Process descriptions are used to create products and deliver services. To lead better processes and services, the first step
is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs.
Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful
but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns
hybrid process models to bridge this gap. Moreover, to cope with today’s big event logs, we propose an efficient method, called f-HMD,
aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach
over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalable
Metadata
Item Type:
Article (Published)
Refereed:
Yes
Uncontrolled Keywords:
Process discovery; hybrid process model; event log; big data; service computing; cloud computing
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
NWO DeLiBiDa research program, European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 799066, Alexander von Humboldt (AvH) Stiftung
ID Code:
24286
Deposited On:
18 Mar 2020 17:53 by
Long Cheng
. Last Modified 27 Apr 2020 14:01