Porter stemming algorithm: then and now Program: Electr Libr Inform Syst, 40 (3) (2006), pp. 219–223.
• Wang Jun, Li Lei and Ren Fuji, "An Improved method of Keywords Extraction Based on Short Technology Text", International Conference on Natural Language processing and Knoledge Engineering (NLP-KE), pp. 1-6.
• Adomavicius G., Kwon Y.: New recommendation techniques for multicriteria rating systems. IEEE Intel. Syst. 22(3), 48–55 (2007).
• X. Zhang, J.-J. Lu, X. Qin and X.-N. Zhao, "A high-level energy consumption model for heterogeneous data centers", Simul. Model. Pract. Theory, vol. 39, pp. 41-55, 2013 .
• X. Peng and Z. Sai, ``A low-cost power measuring technique for virtual machine in cloud environments,'' Int. J. Grid Distrib. Comput., vol. 6, no. 3, p. 69, 2013.
• Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253 (2011).
• Hadoop Architecture. Image Online: http://ercoppa.github.io/HadoopInternals/HadoopArchitectureOverview.html