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

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. 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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