# Merging Spatio-Temporal Objects and Business Processes: Land Reform Process Case Study
## 5 Discussion  
The combination applied between MADS and BPMN gives the user full control of the conceptual model and the ability to clearly define when and how a process will impact the objects represented. The modeling of the process provides qualitative information that describes spatio-temporal changes and fulfills the need for an accurate representation of a real-world evolution. The representation of processes is distributed at the most appropriate level between MADS and BPMN: in MADS, the generic form is kept, while with BPMN, the specificity of each process is represented for management purposes. Consequently, our modeling approach offers the following contributions:  
1. Holistic framework for MADS and BPMN Integration: it introduced a novel holistic framework that leverages the synergies between MADS and BPMN. While conceptual modeling approaches, like MADS, are invaluable for clearly modeling complex geographical areas of study, they may fall short in depicting the intricacies of data management processes within an organization, especially when data management tasks predominate. 2. Shared conceptual model for geographical data: it established an understandable shared conceptual model for geographical data and seamlessly made it compatible with various business models. An initial draft of the business model is automatically generated. Our approach preserves the separation of data, business processes, and applications. This separation is a novel contribution to the domain of geographical data and is crucial for maintaining flexibility and adaptability as requirements evolve. It facilitates data reuse, promotes extensibility, and establishes a clear distinction between data and process modeling. 3. Integrated view: it provides a technical integration of conceptual and business modeling abstractions into a logical model. It provides a comprehensive view of geographical data within an organization, including its associated data management tasks. 4. Adhering to standard SQL: The logical implementation of the conceptual and business models into PostgreSQL, adhering to standard SQL practices, ensures the wide applicability of our proposed framework. This ensures compatibility with static data that already exists within large organizations. Figure 5: Petri net resulting from the transition of the BPMN. Furthermore, our approach paves the way for additional advantages that we plan to elaborate on in the future perspectives of this work. The following are potential supplementary contributions we intend to develop:  
1. Streamlined data manipulation: Geographical objects exhibit continuous evolution in both spatial and temporal dimensions. Any alteration made to a geographical object may necessitate complex updates to neighboring spatial objects. Our framework can be used to simplify this work by modeling all potential modifications as an integral part of the business process, leveraging BPMN, and automating the execution through Petri Net. Initiating each process and defining specific constraints using the OCL language is all that is required, allowing Petri Net to dictate the execution of tasks and events seamlessly. 2. Deep analysis of modifications: While traditional approaches focus on answering \"what, when, and where\" questions about geographical data as reflected by conceptual models, our methodology goes deeper, elucidating \"how and why\" modifications occur (what the trigger was, the event, and the consequences), enriching the context for analyzing and decision-making, generally part of business modeling approaches. Additionally, by embracing our approach, we aim to unlock the potential for more advanced AI classifiers, enabling them to forecast forthcoming situations in geographical data and be able to answer \"foresight\" questions. This capability holds vast value across various industries and applications. For instance, these new types of queries are supported: * What are the consequences of splitting parcel A?