Hojabr: Unified Language for AI and Data Analytics
Published:Dec 30, 2025 00:55
•1 min read
•ArXiv
Analysis
This paper addresses the fragmentation in modern data analytics pipelines by proposing Hojabr, a unified intermediate language. The core problem is the lack of interoperability and repeated optimization efforts across different paradigms (relational queries, graph processing, tensor computation). Hojabr aims to solve this by integrating these paradigms into a single algebraic framework, enabling systematic optimization and reuse of techniques across various systems. The paper's significance lies in its potential to improve efficiency and interoperability in complex data processing tasks.
Key Takeaways
- •Proposes Hojabr as a unified intermediate language for AI and data analytics.
- •Integrates relational algebra, tensor algebra, and constraint-based reasoning.
- •Aims to improve interoperability and reduce repeated optimization efforts.
- •Supports bidirectional translation with existing declarative languages.
Reference
“Hojabr integrates relational algebra, tensor algebra, and constraint-based reasoning within a single higher-order algebraic framework.”