Name
Abstract | ||
|---|---|---|
Process models as knowledge graph representation have been widely used in various domains to create products and deliver services. Although different process model discovery approaches have been proposed in recent years, few of them are designed for distributed computing environments. Specifically, none of them has been studied in the emerging edge computing application scenarios. In this paper, based on the requirements of some real-time process services, we propose a system design for learning process models in IoT edge. We present the details of our solution and our preliminary results on a simulated IoT network show that our method can discover real-time process models in less than a second. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SERVICES.2019.00043 | 2019 IEEE World Congress on Services (SERVICES) |
Keywords | Field | DocType |
process mining,model discovery,edge computing,IoT,service computing | Edge computing,Data mining,Services computing,Knowledge graph,Computer science,Process modeling,Internet of Things,Systems design,Process mining,Distributed computing | Conference |
Volume | ISSN | ISBN |
2642-939X | 2378-3818 | 978-1-7281-3852-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Cheng | 1 | 91 | 16.99 |
| Cong Liu | 2 | 0 | 0.34 |
| Qingzhi Liu | 3 | 1 | 1.70 |
| Duan Yucong | 4 | 39 | 10.98 |
| John Murphy | 5 | 597 | 52.43 |