Name
Abstract | ||
|---|---|---|
Process mining aims at gaining insights into business processes by analysing event data recorded during process execution. The majority of existing process mining techniques works offline, i.e. using static, historical data stored in event logs. Recently, the notion of online process mining has emerged, whereby techniques are applied on live event streams, as process executions unfold. Analysing event streams allows us to gain instant insights into business processes. However, current techniques assume the input stream to be completely free of noise and other anomalous behaviours. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out spurious events from a live event stream. Our experiments show that we are able to effectively filter out spurious events from the input stream and, as such, enhance online process mining results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-91563-0_3 | ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018 |
Keywords | Field | DocType |
Process mining,Event stream,Filtering,Anomaly detection | Anomaly detection,Data mining,Business process,Computer science,Event stream,Filter (signal processing),Event data,STREAMS,Spurious relationship,Process mining | Conference |
Volume | ISSN | Citations |
10816 | 0302-9743 | 4 |
PageRank | References | Authors |
0.49 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastiaan J. van Zelst | 1 | 53 | 16.60 |
| Mohammadreza Fani Sani | 2 | 9 | 2.98 |
| Alireza Ostovar | 3 | 34 | 3.04 |
| Raffaele Conforti | 4 | 172 | 12.85 |
| marcello la rosa | 5 | 1402 | 81.70 |