
handle: 11368/3004548 , 2434/674001
Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously?: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.
business data processing; data mining; pattern clustering, Process Mining; Anomaly Detection; Concept drift; Clustering; Stream Mining, pattern clustering, business data processing, data mining
business data processing; data mining; pattern clustering, Process Mining; Anomaly Detection; Concept drift; Clustering; Stream Mining, pattern clustering, business data processing, data mining
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 13 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
