Abstract
In this paper we explore the scalability and distribution of a spatial data stream processing system by expanding a previously-proposed deep neural network-based classification system. Three distributions are studied: a centrally controlled cluster, a peer-to-peer system, and a hybrid of both. We also study the effect of expanding the number of classes being classified, including the gradual addition of classes to be classified to the neural network. We find that the deep neural network classification system does expand to more classes though it is not amenable to the gradual addition of classes during the networks training and that this is not improved by pre-training the network on all classes. We also find that the network performs asymmetrically depending on the number of classes being classified with optimal accuracy occurring with about half to a quarter as many classes as output nodes in the network. We finish this paper offering some recommendations to potentially use this system in a distributed manner to increase accuracy for the total set of classes.
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We thank for reviewers of our paper for their constructive comments.
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Clarke, K., Osborn, W. (2023). The Effects of Scale and Distribution on a Deep Neural Network Iterative Classification System of Spatial Data Streams. In: Barolli, L. (eds) Advances in Networked-based Information Systems. NBiS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-031-40978-3_3
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DOI: https://doi.org/10.1007/978-3-031-40978-3_3
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