Hypothetical answers to continuous queries over data streams
Luís Cruz-Filipe, Graça Gaspar, Isabel Nunes
TL;DR
This work addresses the challenge of answering continuous queries on unbounded data streams when input is incomplete or delayed. It introduces hypothetical answers that combine current data with plausible future facts, formalizing this with a declarative semantics in Temporal Datalog and an SLD-resolution-based operational semantics. The authors present a two-stage online algorithm that first pre-processes the program to generate schematic, premise-bearing answers and then incrementally updates them as new stream data arrives, proving soundness and completeness. They extend the framework to Temporal Datalog with negation using a safety- and stratification-based approach, and discuss related work and practical considerations such as memory management and forgetting. Overall, the approach promises timely, informative guidance in prognosis-style applications while providing a principled basis for incremental stream reasoning with potentially unbounded input.
Abstract
Continuous queries over data streams may suffer from blocking operations and/or unbound wait, which may delay answers until some relevant input arrives through the data stream. These delays may turn answers, when they arrive, obsolete to users who sometimes have to make decisions with no help whatsoever. Therefore, it can be useful to provide hypothetical answers - "given the current information, it is possible that X will become true at time t" - instead of no information at all. In this paper we present a semantics for queries and corresponding answers that covers such hypothetical answers, together with an online algorithm for updating the set of facts that are consistent with the currently available information.