Data Spatial Programming

We introduce a novel programming model, Data Spatial Programming, which extends the semantics of Object-Oriented Programming (OOP) by introducing new class-like constructs called archetypes. These archetypes encapsulate the topological relationships between data entities and the execution flow in a structured manner, enabling more expressive and semantically rich computations over interconnected data structures or finite states. By formalizing the relationships between data elements in this topological space, our approach allows for more intuitive modeling of complex systems where a topology of connections is formed for the underlying computational model. This paradigm addresses limitations in traditional OOP when representing a wide range of problems in computer science such as agent-based systems, social networks, processing on relational data, neural networks, distributed systems, finite state machines, and other spatially-oriented computational problems.