Abstract
Retrieved data from sensors may have a high level of quality to ensure crucial decisions and determine effective strategies. Nowadays, in view of the mass of generated information from these data, there is a real need to handle their quality. This paper propose new indices for quantifying the variability/stability of a data flow according to a data modeling that handles data imperfection. To deal with the data imprecision, we adopt a quantile-based approach. Our index definitions use parameters. Hence, to obtain an efficient judgments by this approach, we examine the choice of the appropriate parameters, and how it can affect the judgment on the harvest stability.
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ben Othmane, Z., de Runz, C., Younes, A.A., Mercelot, V. (2019). Effect of Imprecise Data Income-Flow Variability on Harvest Stability: A Quantile-Based Approach. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_19
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DOI: https://doi.org/10.1007/978-3-030-27615-7_19
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