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Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure/algorithm and efficient execution can present significant challenges. For example, selection of appropriate/optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts/data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The ScalaTion framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.

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Predictive analytics and simulation modeling are two complementary disciplines that will increasingly be used together in the future. They share in common a focus on predicting how systems, existing or proposed, will function. The predictions may be values of quantifiable metrics or classification of outcomes. Both require collection of data to increase their validity and accuracy. The coming era of big data will be a boon to both and will accelerate the need to use them in conjunction. This paper discusses ways in which the two disciplines have been used together as well as how they can be viewed as belonging to the same modeling continuum. Various modeling techniques from both disciplines are reviewed using a common notation. Finally, examples are given to illustrate these notions.

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.