Abstract
The challenging task for streaming data is to store, analyze and visualize massive volumes of data using systems with memory and run-time constraints. This paper presents a novel FPGA-based system to accelerate the classifier building process over data streams. The proposed system maps efficiently the well-known Very Fast Decision Tree (VFDT) streaming algorithm on a single custom processor in a high-end Convey HC-2ex FPGA platform. The experimental results show that the proposed system outperforms by two orders of magnitude the software-based solutions for streaming data processing, whereas the available resources of the platform allow for substantial further scaling.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. ACM, August 2000
Panda, B., Herbach, J.S., Basu, S., Bayardo, R.J.: Planet: massively parallel learning of tree ensembles with mapreduce. Proceedings of the VLDB Endowment 2(2), 1426–1437 (2009)
Yin, W., Simmhan, Y., Prasanna, V. K.: Scalable regression tree learning on Hadoop using OpenPlanet. In: Proceedings of Third International Workshop on MapReduce and its Applications Date, pp. 57–64. ACM, June 2012
He, Q., Tan, Q., Ma, X., Shi, Z.: The High-Activity Parallel Implementation of Data Preprocessing Based on MapReduce. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS, vol. 6401, pp. 646–654. Springer, Heidelberg (2010)
Tyree, S., Weinberger, K. Q., Agrawal, K., Paykin, J.: Parallel boosted regression trees for web search ranking. In: Proceedings of the 20th International Conference on World Wide Web, pp. 387–396. ACM, March 2011
Bifet, A., Holmes, G., Pfahringer, B., Read, J., Kranen, P., Kremer, H., Jansen, T., Seidl, T.: MOA: A Real-Time Analytics Open Source Framework. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 617–620. Springer, Heidelberg (2011)
De Francisci Morales, G.: SAMOA: A platform for mining big data streams. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 777–778. International World Wide Web Conferences Steering Committee, May 2013
Luo, Y., Xiang, K., Li, S.: Acceleration of decision tree searching for IP traffic classification. In: Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems, pp. 40–49. ACM, November 2008
Papadonikolakis, M., Bouganis, C. S., Constantinides, G.: Performance comparison of GPU and FPGA architectures for the SVM training problem. In: International Conference on Field-Programmable Technology, FPT 2009, pp. 388–391. IEEE, December 2009
Narayanan, R., Honbo, D., Memik, G., Choudhary, A., Zambreno, J.: An FPGA implementation of decision tree classification. In: Design, Automation & Test in Europe Conference & Exhibition, DATE 2007, pp. 1–6. IEEE, April 2007
Chrysos, G., Dagritzikos, P., Papaefstathiou, I., Dollas, A.: HC-CART: A parallel system implementation of data mining classification and regression tree (CART) algorithm on a multi-FPGA system. ACM Transactions on Architecture and Code Optimization (TACO) 9(4), 47 (2013)
Dollas, A.: Big Data Processing with FPGA Supercomputers: Opportunities and Challenges. In: Proceedings of the IEEE Computer Society Annual Symposium on VLSI. ISVLSI, pp. 474–479. IEEE, July 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Giakoumakis, P., Chrysos, G., Dollas, A., Papaefstathiou, I. (2015). Acceleration of Data Streaming Classification using Reconfigurable Technology. In: Sano, K., Soudris, D., Hübner, M., Diniz, P. (eds) Applied Reconfigurable Computing. ARC 2015. Lecture Notes in Computer Science(), vol 9040. Springer, Cham. https://doi.org/10.1007/978-3-319-16214-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-16214-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16213-3
Online ISBN: 978-3-319-16214-0
eBook Packages: Computer ScienceComputer Science (R0)