Foundations of Declarative Data Analysis Using Limit Datalog Programs

Foundations of Declarative Data Analysis Using Limit Datalog Programs

Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1123-1130. https://doi.org/10.24963/ijcai.2017/156

Motivated by applications in declarative data analysis, we study DatalogZ---an extension of positive Datalog with arithmetic functions over integers. This language is known to be undecidable, so we propose two fragments. In limit DatalogZ predicates are axiomatised to keep minimal/maximal numeric values, allowing us to show that fact entailment is coNExpTime-complete in combined, and coNP-complete in data complexity. Moreover, an additional stability requirement causes the complexity to drop to ExpTime and PTime, respectively. Finally, we show that stable DatalogZ can express many useful data analysis tasks, and so our results provide a sound foundation for the development of advanced information systems.
Keywords:
Knowledge Representation, Reasoning, and Logic: Computational Complexity of Reasoning
Knowledge Representation, Reasoning, and Logic: Knowledge Representation Languages
Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation
Knowledge Representation, Reasoning, and Logic: Tractable languages and knowledge compilation