ILP Modulo Data
Panagiotis Manolios, Vasilis Papavasileiou and Mirek Riedewald.
The vast quantity of data generated and captured every day has led to
a pressing need for tools and processes to organize, analyze and
interrelate this data. Automated reasoning and optimization tools with
inherent support for data could enable advancements in a variety of
contexts, from data-backed decision making to data-intensive
scientific research. To this end, we introduce a decidable logic aimed
at database analysis. Our logic extends quantifier-free Linear Integer
Arithmetic with operators from Relational Algebra, like selection and
cross product. We provide a scalable decision procedure that is based
on the BC(T ) architecture for ILP Modulo Theories. Our decision
procedure makes use of database techniques. We also experimentally
evaluate our approach, and discuss potential applications.