Process Mining over Unordered Event Streams
Process mining is no longer limited to the one-off analysis of static event logs extracted from a single enterprise system. Rather, process mining may strive for immediate insights based on streams of events that are continuously generated by diverse information systems. This requires online algorit...
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2020
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| Online Access: | https://bspace.buid.ac.ae/handle/1234/2926 https://ieeexplore.ieee.org/document/9230157 https://doi.org/10.1109/ICPM49681.2020.00022 |
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| _version_ | 1862980613595725824 |
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| author | Awad, Ahmed |
| author2 | Weidlich, Matthias Sakr, Sherif |
| author2_role | author author |
| author_facet | Awad, Ahmed Weidlich, Matthias Sakr, Sherif |
| author_role | author |
| dc.creator.none.fl_str_mv | Awad, Ahmed Weidlich, Matthias Sakr, Sherif |
| dc.date.none.fl_str_mv | 2020 2025-05-06T08:36:48Z 2025-05-06T08:36:48Z |
| dc.identifier.none.fl_str_mv | Awad, A. et al. (2020) “Process Mining over Unordered Event Streams,” in 2020 2nd International Conference on Process Mining (ICPM), pp. 81–88. https://bspace.buid.ac.ae/handle/1234/2926 https://ieeexplore.ieee.org/document/9230157 https://doi.org/10.1109/ICPM49681.2020.00022 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | 2020 2nd International Conference on Process Mining (ICPM) Padua, Italy 2020 Oct. 5 - 2020 Oct. 8 |
| dc.subject.none.fl_str_mv | Process mining, event streams, unordered streams |
| dc.title.none.fl_str_mv | Process Mining over Unordered Event Streams |
| dc.type.none.fl_str_mv | Article |
| description | Process mining is no longer limited to the one-off analysis of static event logs extracted from a single enterprise system. Rather, process mining may strive for immediate insights based on streams of events that are continuously generated by diverse information systems. This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. While such online algorithms have been proposed for several process mining tasks, from discovery through confor mance checking to time prediction, they all assume that an event stream is ordered, meaning that the order of event generation coincides with their arrival at the analysis engine. Yet, once events are emitted by independent, distributed systems, this assumption may not hold true, which compromises analysis accuracy. In this paper, we provide the first contribution towards handling unordered event streams in process mining. Specifically, we formalize the notion of out-of-order arrival of events, where an online analysis algorithm needs to process events in an order different from their generation. Using directly-follows graphs as a basic model for many process mining tasks, we provide two approaches to handle such unorderedness, either through buffering or speculative processing. Our experiments with synthetic and real-life event data show that these techniques help mitigate the accuracy loss induced by unordered streams. |
| id | budr_64460e79da13e6e5f5519f499964edbe |
| identifier_str_mv | Awad, A. et al. (2020) “Process Mining over Unordered Event Streams,” in 2020 2nd International Conference on Process Mining (ICPM), pp. 81–88. |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2926 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Process Mining over Unordered Event StreamsAwad, AhmedWeidlich, MatthiasSakr, SherifProcess mining, event streams, unordered streamsProcess mining is no longer limited to the one-off analysis of static event logs extracted from a single enterprise system. Rather, process mining may strive for immediate insights based on streams of events that are continuously generated by diverse information systems. This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. While such online algorithms have been proposed for several process mining tasks, from discovery through confor mance checking to time prediction, they all assume that an event stream is ordered, meaning that the order of event generation coincides with their arrival at the analysis engine. Yet, once events are emitted by independent, distributed systems, this assumption may not hold true, which compromises analysis accuracy. In this paper, we provide the first contribution towards handling unordered event streams in process mining. Specifically, we formalize the notion of out-of-order arrival of events, where an online analysis algorithm needs to process events in an order different from their generation. Using directly-follows graphs as a basic model for many process mining tasks, we provide two approaches to handle such unorderedness, either through buffering or speculative processing. Our experiments with synthetic and real-life event data show that these techniques help mitigate the accuracy loss induced by unordered streams.IEEE2025-05-06T08:36:48Z2025-05-06T08:36:48Z2020ArticleAwad, A. et al. (2020) “Process Mining over Unordered Event Streams,” in 2020 2nd International Conference on Process Mining (ICPM), pp. 81–88.https://bspace.buid.ac.ae/handle/1234/2926https://ieeexplore.ieee.org/document/9230157https://doi.org/10.1109/ICPM49681.2020.00022en2020 2nd International Conference on Process Mining (ICPM) Padua, Italy 2020 Oct. 5 - 2020 Oct. 8oai:bspace.buid.ac.ae:1234/29262025-08-13T13:07:19Z |
| spellingShingle | Process Mining over Unordered Event Streams Awad, Ahmed Process mining, event streams, unordered streams |
| title | Process Mining over Unordered Event Streams |
| title_full | Process Mining over Unordered Event Streams |
| title_fullStr | Process Mining over Unordered Event Streams |
| title_full_unstemmed | Process Mining over Unordered Event Streams |
| title_short | Process Mining over Unordered Event Streams |
| title_sort | Process Mining over Unordered Event Streams |
| topic | Process mining, event streams, unordered streams |
| url | https://bspace.buid.ac.ae/handle/1234/2926 https://ieeexplore.ieee.org/document/9230157 https://doi.org/10.1109/ICPM49681.2020.00022 |