Business Process Mining and Rules Detection for Unstructured Information

Author

Rosso-Pelayo, Dafne A. ; Trejo-Ramírez, Raúl A. ; Gonzalez-Mendoza, Miguel ; Hernandez-Gress, Neil

Author_Institution

Dept. of Comput. Sci., ITESM, Mexico City, Mexico

fYear

2010

fDate

8-13 Nov. 2010

Firstpage

81

Lastpage

85

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.

Keywords

business data processing; data mining; document handling; ontologies (artificial intelligence); pattern classification; probability; statistical analysis; SLM; business process mining; cause effect relationships; document set; domain specific ontology; fractured processes; nonstructured reports; priori classifications; process activities; rule detection; statistical language model; unstructured information; Business Process; Business Process Mining; Data Mining; Statistical Language Model; Text Mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence (MICAI), 2010 Ninth Mexican International Conference on

Conference_Location

Pachuca

Print_ISBN

978-0-7695-4284-3

Type

conf

DOI

10.1109/MICAI.2010.22

Filename

5699164