Hojabr: Towards a Theory of Everything for AI and Data Analytics

Modern data analytics pipelines increasingly combine relational queries, graph processing, and tensor computation within a single application, but existing systems remain fragmented across paradigms, execution models, and research communities. This fragmentation results in repeated optimization efforts, limited interoperability, and strict separation between logical abstractions and physical execution strategies. We propose Hojabr as a unified declarative intermediate language to address this problem. Hojabr integrates relational algebra, tensor algebra, and constraint-based reasoning within a single higher-order algebraic framework, in which joins, aggregations, tensor contractions, and recursive computations are expressed uniformly. Physical choices, such as join algorithms, execution models, and sparse versus dense tensor representations, are handled as constraint-specialization decisions rather than as separate formalisms. Hojabr supports bidirectional translation with existing declarative languages, enabling programs to be both lowered into Hojabr for analysis and optimization and lifted back into their original declarative form. By making semantic, structural, and algebraic properties explicit, and by supporting extensibility across the compilation stack, Hojabr enables systematic reasoning and reuse of optimization techniques across database systems, machine learning frameworks, and compiler infrastructures.