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   Marian Muste, Dongsu Kim (June 2022, Proceedings of the 39th IAHR World Congress)
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   https://doi.org/10.1145/3290363  

   Alur, Rajeev; Mamouras, Konstantinos; Stanford, Caleb (January 2019, Proceedings of the ACM on Programming Languages)

   In real-time decision making and runtime monitoring applications, declarative languages are commonly used as they facilitate modular high-level specifications with the compiler guaranteeing evaluation over data streams in an efficient and incremental manner. We introduce the model ofData Transducersto allowmodularcompilation of queries over streaming data. A data transducer maintains a finite set of data variables and processes a sequence of tagged data values by updating its variables using an allowed set of operations. The model allows unambiguous nondeterminism, exponentially succinct control, and combining values from parallel threads of computation. The semantics of the model immediately suggests an efficient streaming algorithm for evaluation. The expressiveness of data transducers coincides withstreamable regular transductions, a robust and streamable class of functions characterized by MSO-definable string-to-DAG transformations with no backward edges. We show that the novel features of data transducers, unlike previously studied transducers, make them as succinct as traditional imperative code for processing data streams, but the structuring of the transition function permits modular compilation. In particular, we show that operations such as parallel composition, union, prefix-sum, and quantitative analogs of combinators for unambiguous parsing, can be implemented by natural and succinct constructions on data transducers. To illustrate the benefits of such modularity in compilation, we define a new language for quantitative monitoring, QRE-Past, that integrates features of past-time temporal logic and quantitative regular expressions. While this combination allows a natural specification of a cardiac arrhythmia detection algorithm in QRE-Past, compilation of QRE-Past specifications into efficient monitors comes for free thanks to succinct constructions on data transducers. 

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   https://doi.org/10.1080/00224065.2021.1903821  

   Sergin, Nurettin Dorukhan; Yan, Hao (January 2021, Journal of Quality Technology)

   Variational autoencoders have been recently proposed for the problem of process monitoring. While these works show impressive results over classical methods, the proposed monitoring statistics often ignore the inconsistencies in learned lower-dimensional representations and computational limitations in high-dimensional approximations. In this work, we first manifest these issues and then overcome them with a novel statistic formulation that increases out-of-control detection accuracy without compromising computational efficiency. We demonstrate our results on a simulation study with explicit control over latent variations, and a real-life example of image profiles obtained from a hot steel rolling process. 

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