A Framework for Streaming Event-Log Prediction in Business Processes

We present a Python-based framework for event-log prediction in streaming mode, enabling predictions while data is being generated by a business process. The framework allows for easy integration of streaming algorithms, including language models like n-grams and LSTMs, and for combining these predictors using ensemble methods. Using our framework, we conducted experiments on various well-known process-mining data sets and compared classical batch with streaming mode. Though, in batch mode, LSTMs generally achieve the best performance, there is often an n-gram whose accuracy comes very close. Combining basic models in ensemble methods can even outperform LSTMs. The value of basic models with respect to LSTMs becomes even more apparent in streaming mode, where LSTMs generally lack accuracy in the early stages of a prediction run, while basic methods make sensible predictions immediately.

A Composable Glitch-Aware Delay Model

We introduce the Composable Involution Delay Model (CIDM) for fast and accurate digital simulation. It is based on the Involution Delay Model (IDM) [F\"ugger et al., IEEE TCAD 2020], which has been shown to be the only existing candidate for faithful glitch propagation known so far. In its present form, however, it has shortcomings that limit its practical applicability and utility. First, IDM delay predictions are conceptually based on discretizing the analog signal waveforms using specific matching input and output discretization threshold voltages. Unfortunately, they are difficult to determine and typically different for interconnected gates. Second, metastability and high-frequency oscillations in a real circuit could be invisible in the IDM signal predictions. Our CIDM reduces the characterization effort by allowing independent discretization thresholds, improves composability and increases the modeling power by exposing canceled pulse trains at the gate interconnect. We formally show that, despite these improvements, the CIDM still retains the IDM's faithfulness, which is a consequence of the mathematical properties of involution delay functions.