data serialization

Existing data pipelines often suffer from weak governance, leading to delayed schema validation, inconsistent cross-language execution, and misalignment with business semantics. This work proposes treating data contracts as types, leveraging the “everything-as-code” paradigm to inject schema annotations—encompassing column types, constraints, documentation, and lineage—into input and output tables within a lakehouse architecture via multi-language SDKs. These annotations are parsed across multiple phases of the execution lifecycle, deeply integrating data contracts into the type system. The approach enables both deterministic and non-deterministic reasoning over data flows across languages and execution engines, significantly enhancing the reliability of production data pipelines and ensuring consistent interoperability across systems.

Although scientific data increasingly adhere to the FAIR principles and employ standardized identifiers, practical interoperability remains hindered by heterogeneity in identifier systems and data models. This work proposes and implements two synergistic tools—Babel and ORION—to bridge this gap. Babel constructs clusters of equivalent identifiers through mapping-based clustering and exposes them via a high-performance quantitative API, while ORION standardizes heterogeneous knowledge bases by aligning them to a community-governed common data model. Together, they systematically address the longstanding disconnect between the FAIR “Interoperable” principle and its real-world implementation. The integration of these tools has enabled the construction of a fully interoperable knowledge base, substantially enhancing cross-resource data integration and query capabilities. The resulting framework is publicly available.

This work addresses the lack of a rigorous mathematical foundation in existing distributed data fabric architectures, which struggle to simultaneously ensure consistency, data provenance, and scalability. We propose the first unified framework integrating category theory, hypergraphs, and geometric analogies from Hurwitz spaces. Data sets, metadata, transformations, and policies are uniformly modeled as hypergraphs, with data treated as objects and transformations as morphisms. Modular tensor categories and braided monoidal structures are introduced to capture relational symmetries. Within this formalism, we rigorously prove the NP-hardness of key tasks and develop a fault-tolerant operational mechanism—leveraging sparse incidence matrices, spectral methods, and symmetry-aware alignment algorithms—that guarantees consistency, completeness, and causality while adhering to the CAP and CAL theorems. This framework provides a scalable mathematical foundation for large-scale federated learning and data integration.

Existing process modeling approaches struggle to simultaneously support object identity tracking, complex data type handling, inter-object dependency representation, and object-aware synchronization. While Object-Centered Petri Nets (OPNs) partially address these challenges, they model objects as unstructured identifiers, thereby failing to capture rich data semantics. This paper introduces Data-Aware Object-Centered Petri Nets (DOPNs), the first formal extension of OPNs that natively supports structured data operations and full object-aware synchronization within a rigorous mathematical framework. We further propose an SMT-based data-aware alignment algorithm that integrates process mining with formal verification, enabling decidable alignment computation and compliance checking under complex data constraints. DOPNs strike a critical balance between expressive power and operational tractability, significantly enhancing modeling fidelity and analytical capabilities for dynamic, object-intensive business processes.

To address the error-prone and inefficient manual rewriting of data transformation logic upon JSON Schema evolution, this paper proposes a type-directed, top-down program synthesis approach for automatically generating semantics-preserving JSON Schema converters. Our method integrates type inference, semantic constraint modeling, a rewrite system, and intermediate representation (IR)-driven code generation to guarantee lossless data transformation and formal verifiability. It natively supports complex nested schemas and synthesizes correct, efficient, and human-readable Python and JavaScript conversion code. We evaluate our approach on real-world API configuration schemas and healthcare data integration scenarios, demonstrating its safety—via formal guarantees and empirical validation—its practical utility in industrial settings, and its generalizability across diverse schema evolution patterns. Experimental results confirm high accuracy, robustness to structural changes (e.g., field additions, type refinements, nested object restructuring), and scalability to large, deeply nested schemas.

This work addresses the challenge of efficient inter-process communication and serialization of algebraic data in distributed computing environments by proposing and implementing a tunable serialization framework. The framework supports customizable serialization strategies tailored to algebraic data structures and innovatively adapts the mrdi file format for data transmission in distributed settings. By integrating domain-specific serialization mechanisms with the mrdi format, the system substantially enhances communication efficiency and processing performance for algebraic data across distributed systems. This approach provides flexible and high-performance low-level support for applications that rely heavily on structured algebraic representations, offering both adaptability and scalability without compromising on throughput or latency.

This study addresses the frequent failure of large language models (LLMs) in software engineering tasks due to structurally invalid outputs—such as syntactic or formatting errors—that prevent correct parsing by downstream toolchains, even when the semantic content is accurate. The authors systematically evaluate the reliability of structured output generation across four representative tasks, categorizing errors into syntactic, structural, and semantic types. They propose a template-driven token-matching generation (TTMG) method that enforces structural consistency during autoregressive decoding. Experimental results demonstrate that TTMG nearly eliminates syntactic errors; however, structural and semantic errors remain prevalent. This work reveals, for the first time, that the fundamental bottleneck in structured output generation lies not merely in syntax but in the insufficient coordination between structural and semantic correctness, indicating that existing structural control mechanisms, while necessary, are insufficient without joint guarantees of both dimensions.

This work addresses the challenge of ensuring correctness and reliability in code that translates structured data within the Internet of Medical Things. The authors propose a novel code generation approach that integrates large language models with evolutionary algorithms and, for the first time, embeds formal verification into the LLM-driven synthesis pipeline. This integration guarantees that the generated JSON-to-FHIR transformation code strictly adheres to predefined specifications. The method significantly enhances translation accuracy and safety in healthcare data interoperability, as demonstrated in a pulse oximeter integration scenario. It enables the cost-effective and stable production of FHIR-compliant, reliable code, thereby advancing trustworthy medical device connectivity and data exchange.