Abstract
This paper describes the MineFleet
distributed vehicle performance data stream mining system designed for commercial fleets. The MineFleet Onboard analyzes high throughput data streams onboard the vehicle, generates the analytics, and sends them to the remote server over the wireless networks. The paper describes the overall architecture of the system, business needs, and shares experience from successful large-scale commercial deployments. MineFleet is probably one of the first distributed data stream mining systems that is widely deployed at the commercial level. The paper discusses an important problem in the context of the MineFleet
application—computing and detecting changes in correlation matrices in a resource-contrained device that are typically used onboard the vehicle. The problem has immediate connection with many vehicle performance data stream analysis techniques such as principal component analysis, feature selection, and building predictive models for vehicle subsystems.
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References
Alon, N., Matias, Y., Szegedy, M.: The space complexity of approximating the frequency moments. In: Proceedings of the ACM Symposium on Theory of Computing, pp. 20–29 (1996)
Alqallaf, F., Konis, K., Martin, R., Zamar, R.: Scalable robust covariance and correlation estimates for data mining. In: ACM Press (ed.) Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 14–23 (2002)
Falk, R., Well, A.: Many faces of the correlation coefficient. Journal of Statistics Education 5(3) (1997)
Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M., Handy, D.: Vedas: A mobile and distributed data stream mining system for real-time vehicle monitoring. In: Proceedings of the SIAM International Data Mining Conference, Orlando (2004)
Kargupta, H., Chan, P.: Advances in Distributed and Parallel Knowledge Discovery. AAAI/MIT Press (2000)
Kargupta, H., Puttagunta, V., Klein, M.: On-board vehicle data stream monitoring using minefleet and fast resource constrained monitoring of correlation matrices. Special issue of New Generation Computing Journal on Learning from Data Streams 25(1), 5–32 (2007)
Kargupta, H., Sivakumar, K.: Existential pleasures of distributed data mining. In: Next Generation Data Mining: Future Directions and Challenges. MIT/AAAI Press (2004)
Srivastava, A.N., Stroeve, J.: Onboard detection of snow, ice, clouds and other geophysical processes using kernel methods. In: Proceedings of the ICML 2003 Workshop on Machine Learning Technologies for Autonomous Space Sciences (2003)
Weldon, K.L.: A simplified introduction to correlation and regression. Journal of Statistics Education 8(3) (2000)
Zue, Y., Shasha, D.: Statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th VLDB Conference, Hong Kong, China (2002)
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Kargupta, H. et al. (2010). MineFleet
: The Vehicle Data Stream Mining System for Ubiquitous Environments.
In: May, M., Saitta, L. (eds) Ubiquitous Knowledge Discovery. Lecture Notes in Computer Science(), vol 6202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16392-0_14
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DOI: https://doi.org/10.1007/978-3-642-16392-0_14
Publisher Name: Springer, Berlin, Heidelberg
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