发表机构
Denodo Corporation; Harrisburg University（Denodo公司; 哈里斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出企业表示简化（ERS）与企业表示复杂性（ERC）模型，通过四个维度量化表示复杂性，区分架构简化与检索优化，并论证降低任务级ERC可提升AI推理准确性，同时提供经济模型评估成本。
AI 中文摘要
企业信息通过由应用程序、项目、技术、组织边界和局部需求塑造的工件来表示。这些结构随时间累积，产生表示复杂性，企业必须维护这种复杂性，信息消费者和AI系统也必须对其进行解释。本文引入企业表示简化（ERS），将其定义为在限定范围内保留所需信息的同时减少不必要的表示复杂性，并引入企业表示复杂性（ERC），这是一种表示中立的模型，用于比较不同表示状态之间的复杂性。ERC通过四个维度表征表示范围：表示对象、交互、行为和支撑来源。对象、交互和行为构成依赖类别，而支撑来源则表征表示暴露程度。ERC在表示层和任务层定义，支持比较，并区分架构简化与检索优化。本文阐述了ERS的两个后果。第一，表示结构产生维护、治理、依赖、变更、增强和运营的生命周期义务。一个经济模型区分了经常性全局表示成本、经常性任务级成本和一次性转换成本，从而能够在定义的时间范围内进行评估。第二，任务级ERC的降低减少了AI系统必须识别、关联和解释的表示范围。文本到SQL研究提供了证据表明，降低模式和推理复杂性可以提高推理准确性。ERC不是通用的复杂性、性能或成本度量。它提供了可测量的架构变量，用于比较表示替代方案、转换效果、经济结果和AI推理性能。
英文摘要
Enterprise information is represented through artifacts shaped by applications, projects, technologies, organizational boundaries, and local requirements. These structures accumulate over time, creating representational complexity that must be maintained by the enterprise and interpreted by information consumers and AI systems. This paper introduces Enterprise Representation Simplification (ERS) as reducing unnecessary representational complexity while preserving required information within a defined scope, and Enterprise Representation Complexity (ERC), a representation-neutral model for comparing complexity across representation states. ERC characterizes representational extent through four dimensions: Representation Objects, Interactions, Behaviors, and Supporting Sources. Objects, Interactions, and Behaviors form dependent categories, while Supporting Sources characterize representation exposure. ERC is defined at representation and task levels, enabling comparison and distinguishing architectural simplification from retrieval optimization. The paper develops two consequences of ERS. First, representational structures create lifecycle obligations for maintenance, governance, dependencies, change, enhancement, and operation. An economic model distinguishes recurring global representation cost, recurring task-level cost, and one-time transformation cost, enabling evaluation over a defined time horizon. Second, reductions in task-level ERC reduce the representational extent an AI system must identify, relate, and interpret. Text-to-SQL research provides evidence that reduced schema and reasoning complexity can improve reasoning accuracy. ERC is not a universal complexity, performance, or cost metric. It provides measurable architectural variables for comparing representational alternatives, transformation effects, economic outcomes, and AI reasoning performance.
