Graph databases allow one to store data while emphasizing the importance of the data itself along with its internal connections.  GQL, the new standard for graph query languages, has been under development since 2019. Standardization raises many inherent questions on expressivity and complexity. The outcomes of such theoretical analysis can lead to design decisions that would affect the standard.

Handling incomplete information is a longstanding problem in data management. Classical research on the subject is focused mainly on simple relational queries (SELECT-FROM-WHERE). In practice, queries include aggregations, comparisons, arithmetics, and more. How can we adapt theory to study such queries? What approaches can be taken to define the notion of query answering under these settings?

 SQL is inarguably the query language for relational databases (in which data is stored in a tabular form). Its formal semantics is based on Kleene's three-valued logic which is used to handle incomplete information. Unfortunately, the three-valued approach often leads to unintuitive behavior and internal inconsistencies. Is there a way to avoid this? Can we harness existing implementations for that?  What lessons can be applied on graph query languages?