Name
Abstract | ||
|---|---|---|
In this article we show how to find evidence of incomplete or fractured processes in non-structured reports of known business processes, by means of rules, patterns and detection of cause-effect relationships. A priori classifications and probabilities of process activities are used as inputs for the analysis and rules detection. In this method we use a domain-specific ontology associated to process activities in order to improve on previous results, where occurrence of a process in a document set was detected by means of SLM |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/MICAI.2010.22 | MICAI (Special Sessions) |
Keywords | Field | DocType |
document set,rules detection,unstructured information,fractured process,domain-specific ontology,cause-effect relationship,process activity,previous result,business process mining,non-structured report,known business process,text mining,statistical analysis,logistics,ontologies,business process,probability,data mining | Artifact-centric business process model,Ontology (information science),Data mining,Ontology,Semantics of Business Vocabulary and Business Rules,Business process,Computer science,Artificial intelligence,Business process discovery,Business rule,Machine learning,Process mining | Conference |
Citations | PageRank | References |
4 | 0.39 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dafne A. Rosso-Pelayo | 1 | 4 | 1.40 |
| Raul A. Trejo-Ramirez | 2 | 4 | 0.39 |
| Miguel Gonzalez-Mendoza | 3 | 4 | 2.08 |
| Neil Hernandez-Gress | 4 | 28 | 8.51 |