Many IoT applications from diverse domains rely on real-time, online analytics workflow execution to timely support decision making procedures. The efficient execution of analytics workflows requires the utilization of the processing power available across the cloud to edge continuum. Nonetheless, suggesting the optimal workflow execution over a large network of heterogeneous devices is a challenging task. The increased IoT network size increases the complexity of the optimization problem at hand. The ingested data streams exhibit highly volatile properties. The population of network devices dynamically changes. We introduce DAG*, an A*-alike algorithm that prunes large amounts of the search space explored for suggesting the most efficient workflow execution with formal optimality guarantees. We provide an incremental version of DAG* retaining the optimality property. Our experimentation in real-world scenarios shows that DAG* suggests the optimal workflow execution with 3 to 31 orders of magnitude fewer iterations compared to the entire search space size, outperforming heuristics employed in prior state of the art up to x4.S wrt the goodness of the suggested workflow.

We demonstrate NeuroFlinkCEP, the first framework that integrates neural and symbolic Complex Event Recognition (CER) over a state-of-the-art Big Data platform, also optimizing neurosymbolic CER upon operating over IoT settings. NeuroFlinkCEP receives expressed patterns as extended regular expressions and automatically transforms them to FlinkCEP jobs per device. To enable detection of simple events involved in CER patterns, NeuroFlinkCEP can integrate any neural model in FlinkCEP jobs. To optimally assign operator execution in-network, we incorporate and extend a state-of-the-art IoT optimizer.