# Foundations of Declarative Data Analysis Using Limit Datalog Programs

arXiv: https://arxiv.org/abs/1705.06927 · HTML: https://mlsanity.com/paper/1705.06927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2017.

## Built on by (most cited first, 2 of 2)

- [Are Language Models Efficient Reasoners? A Perspective from Logic Programming](https://mlsanity.com/paper/2510.25626/) (2025)
- [Complexity of Arithmetic in Warded Datalog+-](https://mlsanity.com/paper/2202.05086/) (2022)

## Abstract (arXiv)

Motivated by applications in declarative data analysis, we study mathitDatalog_mathbbZ---an extension of positive Datalog with arithmetic functions over integers. This language is known to be undecidable, so we propose two fragments. In mathitlimit~mathitDatalog_mathbbZ 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 mathitstability requirement causes the complexity to drop to ExpTime and PTime, respectively. Finally, we show that stable mathitDatalog_mathbbZ can express many useful data analysis tasks, and so our results provide a sound foundation for the development of advanced information systems.