Taming Primary Key Violations to Query Large Inconsistent Data
This addresses a classical hard problem in database research for handling inconsistent data, offering a scalable solution that is incremental in improving applicability to large datasets.
The paper tackles the problem of consistent query answering over databases violating primary key constraints by introducing a decomposition and pruning strategy that reduces it to smaller, independent subproblems in polynomial time, achieving effectiveness and efficiency on large datasets as proven by experiments on database benchmarks.
Consistent query answering over a database that violates primary key constraints is a classical hard problem in database research that has been traditionally dealt with logic programming. However, the applicability of existing logic-based solutions is restricted to data sets of moderate size. This paper presents a novel decomposition and pruning strategy that reduces, in polynomial time, the problem of computing the consistent answer to a conjunctive query over a database subject to primary key constraints to a collection of smaller problems of the same sort that can be solved independently. The new strategy is naturally modeled and implemented using Answer Set Programming (ASP). An experiment run on benchmarks from the database world prove the effectiveness and efficiency of our ASP-based approach also on large data sets. To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of ICLP 2015.