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Automated Predictive Big Data Analytics Using Ontology Based Semantics.

Mustafa V Nural¹, Michael E Cotterell¹, Hao Peng¹

¹Department of Computer Science, Statistics University of Georgia, Athens.

International Journal of Big Data

|April 17, 2018

Summary

This summary is machine-generated.

This study introduces semantic technology to aid data scientists in selecting optimal predictive analytics models for big data. An Analytics Ontology and SCALATION framework facilitate semi-automated model selection and rationale explanation.

Keywords:

big-data-analytics model-selection ontology semantics

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Area of Science:

Data Science
Artificial Intelligence
Semantic Technology

Background:

Predictive analytics is crucial in the big data era.
Selecting optimal modeling techniques and algorithms for big data presents significant challenges.
Expertise for model selection may not always be readily available.

Purpose of the Study:

To leverage semantic technology for assisting data analysts and scientists.
To provide support in selecting appropriate modeling techniques and building specific models.
To offer the rationale behind selected techniques and models.

Main Methods:

Development of the Analytics Ontology for formal description of modeling techniques, models, and results.
Utilizing the Analytics Ontology to support inferencing for semi-automated model selection.
Employing the SCALATION framework, supporting over thirty predictive big data analytics techniques, as a testbed.

Main Results:

Demonstration of semantic technology's utility in assisting predictive analytics model selection.
Formalization of modeling techniques and rationale through the Analytics Ontology.
Evaluation of the semantic technology approach within the SCALATION framework.

Conclusions:

Semantic technology can effectively assist in the selection of predictive big data analytics models.
The developed Analytics Ontology enables semi-automated model selection and provides clear rationale.
The SCALATION framework serves as a viable platform for evaluating such semantic approaches.