1 research outputs found
Generating Reliable Process Event Streams and Time Series Data based on Neural Networks
Domains such as manufacturing and medicine crave for continuous monitoring
and analysis of their processes, especially in combination with time series as
produced by sensors. Time series data can be exploited to, for example, explain
and predict concept drifts during runtime. Generally, a certain data volume is
required in order to produce meaningful analysis results. However, reliable
data sets are often missing, for example, if event streams and times series
data are collected separately, in case of a new process, or if it is too
expensive to obtain a sufficient data volume. Additional challenges arise with
preparing time series data from multiple event sources, variations in data
collection frequency, and concept drift. This paper proposes the GENLOG
approach to generate reliable event and time series data that follows the
distribution of the underlying input data set. GENLOG employs data resampling
and enables the user to select different parts of the log data to orchestrate
the training of a recurrent neural network for stream generation. The generated
data is sampled back to its original sample rate and is embedded into the
originating log data file. Overall, GENLOG can boost small data sets and
consequently the application of online process mining