Systems-Level Framework for AI-Driven Transformation of Historical Data at Scale: Design Patterns for Modern Archival Ecosystems

Abstract

This paper aims to develop a systems-level framework for the AI-driven transformation of historical archival data at scale, with emphasis on architectural layers, reusable design patterns, and evaluation requirements for modern archival ecosystems. The study adopts a design science research approach and synthesizes literature, technical standards, and operational practices from archival informatics, data engineering, document image analysis, OCR/HTR, natural language processing, knowledge graph construction, metadata interoperability, and workflow orchestration. The proposed framework consists of five interrelated layers: Ingestion and Preservation, Recognition and Extraction, Semantic Enrichment, Knowledge Representation, and Orchestration and Delivery. It also identifies five reusable design patterns: Ingestion Gateway, Adaptive Recognition, Semantic Enrichment Chain, Graph Federation, and Orchestrated Provenance. These layers and patterns guide the integration of ETL pipelines, OCR/HTR models, NLP tools, CIDOC-CRM and PROV-O-based knowledge graphs, and cloud- or hybrid-orchestration workflows. The study provides a prescriptive reference architecture and evaluation protocol for scalable archival data processing. It concludes that a systems-level framework can bridge the gap between component-level AI research and deployment-oriented archival engineering, while providing a foundation for future empirical validation.