Reasoning on Data Words over Numeric Domains

Abstract

We introduce parametric semilinear data logic (pSDL) for reasoning about data words with numeric data. The logic allows parameters, and Presburger guards on the data and on the Parikh image of equivalence classes (i.e. data counting), allowing us to capture data languages like: (1) each data value occurs at most once in the word and is an even number, (2) the subset of the positions containing data values divisible by 4 has the same number of a’s and b’s, (3) the data value with the highest frequency in the word is divisible by 3, and (4) each data value occurs at most once, and the set of data values forms an interval. We provide decidability and complexity results for the problem of membership and satisfiability checking over these models. In contrast to two-variable logic of data words and data automata (which also permit a form of data counting but no arithmetics over numeric domains and have incomparable inexpressivity), pSDL has elementary complexity of satisfiability checking. We show interesting potential applications of our models in databases and verification.