Pattern recognition for cache management in distributed medical imaging environments.
Carlos Viana-Ferreira, Luís Ribeiro, Sérgio Matos, Carlos Costa
Author Information
Carlos Viana-Ferreira: Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal. c.ferreira@ua.pt.
Luís Ribeiro: Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal. luisribeiro@ua.pt.
Sérgio Matos: Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal. aleixomatos@ua.pt.
Carlos Costa: Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal. carlos.costa@ua.pt.
PURPOSE: Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon. METHODS: This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated. RESULTS: The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75% of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism. CONCLUSIONS: The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.
