Abstract Whereas requirements engineering for transactional systems aims to discover the functionality of the system-to-be, data warehouse requirements engineering aims to discover the Information contents of the data-warehouse-to-be. Though notions of goals, Decisions, business processes, business events have been used to set the context for Information discovery, the move from these to obtain the relevant Information is largely ad hoc, unguided, and does not provide traceability of Information. We propose four elicitation techniques that are inferred from manager concerns during Decision making and that provide guidance and traceability. These form a suite such that each augments the set of already discovered Information. Consequently, the possibility of missing requirements is reduced, thereby making for more effective requirements engineering.

中文翻译：

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一种用于获取数据仓库信息需求的多因素方法

摘要 事务系统的需求工程旨在发现未来系统的功能，而数据仓库需求工程旨在发现未来数据仓库的信息内容。尽管目标、决策、业务流程、业务事件的概念已被用于设置信息发现的上下文，但从这些概念获取相关信息的过程在很大程度上是临时的、无指导的，并且不提供信息的可追溯性。我们提出了四种启发式技术，这些技术是从决策制定期间经理​​关注的问题中推断出来的，并提供指导和可追溯性。这些形成了一个套件，每个套件都增加了已经发现的信息集。因此，减少了丢失需求的可能性，从而使需求工程更加有效。