Section: New Results

Advanced Algorithms for Data Querying and Transformation

We focused on explaining why some data, so-called missing answers, are not part of the result of a query, even though a developer expects them to be there.

The query-based explanations we return during query analysis serve as the starting point for our query rewriting process. Indeed, knowing the condition combinations pruning data relevant to the missing answers significantly narrows the search space for eligible query rewritings as we can first focus on finding solutions that only affect these query conditions. To further prune the search space, our current solution applies a cost model for rewritings based on several criteria, including edit distance to the original query, or the number of side-effects (tuples additionally appearing in the result of the rewritten query that are not our original missing answers). To select the best solutions w.r.t. the different dimensions of our cost model, we compute and return the skyline over these. We have demonstrated a preliminary version of the proposed algorithn in [8] . This work is reported [7] and in the PhD thesis of K. Tzompanaki [1] .