Two Ways to See the Future: Combining Prediction and Future-Offset Accesses in RTLola

📅 2026-08-06
📈 Citations: 0
Influential: 0
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🤖 AI Summary
This work addresses a key limitation of the stream-based specification language RTLola: its inability to express temporal properties that depend on future behaviors. To overcome this, we introduce two complementary mechanisms— a prediction operator that supports speculative future values and a discrete future offset operator that enables precise yet delayed access to future data—and integrate them into RTLola for the first time. We formally define the semantics of both operators and provide a concrete implementation within the RTLola framework. Experimental evaluation demonstrates that the proposed approach significantly enhances RTLola’s expressiveness for prospective temporal properties while maintaining practical efficiency, as evidenced by moderate runtime overhead and memory consumption.
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

future-dependent properties
temporal specifications
asynchronous real-time systems
stream-based specification language
Innovation

Methods, ideas, or system contributions that make the work stand out.

prediction operator
future-offset access
RTLola
stream-based specification
real-time systems
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