@INPROCEEDINGS { Rsds4877,
author = {Suraj, Zbigniew}, 
title = {Discovering Concurrent Process Models in Data: A Rough Set Approach}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 5908, 
pages = {12--19}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2009, 
editor = {Sakai, Hiroshi and Chakraborty, Mihir Kumar and Hassanien, Aboul Ella and Slezak, Dominik and Zhu, William}, 
url = {http://www.springerlink.com/content/r06323721k31/?p=7e2c1f1fe7ff42eb9b862fbed1bfd7d8&pi=19}, 
issn = {0302-9743}, 
isbn = {978-3-642-10645-3}, 
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.).}, 
keywords = {knowledge discovery, data mining, process mining, concurrent systems, rough sets, }, 
}