Multimodal Semantic Fusion of Heterogeneous Data Silos

Abstract

Maintaining consistency in complex systems is a continuous challenge that requires active coordination. Data management systems often face the issue of segregated data silos due to various organizational and technical factors. Integrating them when needed can present challenges due to heterogeneity and multimodality. Recent advances in AI models with enhanced multimodal inference and semantic reasoning capabilities offer an opportunity to resolve interoperability issues at both the schema and data levels. In this paper, we discuss ways to leverage such models to mitigate a variety of heterogeneous timing and data barriers across disparate silos. We also examine their fusion and propose ways to formally define it as a foundational means for self-evolving unified meta-space in light of recent model enablements and active inference. Assessing the degree of fusion is necessary to understand and determine how silos, as subsystems, collectively interact, and therefore to control their integration while preserving data source independence. Adherence to a principled design that handles complexity can guide crucial decisions and enhance controllability over the reasoning process. We formalize a foundation for separating prior knowledge from observed data, showing how to leverage inference in both cases with examples and real data. The resulting approach enables advanced inference while providing statistical evidence from observed data by applying reasoning at multiple steps. To conclude, we discuss the implications of this approach for complex systems more generally.