###### Abstract
In a similar way, some other formalisms have been employed to sketch the control flow, such as BPMN (business process modeling notation) [20; 21] and UML activity diagrams [22]. However, these process-aware formalisms only consider the process data and corresponding operations, ignoring the interaction of these data with the underlying database. In fact, process data may eventually be stored in the persistent data repositories by database select/insert/update/delete operations [23]. Inappropriate database operations can easily lead to inconsistent data regardless of whether a data item is stored in one or several locations [24; 25; 26]. As a result, there have been many related studies in which inconsistent data have been analyzed directly in the database. For instance, [PERSON] et al. [27] detected inconsistent data values caused by database access operations of business data objects, and this method was also applied to identify similar errors in Internet of Things systems [28]. [PERSON] et al. [29] integrated control flow, process data, and a database to discuss inconsistent data based on sequences of database access operations. [PERSON] et al. [30] developed a model based on the concept of monetization to detect inconsistent data in databases by data fusion techniques. These data-aware methods focus on the access operations of the persistent data without considering process/activity execution logic, which leads to the data-flow errors always being detected through the idea of testing. To sum up, in workflow systems, it is inadequate to discover inconsistent data by only focusing on the process data operations or the persistent data operations. Nevertheless, the fact is that there are only a few studies that have integrated such two aspects. And the essential reason is that it is a challenge to construct a formal model to facilitate the analysis of the problem from such two aspects. In addition, when the values of process data are stored in a database, an efficient way to handle unbounded data [31] is needed. In fact, there are already several formal models capturing process and persistent data and their operations, such as the RAW-SYS framework [32] and DB-nets [33], but the discovery of inconsistent data is still one-sided. Moreover, it does not specify how to handle unbounded data. Yet the WFT-net (workflow net with tables) proposed in our previous work [34] focuses on such problems: firstly, the WFT-net utilizes the workflow net and relational tables to display the control flow and the persistent data, which are interconnected by the process data. Moreover, since the process and persistent data operations are attached to the transitions, many analysis methods of Petri nets are also applicable to WFT-nets, such as graph traversal. Lastly, as a conceptual model, WFT-nets can refine infinite data into finite data to address problems by means of the corresponding data refining strategy. Therefore, this paper is dedicated to detecting data inconsistency issues. To achieve this, the WFT-net model is utilized, which thoroughly considers interactions between process data and persistent data during process execution. An extended reachability graph for WFT-nets is constructed, incorporating an improved data refining strategy where all data operations of each firing transition are added to the corresponding arc. Addressing the limitation of the previous data refining strategy in [34], which is that it fails to capture data inconsistency errors caused by overwritten data due to the absence of fresh values, this paper proposes a novel data refining strategy that explicitly accounts for overwriting scenarios. Data inconsistencies are subsequently detected by traversing the extended reachability graph, with the correctness of the proposed approach demonstrated through a theoretical analysis. Moreover, a set of comparative experiments with WFD-nets were conducted to demonstrate the effectiveness of the proposed detection method. The rest of this paper is organized as follows. Section 2 recalls some basic concepts of WFT-nets.