首页|期刊导航|重庆邮电大学学报（自然科学版）|A rough-granular computing in discovery of process models from data and domain knowledge
重庆邮电大学学报（自然科学版）2008，Vol.20Issue(3)：341-347,7.
A rough-granular computing in discovery of process models from data and domain knowledge
A rough-granular computing in discovery of process models from data and domain knowledge
摘要
Abstract
The rapid expansion of the Internet has resulted not only in the ever growing amount of data therein stored, but also in the burgeoning complexity of the concepts and phenomena pertaining to those data. This issue has been vividly com- pared by the renowned statistician, prof. Friedman of Stanford University, to the advances in human mobility from the pe- riod of walking afoot to the era of jet travel. These essential changes in data have brought new challenges to the develop- ment of new data mining methods, especially that the treatment of these data increasingly involves complex processes that e- lude classic modeling paradigms. "Hot" datasets like biomedical, financial or net user behavior data are just a few examples. Mining such temporal or stream data is on the agenda of many research centers and companies worldwide. In the data min- ing community, there is a rapidly growing interest in developing methods for process mining, e. g. , for discovery of struc- tures of temporal processes from data. Works on process mining have recently been undertaken by many renowned centers worldwide. This research is also related to functional data analysis , cognitive networks , and dynamical system modeling, e. g. , in biology. In the lecture, we outline an approach to discovery of processes from data and domain knowledge which is based on the rough-granular computing.关键词
granular computing/ process mining/ rough sets/ wistechKey words
granular computing/ process mining/ rough sets/ wistech分类
信息技术与安全科学引用本文复制引用
NGUYEN Hung Son,SKOWRON Andrzej..A rough-granular computing in discovery of process models from data and domain knowledge[J].重庆邮电大学学报（自然科学版）,2008,20(3):341-347,7.基金项目
The research has been supported by the grant N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland and by the grant Innovative Economy Operational Programmme 2007-2013(Priority Axis 1. Research and development of new technologies) managed by Ministry of Reginoal Development of the Republic of Poland. （Priority Axis 1. Research and development of new technologies）
