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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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This article presents an application of the Discrete Event System Specification (DEVS) framework to the design and safety analysis of a real-time embedded control system, a railroad crossing control system. The authors employ an extension of the DEVS formalism, real-time DEVS (RT-DEVS), which has a sound semantics for the specification of real-time systems in a hierarchical modular fashion. The notion of a clock matrix for communicating RT-DEVS models is proposed, which represents a global time between the models. Based on the composition rules and the clock matrix, an algorithm for the generation of a timed reachability tree is developed that can be used for safety analysis at two phases: an untimed and timed analysis phase. A railroad crossing control example demonstrates that the proposed analysis for RT-DEVS models would be effective to verify the safety property of real-time control systems.

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