On SAGE Insight: Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system

From SIMULATION

The SCS Editor’s Choice Award 2018

As a means of forecasting for a diversified society, the phrase “big data” has become widespread. The word means data sets with sizes beyond the ability of common software tools to capture, curate, manage, and process within a tolerable elapsed time. Recently, big data has received greater attention in diverse research fields, including medicine, science, engineering, management, defense, politics, and others. Such research uses big data to predict target systems, thereby constructing a model of the system in two ways: data modeling and simulation modeling.

This paper clarifies the difference between the two modeling approaches, (ii) explains their advantages and limitations and compares each characteristic, and presents a complementary cooperation modeling approach. This approach combines the merits of both with two issues: in-depth consideration of existing modeling; and empirical analysis with the proposed approach. The proposed modeling is applied to develop a greenhouse control system in the real world. Finally, it is expected that this modeling approach will be an alternative modeling approach in the big data era.

Abstract

Recently, big data has received greater attention in diverse research fields, including medicine, science, engineering, management, defense, politics, and others. Such research uses big data to predict target systems, thereby constructing a model of the system in two ways: data modeling and simulationmodeling. Data modeling is a method in which a model represents correlation relationships between one set of data and the other set of data. On the other hand, physics-based simulation modeling (or simply simulation modeling) is a more classical, but more powerful, method in which a model represents causal relationships between a set of controlled inputs and corresponding outputs. This paper (i) clarifies the difference between the two modeling approaches, (ii) explains their advantages and limitations and compares each characteristic, and (iii) presents a complementary cooperation modeling approach. Then, we apply the proposed modeling to develop a greenhouse control system in the real world. Finally, we expect that this modeling approach will be an alternative modeling approach in the big data era.

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Article details
Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system
Byeong Soo Kim, Bong Gu Kang, Seon Han Choi, Tag Gon Kim
First Published June 9, 2017
DOI: 10.1177/0037549717692866
From SIMULATION

 

 

     
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