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1 Ιαν 2019 · Prescriptive analytics is the use of data analysis tools to make suggestions for actions to be performed to attain a specified objective.
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Prescriptive analytics is the use of data analysis tools to...
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Using a step-by-step approach, readers learn a unified architecture for sensitivity, scenario, simulation, decision, and optimization analysis. Spreadsheets with numerous screenshots support visual, hands-on learning (and provide some surprising innovations).
21 Οκτ 2019 · Discover where prescriptive analytics fits and how it improves decision-making; Identify optimal solutions for achieving an objective within real-world constraints; Analyze complex systems via...
This paper proposes a modeling framework (including a set of metamodels and a set of design catalogues) for requirements analysis and design of data analytics systems. It consists of three complementary modeling views: business view, analytics design view, and data preparation view.
1 Φεβ 2020 · Another important category of methods for prescriptive analytics is Simulation. Giurgiu et al. (2017) applied simulation over the random forest model for prescribing the required improvement actions to servers by using the information available from the incident tickets.
In this paper, we present a literature review on prescriptive analytics, we frame the prescriptive analytics lifecycle and we identify the existing research challenges on this topic. To the best of our knowledge, this is the first literature review on prescriptive analytics.
This paper discusses the reusability and extensibility of simulation models and their roles in predictive and prescriptive analytics using real time data connectivity as used in SimTrack real time predictive analytics and schedule adherence.
