Synthetic Cognition Meets Data Deluge: Architecting Agentic AI Models for Self-Regulating Knowledge Graphs in Heterogeneous Data Warehousing

Abstract

The realities of contemporary data management and representation are evolving at an increasing rate. However, we still lack the broad foundational bridges of core data warehousing principles relating to how high-level reports are generated internally so that users can psychologically intuit where they are in the vast and complex repository of data that resides in a typical data warehouse. IT workers must constantly support users or worry about failed ad-hoc or automated operations or whose results appear without explanation. Data management may not yet exist as a science. We need a more complete transformational view of the details of the internal mappings between data from diverse sources and conceptual data model object types. Cognitive model-driven and symbolic techniques have been approached to design and develop systems to automate and rationalize these transformational processes and to support user navigation and work. These techniques are now being displaced by advanced statistical learning methods. As designed, these methods mostly do knowledge creation in the basic steps of the transformational process, but they likewise at times pave data as well. Through AI as Intentional Cognition supplemented by language, this inhibition may be bypassed. Thus, despite both their synthetic and agentic capabilities, these approaches follow a surprising and quite diverse transition. The goal of this work is to show what tasks of data management and representation these methods might be able to tackle and when and how they might interleave towards a more collaborative AI Data Management. We conclude with directions for future work.