The rapid growth of information and communication technology captured common man and various organizations and influenced each individual’s life, work, and study. It leads to a data explosion. It has no utility without any analysis and leads to many analytical techniques. The prime objective of these techniques is to derive some useful knowledge. However, the transformation of data into knowledge is not easy because of many reasons, such as disorganized, incomplete, uncertainties, etc. Furthermore, analyzing uncertainties present in data is not a straight forward task. Many different models, like fuzzy sets, rough sets, soft sets, neural networks, generalizations, and hybrid models obtained by combining two or more of these models, have been fruitful in representing knowledge. To this end, this paper identifies the conventionally used rough computing techniques and discusses their concepts, developments, abstraction, hybridization, and scope of applications.

信息和通信技术的迅速发展吸引了普通人和各种组织，并影响了每个人的生活，工作和学习。这导致数据爆炸。如果不进行任何分析，它就毫无用处，并且会导致许多分析技术。这些技术的主要目的是获得一些有用的知识。但是，由于多种原因，例如混乱，不完整，不确定性等，将数据转换为知识并不容易。此外，分析数据中存在的不确定性也不是直接的任务。通过组合两个或多个这些模型而获得的许多不同模型，例如模糊集，粗糙集，软集，神经网络，归纳和混合模型，在表示知识方面均卓有成效。为此，