AI 中文总结
研究呼吁从有效性驱动转向价值驱动的数据依赖发现，定义了依赖使用价值和净值，基于此框架概述相关原则并确定研究议程，涉及价值估计、损失成本学习、预算集选择等多方面。
AI 中文摘要
传统上，数据依赖发现专注于识别数据中成立或统计上强大的依赖关系。然而，一种依赖关系可能有效但无价值：它可能与治理任务无关，鉴于现有知识是冗余的，或者发现、验证、维护和应用成本过高。我们呼吁从有效性驱动转向价值驱动的依赖发现。我们从决策理论上定义依赖使用价值为将依赖纳入治理过程中任务特定损失的预期减少，并通过进一步考虑生命周期成本来定义净值。基于此框架，我们概述了价值感知搜索、验证、依赖集选择和维护的原则，并确定了一个研究议程，涵盖完全发现前的价值估计、损失和成本学习、预算集选择、生命周期监测和基准测试。
英文摘要
Data dependency discovery has traditionally focused on identifying dependencies that hold in the data or are statistically strong. Yet a dependency may be valid without being valuable: it may be irrelevant to the governance task, redundant given existing knowledge, or too costly to discover, validate, maintain, and apply. We call for a shift from validity-driven to value-driven dependency discovery. We define dependency use value decision-theoretically as the expected reduction in task-specific loss from incorporating a dependency into the governance process, and define net value by further accounting for lifecycle costs. Building on this framework, we outline principles for value-aware search, validation, dependency-set selection, and maintenance, and identify a research agenda spanning value estimation before full discovery, loss and cost learning, budgeted set selection, lifecycle monitoring, and benchmarking.
