Abstract
Modern data analytic flows involve complex data computations that may span multiple execution engines and need to be optimized for a variety of objectives like performance, fault-tolerance, freshness, and so on. In this paper, we present optimization techniques and tradeoffs in terms of a real-world, cyber-physical flow that starts with raw time series sensor data and external event data, and through a series of analytic operations produces automated actions and actionable insights.
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Simitsis, A., Gupta, C., Wilkinson, K., Dayal, U. (2012). Optimizing Flows for Real Time Operations Management. In: Ailamaki, A., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2012. Lecture Notes in Computer Science, vol 7338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31235-9_44
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DOI: https://doi.org/10.1007/978-3-642-31235-9_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31234-2
Online ISBN: 978-3-642-31235-9
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