Abstract
Software architectures that allow researchers to explore advanced modeling by scaling horizontally in the cloud can lead to new insights and improved accuracy of modeling results. We propose a generalized highly scalable information system architecture that researchers can employ in predictive analytics research for working with both historical data and real-time temporally structured big data. The proposed architecture is fully automated and uses the same analytical software for both training and live predictions.
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Westerlund, M., Hedlund, U., Pulkkis, G., Björk, KM. (2014). A Generalized Scalable Software Architecture for Analyzing Temporally Structured Big Data in the Cloud. In: Rocha, Á., Correia, A., Tan, F., Stroetmann, K. (eds) New Perspectives in Information Systems and Technologies, Volume 1. Advances in Intelligent Systems and Computing, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-319-05951-8_53
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DOI: https://doi.org/10.1007/978-3-319-05951-8_53
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05950-1
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