Temporal Data Exchange

TL;DR

This paper extends data exchange frameworks to temporal databases by developing a chase procedure that aligns concrete and abstract views, enabling effective query answering over temporal data.

Contribution

It introduces a novel chase algorithm for temporal data exchange that ensures semantic alignment between concrete and abstract data representations.

Findings

The c-chase procedure aligns concrete and abstract temporal data.

Naive evaluation on c-chase results yields certain answers.

The approach supports query answering in temporal data exchange.

Abstract

Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas. Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time. In this work, we study data exchange for temporal data. We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots. We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases. Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently. Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance. In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query. To obtain the semantic alignment, the nulls in the concrete view are annotated with temporal information. Furthermore, we show that the result of the concrete chase provides a foundation for query answering. We define naive evaluation on the result of the c-chase and show it produces certain answers.