两种展望未来的方式：在RTLola中结合预测与未来偏移访问
Two Ways to See the Future: Combining Prediction and Future-Offset Accesses in RTLola
AI总结：
该研究为面向异步实时系统的流规范语言RTLola扩展了预测算子与离散未来偏移算子两种未来推理机制，并在运行时和内存消耗上评估了其实现。
AI中文摘要：
RTLola是一种面向异步实时系统的基于流的规范语言。尽管许多时态规范自然地涉及未来行为，但RTLola目前没有表达此类未来依赖属性的机制。本文中，我们为RTLola扩展了两种互补的未来推理机制：首先，引入预测算子，基于过去的观测值外推任意时间戳的未来流值；其次，添加离散未来偏移算子，通过延迟依赖流表达式的求值来获取精确的未来值。前者支持即时但可能不精确的预测，后者则在所需信息可用时确保精确值。我们在RTLola语义中对这两种扩展进行形式化，并在运行时和内存消耗上评估其实现。
英文摘要：
RTLola is a stream-based specification language designed for asynchronous real-time systems. While many temporal specifications naturally refer to future behavior, RTLola currently offers no mechanism to express such future-dependent properties. In this paper, we extend RTLola with two complementary mechanisms to reason about the future. First, we introduce a prediction operator that extrapolates future stream values at arbitrary timestamps based on past observations. Second, we add a discrete future offset operator, which provides access to precise future values by delaying the evaluation of the dependent stream expressions. While the former enables immediate, but possibly imprecise predictions, the latter ensures exact values once the required information becomes available. We formalize both extensions in the RTLola semantics and evaluate their implementation on runtime and memory consumption.
