Synthesizing Nested Relational Queries from Implicit Specifications

Derived datasets can be defined implicitly or explicitly. An implicit definition (of dataset $O$ in terms of datasets $\vec{I}$) is a logical specification involving the source data $\vec{I}$ and the interface data $O$. It is a valid definition of $O$ in terms of $\vec{I}$, if any two models of the specification agreeing on $\vec{I}$ agree on $O$. In contrast, an explicit definition is a query that produces $O$ from $\vec{I}$. Variants of Beth's theorem state that one can convert implicit definitions to explicit ones. Further, this conversion can be done effectively given a proof witnessing implicit definability in a suitable proof system. We prove the analogous effective implicit-to-explicit result for nested relations: implicit definitions, given in the natural logic for nested relations, can be effectively converted to explicit definitions in the nested relational calculus NRC. As a consequence, we can effectively extract rewritings of NRC queries in terms of NRC views, given a proof witnessing that the query is determined by the views

Deductive Systems in Traditional and Modern Logic

The book provides a contemporary view on different aspects of the deductive systems in various types of logics including term logics, propositional logics, logics of refutation, non-Fregean logics, higher order logics and arithmetic

Formalization of Real Analysis: A Survey of Proof Assistants and Libraries

International audienceIn the recent years, numerous proof systems have improved enough to be used for formally verifying non-trivial mathematical results. They, however, have different purposes and it is not always easy to choose which one is adapted to undertake a formalization effort. In this survey, we focus on properties related to real analysis: real numbers, arithmetic operators, limits, differentiability, integrability, and so on. We have chosen to look into the formalizations provided in standard by the following systems: Coq, HOL4, HOL Light, Isabelle/HOL, Mizar, ProofPower-HOL, and PVS. We have also accounted for large developments that play a similar role or extend standard libraries: ACL2(r) for ACL2, C-CoRN/MathClasses for Coq, and the NASA PVS library. This survey presents how real numbers have been defined in these various provers and how the notions of real analysis described above have been formalized. We also look at the methods of automation these systems provide for real analysis

Case Studies in Proof Checking

The aim of computer proof checking is not to find proofs, but to verify them. This is different from automated deduction, which is the use of computers to find proofs that humans have not devised first. Currently, checking a proof by computer is done by taking a known mathematical proof and entering it into the special language recognized by a proof verifier program, and then running the verifier to hopefully obtain no errors. Of course, if the proof checker approves the proof, there are considerations of whether or not the proof checker is correct, and this has been complicated by the fact that so many systems have sprung into being. The two main challenges in using a proof checker today are the time needed to learn the syntax and general usage of the system and the time needed to formalize a proof in the system even when the user is already proficient with it. As mathematicians are not yet using proof checkers regularly, we wanted to evaluate the validity of this reluctance by analyzing these main obstacles. Judging by Dr. Wiedijk’s Formalizing 100 Theorems list, which gives an overview of the headway various proof systems have made in mathematics, Coq and Mizar are two of the most successful systems in use today (Wiedijk, 2007). I simultaneously formalized two fairly involved theorems in these two systems while I was at approximately the same level of familiarity with each. I kept track of my experiences with learning the systems and analyzed their comparative strengths and weaknesses. The analysis and summary of experiences should also give a general idea of the current state of computer-aided proof checking

Tractable Ontology-Mediated Query Answering with Datatypes

Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard, even for lightweight languages. As a consequence, the use of datatypes in ontologies, e.g. in OWL 2 QL, has been severely restricted. We propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems (CSPs) over the datatypes. Given that query answering in this setting is undecidable in general already for very simple datatypes, we introduce, borrowing from the database literature, a property of OMQs called the Bounded Match Depth Property (BMDP). We apply the link to CSPs– using results and techniques in universal algebra and model theory–to prove PTIME/co-NP dichotomies for OMQs with the BDMP over Horn-ALCHI extended with (1) all finite datatypes, (2) rational numbers with linear order and (3) certain families of datatypes over the integers with the successor relation