The monitoring of timing constraints on time intervals

Mok, Aloysius K.; Lee, Chan-Gun; Woo, Honguk; Konana, Prabhudev

doi:10.1109/real.2002.1181574

Cited by 17 publications

(27 citation statements)

References 11 publications

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“…For example, the Realtime Logic (RTL)-based event model has been proposed with point-and interval-based timing constraints in [11] and [12], respectively. More specifically, the timing constraints in RTL-based event model defines the time point-based temporal relationships among the occurrence time of events.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Event Model for Cyber-Physical Systems

Tan

Vuran

Goddard

2009

2009 29th IEEE International Conference on Distributed Computing Systems Workshops

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Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Event Model for Cyber-Physical Systems

Tan

Vuran

Goddard

2009

2009 29th IEEE International Conference on Distributed Computing Systems Workshops

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“…A solution proposed in [15] is to treat each timestamp as an interval of possible time points at which the event might have occurred. Summapun et al [21] have integrated these time constraints within the existing MaC framework.…”

Section: Related Workmentioning

confidence: 99%

Safe Runtime Verification of Real-Time Properties

Colombo

Pace

Schneider

2009

Lecture Notes in Computer Science

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Abstract. Introducing a monitor on a system typically changes the system's behaviour by slowing the system down and increasing memory consumption. This may possibly result in creating new bugs, or possibly even 'fixing' bugs, only to reappear as the monitor is removed. Properties written in a real-time logic, such as duration calculus, can be particularly sensitive to such changes induced through monitoring. The same problem occurs in other scenarios such as when a system is ported to a faster machine. In this paper, we identify a class of real-time properties, in duration calculus, which are monotonic under the slowing down (speeding up) of the underlying system. We apply this approach to the real-time runtime monitoring tool Larva, where we use duration calculus as a monitoring property specification language, so we automatically identify properties which can be shown to be monotonic with respect to system re-timing.

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“…In our own earlier work [10], [11], we proposed the algorithms for monitoring timing constraints where timestamps of events are time intervals. [10] presents two new modalities, certain and possible for specifying timing constraints on interval timestamps. [11] adopts a probabilistic approach in monitoring timing constraints.…”

Section: Related Workmentioning

confidence: 99%

“…We use the event model proposed in [10] to capture the uncertainties in event occurrence times. In this event model, the timestamp of an event is represented as a time interval, which consists of a pair of time values (min time, max time) where min time is the earliest possible time of occurrence of the event and max time is the latest possible time of occurrence of the event.…”

Section: Interval Timing Correlation For Streaming Datamentioning

confidence: 99%

Online Timing Correlation of Streaming Data with Uncertain Timestamps

Lee

Mok

Konana

2009

IEICE Trans. Inf. & Syst.

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SUMMARYWe introduce the interval timing correlation, which can establish timing correlation conditions to handle interval timing timestamps. Interval timestamps are adopted to handle the temporal uncertainties in the timestamps of stream data. A probabilistic querying approach is taken in order to support timing predicates such as deadline, delay, and within over interval timestamps. A timing correlation condition entails a desired confidence threshold (minimum satisfaction probability). We define the interval timing correlation and discuss how to implement the algorithm. We present an analysis result which can effectively identify only tuples that need to be considered in determining the correlation. The performance of the proposed algorithm is shown.

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The monitoring of timing constraints on time intervals

Cited by 17 publications

References 11 publications

Spatio-Temporal Event Model for Cyber-Physical Systems

Spatio-Temporal Event Model for Cyber-Physical Systems

Safe Runtime Verification of Real-Time Properties

Online Timing Correlation of Streaming Data with Uncertain Timestamps

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