LARS: A Logic-Based Framework for Analytic Reasoning over Streams (Extended Abstract)

摘要

Stream reasoning considers continuously deriving conclusions on streaming data. While traditional stream processing approaches focus on throughput and are often based on operational grounds, reasoning approaches aim at high expressiveness based on declarative semantics; yet according theoretical underpinning in the streaming area has been lacking. To fill this gap, we provide LARS, a Logic-based Framework for Analytic Reasoning over Streams. It provides generic window operators to limit reasoning to recent snapshots of data, and modalities to control the temporal information of data. Building on resulting formulas, a rule-based language is presented which can be seen as extension of Answer Set Programming (ASP) for streams. We study semantic properties and the computational complexity of LARS, its relation to other formalisms and mention various work that builds on it.