Semantic data lake architecture with automated schema evolution for intelligent transportation data management

This research presents a semantic data lake architecture with automated schema evolution capabilities specifically designed for intelligent transportation data management in modern toll and traffic systems. The proposed Intelligent Transportation Data Lake (ITDL) addresses the complex challenges of managing diverse, rapidly changing datasets from connected vehicles, infrastructure sensors, payment systems, and mobile applications in transportation environments. Our methodology combines knowledge graphs with advanced metadata management to create self-organizing data repositories that automatically understand, categorize, and optimize transportation data storage and access patterns. The system employs natural language processing and ontology learning techniques to extract semantic meaning from ingested transportation data, creating rich metadata descriptions that enable intelligent data discovery and lineage tracking across toll operations, traffic management, and planning applications. We introduce a novel automated schema evolution mechanism that detects changes in streaming transportation data and automatically updates data catalogs and analytical pipelines without service interruption. The semantic component models relationships between vehicles, infrastructure, payments, and traffic patterns to provide intelligent insights and predictive capabilities. Our implementation includes advanced data quality monitoring specific to transportation systems and automatic anomaly detection for maintaining data integrity across diverse data sources. Experimental validation using real-world transportation datasets demonstrates superior performance in data discovery with 76% improvement in relevant dataset identification and 68% reduction in data preparation time for transportation analytics applications.