Paper Info
Title
Discovering Concurrent Process Models in Data: A Rough Set Approach
Abstract
The aim of the lecture is to provide a survey of state of the art related to a research direction concerning relationships between rough set theory and concurrency in the context of process mining in data. The main goal of this review is the general presentation of the research in this area. Discovering of concurrent systems models from experimental data tables is very interesting and useful not only with the respect to cognitive aspect but also to possible applications. In particular, in Artificial Intelligence domains such as e.g. speech recognition, blind source separation and Independent Component Analysis, and also in other domains (e.g. in biology, molecular biology, finance, meteorology, etc.).
Year
DOI
Venue
2009
10.1007/978-3-642-10646-0_2
RSFDGrC
Keywords
Field
DocType
research direction,general presentation,experimental data table,main goal,independent component analysis,artificial intelligence,possible application,rough set approach,blind source separation,molecular biology,concurrent systems model,concurrent process models,speech recognition,petri net,data mining,process mining,knowledge discovery,rough set theory,rough set,artificial intelligent
Petri net,Experimental data,Concurrency,Computer science,Process modeling,Rough set,Knowledge extraction,Artificial intelligence,Blind signal separation,Machine learning,Process mining
Conference
Volume
ISSN
Citations 
5908
0302-9743
2
PageRank 
References 
Authors
0.39
20
1
Name
Order
Citations
PageRank
Zbigniew Suraj150159.96