## 1 Introduction
The temporal approach requires that each token carries one time stamp, which is a timing value used to determine the firing time of the transitions, which are enabled by the places this token is located. The enabled transitions can fire and produce tokens with a delay, which corresponds to the time it takes for an event to occur. To determine the firing time of a transition, we examine all its input places and from each place, we find the token with a minimal time stamp (that is, the next token to leave this place). This corresponds to the earlier time the conditions that enable an event became true. Then, from these time stamps, we pick the one with the larger value. This indicates the latest time by which _all_ the necessary conditions became true, to enable a transition. Generally, when we consider time, there is always a global clock for keeping the time. Time is usually advances in time units, but sometimes, it is useful to use real time metrics (hours, minutes, seconds, etc.). 3. The typical PN formalism does not impose further conditions for firing a transition. In a PN, an enabled transition will fire regardless of any other conditions that may hold. However, there are many cases where an event requires extra conditions other than the ones determined by the existence of tokens in places. For this reason, the CPN formalism introduces the guardian expressions, which can be used when we need to assign such conditions regarding the time of firings. In addition, PN and CPN have some common disadvantages:  
1. In their basic form, they do not have an inherited capability to transform actual system data and events into PN or CPN transitions and places. Researchers use PN and CPN to create models, which are then used for simulations, in order to study the behavior of various systems. In other words, researchers first produce a system model and then generate simulated data to test its accuracy. In this way, the system can be modeled in some level of accuracy; however, it may miss certain important conditions and details which can only be retrieved by the actual events that have incurred within the system. The colors, the time stamps, and guardian expressions introduced in CPNs are not capable of introducing real data into the model. Therefore, a scheme for transforming events into a sequence of transitions is required. Such a scheme should first define certain modeling rules for sequencing transitions. Then, it should read the actual events and try to efficiently transform them into CPN transitions by using the defined rules. 2. Both PN and CPN formalisms are not capable of self-organizing themselves into autonomous smaller subsystems which are easy to study and check for errors. Dividing the overall model into smaller autonomous systems (which are referred to as _nest_ in the rest of this work) is of great importance. The nests release a researcher from the burden of designing an entire model in one time. Based on the actual data, we can focus on a separate nest and then simply work to consider the connections between pairs of nests. The idea of using real data to generate models can be very efficient in producing such nests because, to a wide extent, a system's real events incur within a subsystem and its effects are then propagated to the entire system. Our work tries to address the two disadvantages that we have described in this introductory section. The specific contributions of this work are listed in the next section. The remaining of this paper is organized as follows: Section 3 presents briefly the use of PN and CPN modeling in various research areas. Section 4 presents our modeling approach. In Section 5, we present a case study: specifically, we show how we have used real data from a courier company to model the company's activities. In Section 6, we show some experimental results from the simulation of our model. These results have shown that the model has quite accurately represented the company's working cycle. Section 7 concludes the paper and offers aspects for improvements of our methodology.