We study the novel problem of efficiently computing the update distance for a pair of relational databases. In analogy to the edit distance of strings, we define the update distance of two databases as the minimal number of set-oriented insert, delete and modification operations necessary to transform one database into the other. We show how this distance can be computed by traversing a search space of database instances connected by update operations. This insight leads to a family of algorithms that compute the update distance or approximations of it. In our experiments we observed that a simple heuristic performs surprisingly well in most considered cases. Our motivation for studying distance measures for databases stems from the field of scientific databases. There, replicas of a single database are often maintained at different sites, which typically leads to (accidental or planned) divergence of their content. To re-create a consistent view, these differences must be resolved. Such an effort requires an understanding of the process that produced them. We found that minimal update sequences of setoriented update operations are a proper and concise representation of systematic errors, thus giving valuable clues to domain experts responsible for conflict resolution.
Müller et al. have proposed the notion of update distance, i.e., "the minimal number of set-oriented insert, delete and modification operations necessary to transform one database into the other." REF .
Describing differences between databases
CIKM '06
Computer Science