Abstract
The outcomes and quality of organizational decisions depend on the characteristics of the data available for making the decisions and on the value of the data in the decision-making process. Toward enabling management of these aspects of data in analytics, we introduce and investigate Data Readiness Level (DRL), a quantitative measure of the value of a piece of data at a given point in a processing flow. Our DRL proposal is a multidimensional measure that takes into account the relevance, completeness, and utility of data with respect to a given analysis task. This study provides a formalization of DRL in a structured-data scenario, and illustrates how knowledge of rules and facts, both within and outside the given data, can be used to identify those transformations of the data that improve its DRL.
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This material is based upon work supported in whole or in part with funding from the Laboratory for Analytic Sciences (LAS). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the LAS and/or any agency or entity of the United States Government.
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Alborzi, F., Chirkova, R., Doyle, J., Fathi, Y. (2015). Determining Query Readiness for Structured Data. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_1
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DOI: https://doi.org/10.1007/978-3-319-22729-0_1
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