Dirk Fahland, Fabiana Fournier, et al.
DKE
Domains such as business processes and workflows require working with multi-dimensional ordered objects. There is a need to analyze this data for operational insights. For example, in business processes, users are interested in clustering process traces to discover per-cluster process models that are less complex. Such applications require the ability to measure the similarity between data objects. However, measuring the similarity between sequence-based data is computationally expensive. We present an intuitive and user-controlled approach to summarize sequence-based multi-dimensional data. Our summarization schemes provide a trade-off between the quality and efficiency of analysis tasks. We also derive an error model for summary-based similarity under an edit-distance constraint. Evaluation results over real-world datasets show the effectiveness of our methods.
Dirk Fahland, Fabiana Fournier, et al.
DKE
Michelle Brachman, Christopher Bygrave, et al.
AAAI 2022
siyu huo, Hagen Völzer, et al.
BPM 2021
Neil Thompson, Martin Fleming, et al.
IAAI 2024