## 1 Introduction
Data service servers [11] monitor and manage data flow and enable them to interact with users. DIaaS (data integrity as a service) [12] is implemented by smart contracts on the blockchain, which is fully decentralized. EIS supports the business expansion and operation of developers, taking advantage of the deployment and management of cloud service infrastructure. Services on edge computing rely on the adequate decomposition of the system into ECN (edge computing node) or SMCs (state machine components), which form a service composition/orchestration, where particular operations are executed successively. The model described by a Petri net can be decomposed into two nets that are able to exchange messages upon the occurrence of observable events. An SPN (stochastic Petri net) model represents and evaluates the performance of an edge computing architecture. Service composition for edge intelligence, particularly for an AI subtask composition, is carried out on an EdaaS (edge device as a service), which has a particular functionality and a set of non-functional features. Data science and big data signify the growing importance of data-based approaches. Real behavior can be reconstructed from data event logs [13]. Process mining techniques provide a wide range of data-driven methods that are process-centric at the same time, which support discovering process models as well as behavior verification and process discovery in past executions of processes [14]. Since the generated simulation model is supported by historical data (event data), which is based on the DES (discrete event simulation) technique, the generated event data are similar to the behavior of the real process. In all of the proposed tools for simulation in process mining, interaction with the user and their knowledge is an undeniable requirement for designing and running the simulation models. Motivated by the above innovative paradigm (edge computing, EIS, process mining), design methodology (XaaS, SDX), and toolchain (WISE-PaaS, CPN Tools, ProM), the fine-grained EIS orchestration with process mining is proposed. The purpose of this study is to establish a design methodology and explore executable design specifications to optimize EIS. The key contributions of this article can be summarized as follows:  
* The methodology (mixing the modeling and verifying for structure, behavior, and data of EIS) provides a constructive way to realize the service's flexible scalability and process automation, which opens up space for optimizing fine-grained service orchestration through DSE (design space exploration). * Behavior logic, as well as architecture, are extracted from the event logs available in EIS application scenarios by the operational process discovery of process mining, to improve the service orchestration. The cross of CPN Tools and ProM provides deeper insight into the knowledge of EIS orchestration. This paper is organized as follows. After introducing the research significance, the objectives and contributions in Sections 1 and 2 explore edge intelligence, process mining technology, and its relationship with the Petri net model and our research route. The prototype of intelligent cloud edge in CPN is put forward in Section 3, including color set attributes and logic assignment with attribute predicate. Section 4 elaborates on the simulation verification of EIS with CPN Tools, and its service instance runs on WISE-PaaS. Section 5 discusses the process behavior and event data by process mining as proof of concept. Section 6 concludes the paper. Finally, future research problems are discussed.