# Merging Spatio-Temporal Objects and Business Processes: Land Reform Process Case Study
## 6 Conclusions  
We proposed in this article a robust integration of MADS and BPMN as popular formalizations for spatiotemporal data conceptualization and business process management. Closer coupling of MADS with BPMN empowers designers to specify and manipulate spatiotemporal data at different levels. Our approach initially introduces the conceptual integration of MADS and BPMN, followed by mapping the entire model into a PostgreSQL/PostGIS database. This framework automates the link between data and business processes using OCL and Petri nets. OCL aligns business activities with relational model relations, while Petri nets ensure precise execution order following BPMN. We presented a syntactic case study, primarily designed as a proof of concept. It focused on studying the business process involved in tracking land reform for the specific commune of 'Malakoff', situated within the larger city of Nantes, France. It entailed actions such as parcel division, category modifications, and more. This case study offers a concise yet informative demonstration of the technical feasibility of the integration we have developed between MADS and BPMN. In summary, our modeling approach combines MADS and BPMN and presents numerous benefits:  
1. Empowering designers to convey data modeling concepts effectively through MADS. 2. Providing a shared conceptual framework that aids stakeholders in understanding and utilizing the data model for specific processes. 3. Enabling business model designers to build upon the primary MADS model for process development, ensuring consistency, and establishing a semi-automatic link between MADS and BPMN. 4. Establishing a common reference point through MADS facilitates interconnectivity among events from various business processes related to the same spatial object, paving the way for AI-driven predictions. 5. Maintaining compatibility with SQL for querying geographical data through automatic transformations and specialized languages. Consequentially, our approach holds great promise in the realm of urban data and city management. For instance, a municipality can employ a shared MADS model to encapsulate the spatiotemporal characteristics of a district. Subsequently, various departments, such as those focused on road safety and innovation, can build their unique business models using this shared foundational structure. This approach proves particularly valuable in activities like analyzing building sales, aiding in the comprehension of location preferences, and avoiding pitfalls. Historical data can be recorded and explained through pre-defined business models, rendering it easily accessible for requests and thorough analysis. Conversely, we have recognized the following limitations in our approach:  
1. Dependency on well-defined conceptual models: This approach is most valuable in structured scenarios, such as cadastral or land management, where geographical objects and processes are well-defined. It may be less effective in less structured environments, like volunteer GIS or crowd-sourced databases. 2. Interface development is required for full implementation, including a user-oriented graphical interface to facilitate the design and management of BPMN drafts auto-generated from MADS and ensure widespread adoption and utilization of our approach. Future directions involve expanding our efforts beyond the limited scope of testing our model with synthetic data. Our primary focus will be on real-case applications to thoroughly evaluate its performance and scalability. Additionally, we will prioritize the development of a user-oriented graphical interface and the incorporation of OCL for event annotations to enhance clarity. This integrated modeling approach unlocks possibilities for advanced analysis, AI analytics, classifiers, and data mining. It enables the discovery of associations and correlations within log files generated by BPMN models. These enhancements provide fresh insights and data-driven decision-making for urban and environmental applications within geographical contexts.