A delay-resilient and quality-aware mechanism over incomplete contextual data streams

• We propose a method for scheduling a Contextual Information Process (CIP).
• We propose a time-optimized, quality-aware mechanism for the activation of CIP.
• We provide two novel stochastic optimization analytical models.
• Our mechanism compensates between saving computational resources and accuracy.
• We provide comprehensive performance and comparative assessment.

We study the case of scheduling a Contextual Information Process (CIP) over incomplete multivariate contextual data streams coming from sensing devices in Internet of Things (IoT) environments. CIPs like data fusion, concept drift detection, and predictive analytics adopt window-based methods for processing continuous stream queries. CIPs involve the continuous evaluation of functions over contextual attributes (e.g., air pollutants measurements from environmental sensors) possibly incomplete (i.e., containing missing values) thus degrading the quality of the CIP results. We introduce a mechanism, which monitors the quality of the contextual streaming values and then optimally determines the appropriate time to activate a CIP. CIP is optimally delayed in hopes of observing in the near future higher quality of contextual values in terms of validity, freshness and presence. Our time-optimized mechanism activates a CIP when the expected quality is maximized taking also into account the induced cost of delay and an aging framework of freshness over contextual values. We propose two analytical time-based stochastic optimization models and provide extensive sensitivity analysis. We provide a comparative assessment with sliding window-centric models found in the literature and showcase the efficiency of our mechanism on improving the quality of results of a CIP.