Name
Abstract | ||
|---|---|---|
Mining workflow models has been a problem of interest for the past few years. Event logs have been the main source of data for the mining process. Previous workflow mining approaches mostly focused on mining control flows that were based on data mining methods, as well as exploited time constraints of events to discover the workflow models. In this work, we present a mining approach which not only takes the behaviourial aspect of workflows into account, but also takes advantage of their informational perspective. Provenance information is a source of reasoning, learning, and analysis since it provides information regarding the service inputs, outputs and quality of service values. Therefore, provenance information along with Bayesian structure-learning methods are exploited for this purpose. Two constraint-based Bayesian structure-learning algorithms are investigated and modified in order to make use of additional provenance information. We will show that this leads to better mining results based on three common mining scenarios. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISDA.2013.6920756 | Intelligent Systems Design and Applications |
Keywords | Field | DocType |
belief networks,data mining,learning (artificial intelligence),quality of service,workflow management software,Bayesian learning,Bayesian structure-learning method,constraint-based Bayesian structure-learning algorithm,data mining method,mining approach,mining workflow models,provenance data,provenance information,quality of service,time constraint,workflow mining,workflow structures | Data mining,Data stream mining,Concept mining,Workflow technology,Bayesian inference,Computer science,Workflow engine,Workflow,Workflow management system,Bayesian probability | Conference |
ISSN | ISBN | Citations |
2164-7143 | 978-1-4799-3515-4 | 1 |
PageRank | References | Authors |
0.34 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahsa Naseri | 1 | 21 | 2.57 |
| Simone A Ludwig | 2 | 1309 | 179.41 |