Detecting the Temporal Context of Queries.

Business intelligence and reporting tools rely on a database that accurately mirrors the state of the world. Yet, even if the schema and queries are constructed in exacting detail, assumptions about the data made during extraction, transformation, and schema and query creation of the reporting database may be (accidentally) ignored by end users, or may change as the database evolves over time. As these assumptions are typically implicit (e.g., assuming that a sales record relation is append-only), it can be hard to even detect that a mistaken assumption has been made. In this paper, we argue that such errors are consequences of unintended contextual dependence, i.e., query outputs dependent on a variable characteristic of the database. We characterize contextual dependence, and explore several strategies for efficiently detecting and quantifying the effects of contextual dependence on query outputs. We present and evaluate our findings in the context of a concrete case study: Detecting temporal dependence using a database management system with versioning capabilities.