UnespDataLens-RM：一种面向分析性数据工程的参考模型，涵盖治理、质量、溯源与可复现性

中文摘要 AI 辅助

分析流程和基于证据的决策对数据的依赖日益增强，这凸显了数据工程在构建能够从异构来源集成、转换、验证和交付数据的管道中的重要性。然而，分析资产的可靠性不仅取决于数据处理能力，还取决于其整个生命周期中的治理、质量保证、溯源、可追踪性、版本控制和可复现性机制。这些职责通常由不同的模型、框架和操作实践来承担，导致分析数据生命周期中的方法论碎片化。为解决这一差距，本文提出了UnespDataLens-RM，一种与技术无关的参考模型，它将技术操作流程和横切能力整合到一个统一的分析性数据工程结构中。该模型旨在通过从设计阶段就纳入治理、质量、溯源、可追踪性和可复现性，来支持分析管道的规范、组织和演进。UnespDataLens-RM遵循设计科学研究方法开发，包含八个技术操作模块、八个横切模块、补充维度以及一套形式化的工件、指标和验证标准。由此产生的规范为分析管道的未来实例化和实证评估提供了一个概念性和方法论框架，这些管道旨在更具可治理性、文档化、可追踪性、可审计性和可复现性。

英文摘要

The growing reliance on data in analytical processes and evidence-based decision-making has reinforced the importance of Data Engineering in building pipelines capable of integrating, transforming, validating, and delivering data from heterogeneous sources. However, the reliability of analytical assets depends not only on data processing capabilities but also on mechanisms for governance, quality assurance, provenance, traceability, versioning, and reproducibility throughout their lifecycle. These responsibilities are commonly addressed by different models, frameworks, and operational practices, resulting in methodological fragmentation across the analytical data lifecycle. To address this gap, this article proposes UnespDataLens-RM, a technology-independent reference model that integrates technical-operational processes and cross-cutting capabilities within a unified structure for Analytical Data Engineering. The model aims to support the specification, organization, and evolution of analytical pipelines by incorporating governance, quality, provenance, traceability, and reproducibility from the design stage. Developed following the Design Science Research approach, UnespDataLens-RM comprises eight technical-operational modules, eight cross-cutting modules, complementary dimensions, and a formalized set of artifacts, metrics, and validation criteria. The resulting specification offers a conceptual and methodological framework for future instantiations and empirical evaluations of analytical pipelines designed to be more governable, documented, traceable, auditable, and reproducible.

发表机构

• São Paulo State University (UNESP)（圣保罗州立大学）

机构由 AI 辅助整理，请以论文原文为准。