https://hal.inria.fr/hal-03127717Console, MarcoMarcoConsoleUniversity of EdinburghHofer, MatthiasMatthiasHoferUniversity of EdinburghLibkin, LeonidLeonidLibkinUniversity of EdinburghVALDA - Value from Data - DI-ENS - Département d'informatique - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - Inria de Paris - Inria - Institut National de Recherche en Informatique et en AutomatiqueQueries with Arithmetic on Incomplete DatabasesHAL CCSD2020Incomplete informationNumerical dataMissing dataQuery an-sweringMeasure of certaintyFirst-order queriesApproximatequery answersAsymptotic behavior[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Senellart, Pierre2021-02-01 16:19:512023-03-24 14:53:202021-02-02 08:30:32enConference papershttps://hal.inria.fr/hal-03127717/document10.1145/3375395.3387666application/pdf1The standard notion of query answering over incomplete database is that of certain answers, guaranteeing correctness regardless of how incomplete data is interpreted. In majority of real-life databases,relations have numerical columns and queries use arithmetic and comparisons. Even though the notion of certain answers still applies,we explain that it becomes much more problematic in situations when missing data occurs in numerical columns. We propose a new general framework that allows us to assign a measure of certainty to query answers. We test it in the agnostic scenario where we do not have prior information about values of numerical attributes, similarly to the predominant approach in handling incomplete data which assumes that each null can be interpreted as an arbitrary value of the domain. The key technical challenge is the lack of a uniform distribution over the entire domain of numerical attributes, such as real numbers. We overcome this by associating the measure of certainty with the asymptotic behaviorof volumes of some subsets of the Euclidean space. We show that this measure is well-defined, and describe approaches to computing and approximating it. While it can be computationally hard, or result in an irrational number, even for simple constraints, we produce polynomial-time randomized approximation schemes with multiplicative guarantees for conjunctive queries, and with additive guarantees for arbitrary first-order queries. We also describe a set of experimental results to confirm the feasibility of this approach.