Online Discovery of Cooperative Structures in Business Processes

Zelst, van Sj Bas; Dongen, van Bf Boudewijn; Aalst, van der Wmp Wil

doi:10.1007/978-3-319-48472-3_12

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…Examples of such applications are the discovery of cooperative structures out of event streams, as tackled in [43], where authors process an event stream and update the set of relationships of a cooperative resource network. In [40], several additional aspects of online process mining are investigated too.…”

Section: Other Applications and Outlookmentioning

confidence: 99%

Streaming Process Mining

Burattin

2022

Lecture Notes in Business Information Processing

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Streaming process mining refers to the set of techniques and tools which have the goal of processing a stream of data (as opposed to a finite event log). The goal of these techniques, similarly to their corresponding counterparts described in the previous chapters, is to extract relevant information concerning the running processes. This chapter presents an overview of the problems related to the processing of streams, as well as a categorization of the existing solutions. Details about control-flow discovery and conformance checking techniques are also presented together with a brief overview of the state of the art.

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Section: Other Applications and Outlookmentioning

confidence: 99%

Streaming Process Mining

Burattin

2022

Lecture Notes in Business Information Processing

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“…IMd, due to its independence from event logs, has also been adapted to the context of process discovery on event streams [24,25]. These techniques assume event data to be dynamic sequences of events and use these sequences to approximate a DF abstraction, after which IMd discovers a process model from the DF abstraction.…”

Section: Related Workmentioning

confidence: 99%

Process discovery using in-database minimum self distance abstractions

Syamsiyah

Leemans

2020

Proceedings of the 35th Annual ACM Symposium on Applied Computing

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Process executions generate event data that are typically stored in legacy information systems, such as databases. However, process discovery, which requires such event data, is performed in main memory. To bridge this gap, existing techniques must transform and extract event data, which can be expensive steps. This issue has been addressed by processing the event data directly in their origin. However, existing methods rely only on the simplest event data abstraction: the Directly Follows (DF) abstraction. This paper improves upon these existing works by considering another abstraction, the Minimum Self Distance (MSD) abstraction, which enables discovery of a larger class of models than the DF alone. That is, we propose IMw, a process discovery technique without logs and uses both the MSD and DF abstractions. Furthermore, this work proposes an approach to compute the MSD abstraction in-database, thus avoiding the need for transforming and moving event data. We evaluate IMw with real-life logs, and the experimental results show that IMw with in-database abstraction is faster than the traditional approach, aware of dynamic updates on event data, and able to discover models with pareto-optimal results, compared to existing techniques.

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“…Online process mining has been applied also to discover cooperative structures out of event streams. For example, in van Zelst et al (2016), authors are able to process an event stream and update the set of relationships of a cooperative resource network.…”

Section: Other Applicationsmentioning

confidence: 99%

Streaming Process Discovery and Conformance Checking

Burattin

2018

Encyclopedia of Big Data Technologies

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SynonymsOnline process mining; online process discovery; online conformance checking. DefinitionsStreaming process discovery, streaming conformance checking, and streaming process mining in general (also known as online process mining) are disciplines which analyze event streams to extract a process model or to assess their conformance with respect to a given reference model. The main characteristic of this family of techniques is to analyze events immediately as they are generated (instead of storing them in a log for late processing). This allows to drastically reduce the latency among phases of the BPM lifecycle (cf. Dumas et al (2013)), thus allowing faster process adaptations and better executions.

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Online Discovery of Cooperative Structures in Business Processes

Cited by 11 publications

References 15 publications

Streaming Process Mining

Streaming Process Mining

Process discovery using in-database minimum self distance abstractions

Streaming Process Discovery and Conformance Checking

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